# EVIDENCE FOR AN INTERACTION BETWEEN THE GALACTIC CENTER CLOUDS: M0.10$-$0.08
AND M0.11$-$0.11

Natalie O. Butterfield Department of Physics, Villanova University, 800 E.
Lancaster Ave., Villanova, PA 19085, USA  National Radio Astronomy
Observatory, 520 Edgemont Road, Charlottesville, VA 22903, USA
<EMAIL_ADDRESS>Cornelia C. Lang  Department of Physics and Astronomy,
University of Iowa, 30 North Dubuque Street, Iowa City, IA 52242, USA  Adam
Ginsburg Department of Astronomy, University of Florida, PO Box 112055, USA
Mark R. Morris Department of Physics & Astronomy, University of California,
430 Portola Plaza, Los Angeles, CA 90095, USA  Jürgen Ott National Radio
Astronomy Observatory, 1003 Lopezville Road, Socorro, NM 87801, USA  Dominic
A. Ludovici Department of Physics and Engineering Science, Coastal Carolina
University, 100 Chanticleer Dr E, Conway, SC 29528, USA

###### Abstract

We present high-resolution ($\sim$2$-$3″; $\sim$0.1 pc) radio observations of
the Galactic center cloud M0.10$-$0.08 using the Very Large Array at K and Ka
band ($\sim$25 and 36 GHz). The M0.10$-$0.08 cloud is located in a complex
environment near the Galactic center Radio Arc and the adjacent M0.11$-$0.11
molecular cloud. From our data, M0.10$-$0.08 appears to be a compact molecular
cloud ($\sim$3 pc) that contains multiple compact molecular cores (5+; $<$0.4
pc). In this study we detect a total of 15 molecular transitions in
M0.10$-$0.08 from the following molecules: NH3, HC3N, CH3OH, HC5N, CH3CN, and
OCS. We have identified more than sixty 36 GHz CH3OH masers in M0.10$-$0.08
with brightness temperatures above 400 K and 31 maser candidates with
temperatures between 100$-$400 K. We conduct a kinematic analysis of the gas
using NH3 and detect multiple velocity components towards this region of the
Galactic center. The bulk of the gas in this region has a velocity of 51.5 km
s-1 (M0.10$-$0.08) with a lower velocity wing at 37.6 km s-1. We also detect a
relatively faint velocity component at 10.6 km s-1 that we attribute to being
an extension of the M0.11$-$0.11 cloud. Analysis of the gas kinematics,
combined with past X-ray fluorescence observations, suggests M0.10$-$0.08 and
M0.11$-$0.11 are located in the same vicinity of the Galactic center and could
be physically interacting.

Galaxy: center, ISM: kinematics and dynamics

††software: CASA (International Consortium Of Scientists, 2011); Clumpfind
(Williams et al., 2011); $pyspeckit$ (Ginsburg & Mirocha, 2011; Ginsburg et
al., 2022)

##  1 Introduction

The central 200 pc of the galaxy (Central Molecular Zone; CMZ) is an extreme
Galactic environment. Molecular clouds in the CMZ have hotter average gas
temperatures (50$-$300 K; Mauersberger et al., 1986; Mills & Morris, 2013;
Krieger et al., 2017; Ginsburg et al., 2016), higher densities (103-5 cm-3;
Zylka et al., 1992; Mills et al., 2018a), and broader line widths, on the
$\sim$10 pc scale ($\sim$20$-$30 km s-1; Bally et al., 1987; Kauffmann et al.,
2017), than typical clouds in the interstellar medium (ISM) of the Galactic
disk. The velocities of CMZ clouds range from $-$250 to +250 km s-1 within the
inner 1$\fdg$5 of our Galactic center. The large velocity range of these
clouds, wide velocity dispersions, and line-of-sight confusion from multiple
velocity components can make it difficult to place individual molecular clouds
within the 3-dimensional context of the CMZ. Figure 1 shows the inner 100 pc
of the Galactic center, where many of these dense molecular clouds are shown
in red in this 3-color image. Recent efforts have been made to connect these
individual clouds (1$-$10 pc) to the larger structures ($\sim$100 pc) in the
Galactic center (Sofue, 1995; Sawada et al., 2004; Molinari et al., 2011;
Kruijssen et al., 2015; Henshaw et al., 2016).

Figure 1:  Three-color composite of the inner 100 pc of the CMZ, centered on
the Radio Arc region, where red and green are the 160 and 70 $\mu$m emission,
respectively, from HiGAL (Molinari et al., 2010), and blue is 8 $\mu$m
emission from Spitzer (Churchwell et al., 2009). The solid white circle shows
the region of the CMZ targeted in this study. This field is centered on the
M0.10$-$0.08 molecular cloud, but could overlap with some of the extended
emission in M0.11$-$0.11. The dashed white circle shows the location of the
M0.20$-$0.033 expanding shell presented in Butterfield et al. (2018).
Additional prominent CMZ regions are labeled for reference purposes. Overlaid
on this figure is a dashed line showing the extent of the orbital stream
proposed by Kruijssen et al. (2015).

The 3-dimensional orientation of the large-scale structures in the CMZ can
depend greatly on the interpretation of the gas kinematics. For example, Sofue
(1995) and Sawada et al. (2004) suggest a two spiral arm structure, whereas
Molinari et al. (2011) argue for a twisted elliptical ring. The most recent
orbital model, presented in Kruijssen et al. (2015), suggests an open orbit
solution (see dashed line in Figure 1 for the projected trajectory of their
orbital model solution). In the Kruijssen et al. (2015) orbital model, gas in
the CMZ traces an open orbit set by the shape of the CMZ potential. Connected
chains of molecular clouds all follow the same orbital path or ‘stream’. The
3-dimensional arrangement of clouds along a continuous stream can be loosely
reconstructed from their projected radial distance to Sgr A∗ and the observed
line-of-sight velocity. However, there is still some ambiguity about whether
certain features are located on the near or far sides of the Galactic center.
Additionally, multiple components along the same line-of-sight can make it
challenging to disentangle the kinematics of a single cloud. High spatial and
spectral resolution observations targeting regions where the kinematics are
complex are needed to resolve the individual components.

Table 1: Spectral Line Imaging Parameters for the 15 molecular transitions detected in this study |  | —Restoring Beam111 Where 1″ is 0.04 pc at an assumed distance of 7.9 kpc to the CMZ (Do et al., 2019).— |  |  |
---|---|---|---|---|---
Species & | Rest |  Major |  Minor |  Position | Velocity |  rms | Peak
Transition |  Frequency | Axis | Axis | Angle |  Resolution | per Channel | Intensity
| (GHz) | (″) | (″) | (°) | (km s-1) |  (mJy beam-1) | (mJy beam-1)
NH3 (1,1) | 23.69450 | 2.81 | 2.62 | -81.85 | 1.58 | 1.3 | 57.6
NH3 (2,2) | 23.72263 | 2.79 | 2.62 | -85.45 | 1.58 | 1.4 | 56.3
NH3 (3,3) | 23.87013 | 2.77 | 2.60 | -85.18 | 3.14 | 2.3 | 485.9
HC5N (9$-$8)222 These four transitions were smoothed from the natural spatial resolution to improve the signal-to-noise.333 These four transitions had a signal-to-noise ratio $<$9 and are therefore not shown in Figure 2. | 23.96390 | 3.00 | 3.00 | 0.00 | 3.13 | 0.8 | 6.4
NH3 (4,4) | 24.13942 | 2.79 | 2.59 | 89.49 | 3.10 | 1.0 | 42.0
OCS (2$-$1)23 | 24.32593 | 5.00 | 5.00 | 0.00 | 3.08 | 1.2 | 8.6
NH3 (5,5) | 24.53299 | 2.74 | 2.55 | -90.00 | 3.05 | 0.8 | 28.3
CH3OH (6${}_{2}-$61)23 | 25.01812 | 3.00 | 3.00 | 0.00 | 3.00 | 0.8 | 5.6
NH3 (6,6) | 25.05603 | 2.63 | 2.48 | 89.48 | 2.99 | 0.9 | 29.4
NH3 (7,7) | 25.71518 | 2.58 | 2.40 | -83.33 | 2.91 | 0.7 | 11.8
HC3N (3$-$2) | 27.29429 | 3.14 | 2.52 | 16.76 | 2.75 | 2.2 | 55.2
NH3 (9,9)3 | 27.47794 | 2.80 | 2.45 | 43.30 | 2.73 | 2.0 | 16.8
CH3OH ($4_{\textrm{-}1}$$-$$3_{0}$) | 36.16927 | 1.95 | 1.81 | -174.63 | 1.05 | 2.9 (50)444The larger value in parentheses is the rms noise in the channel containing the brightest maser, at v = 50.6 km s-1. | 46969.0
HC3N (4$-$3) | 36.39232 | 1.98 | 1.81 | -3.02 | 2.06 | 2.8 | 47.0
CH3CN (2$-$1)2 | 36.79547 | 3.00 | 3.00 | 0.00 | 1.02 | 4.3 | 40.7

One region of the CMZ where the kinematics are complex is toward the
M0.10$-$0.08 molecular cloud (solid white circle in Figure 1). The
M0.10$-$0.08 cloud, and the adjacent M0.11$-$0.11 cloud (annotated in Figure
1), have been observed in several large-scale surveys of molecular gas in the
CMZ for many decades (Güsten et al., 1981; Tsuboi et al., 1997; Chuss et al.,
2003; Handa et al., 2006; Jones et al., 2012; Mills & Battersby, 2017;
Battersby et al., 2020; Hatchfield et al., 2020; Guan et al., 2021). Several
of the low-spatial-resolution surveys of cold dust and molecular gas show that
the M0.10$-$0.08 cloud is relatively bright and compact ($<$3 pc), with a mass
of 1.7 $\times$ 105 M⊙ (e.g., Tsuboi et al., 2011). The M0.10$-$0.08 cloud
also been observed to have substructure, as detected in the recent 1mm CMZoom
survey (Battersby et al., 2020; Hatchfield et al., 2020). M0.11$-$0.11,
however, is relatively faint and extended ($>$5 pc) and could spatially
overlap with M0.10$-$0.08 (Figure 1). The spatial overlap between the two
clouds has led some investigators to argue for a possible connection between
the two clouds (Handa et al., 2006; Clavel et al., 2013). However, there are
unsolved questions about this connection in the literature due to the large
velocity separation between the two clouds along this line-of-sight
($\Delta$v$\sim$30 km s-1; Ponti et al., 2010; Kruijssen et al., 2015).
Understanding the connection or separation of the two clouds can give insight
on the complex kinematics in the region. Furthermore, disentangling the
complex kinematics into a somewhat simple solution is essential for
understanding the 3-dimensional structure of the gas and the effects that
cloud-cloud interactions can have on the gas motions.

We present high-resolution ($\sim$2$-$3″) radio observations of M0.10$-$0.08
using the National Science Foundation’s Karl G. Jansky Very Large Array
(hereafter, VLA). Using these observations we analyze the morphological and
kinematic structure of M0.10$-$0.08 at high-resolution (Section 3) and discuss
the relationship of M0.10$-$0.08 to other clouds in the region (Section 4).

##  2 Observations and Data Calibration

The observations presented in this paper were taken with the VLA
interferometric radio telescope, operated by the National Radio Astronomy
Observatory.555The National Radio Astronomy Observatory is a facility of the
National Science Foundation operated under cooperative agreement by Associated
Universities, Inc. These VLA observations were part of a larger survey of
molecular clouds in the CMZ (PI: Elisabeth A.C. Mills; Project code:
11B-210).666Results from this survey have also been presented in: Mills &
Morris (2013); Mills et al. (2014, 2015); Ludovici et al. (2016); Butterfield
et al. (2018); Mills et al. (2018b). This survey used the K (18.0$-$26.5 GHz)
and Ka (26.5$-$40.0 GHz) band receivers on 2012 January 14th & 13th,
respectively, with the DnC hybrid array. In this survey we observed 15
spectral lines from several regions in the CMZ. The image cube parameters for
all 15 lines are reported in Table 1. The results presented in this paper
focus on a single pointing containing M0.10$-$0.08,777All J$<$7 NH3 images,
shown in Figure 2, are from a larger multi-pointing mosaic (See Figure 3,
left, in Butterfield et al., 2018). centered at
$\alpha$(2000)=17h46m09$\fs$79, $\delta$(2000)=$-28\arcdeg 53\arcmin 18\farcs
0$, for K band, and $\alpha$(2000)=17h46m11$\fs$37, $\delta$(2000)=$-28\arcdeg
53\arcmin 24\farcs 3$, for Ka band, with a time-on-source of $\sim$25 minutes
in each frequency band.

Figure 2: Peak intensity distribution of 11 of the 15 molecular line
transitions detected in this paper. The top two rows show the NH3 (1,1) $-$
(7,7) and CH3OH line emission. The bottom row shows the observed HC3N and
CH3CN transitions. The bottom right-most panel shows the 20, 40, 80, and 140
$\sigma$ contour levels of the NH3 (3,3) emission, with annotations
identifying several of the ‘Features’ discussed in Section 3.1.1. The spatial
resolution of each presented molecular transition is shown in the bottom left
corner of every panel. The imaging parameters of all 15 detected molecular
transitions are described in Table 1. The black dashed line shows the
orientation of the Galactic plane at $b=-0\fdg 075$.

The correlator setup for this survey is described in Mills et al. (2015) and
Butterfield et al. (2018). High-frequency VLA procedures888Hyperlink to the
high frequency CASA tutorial. All imaging and calibration of the VLA
observations presented here used the Common Astronomy Software Application
(CASA) program provided by NRAO (McMullin et al., 2007). were used for
calibration and imaging, as described in Mills et al. (2015), with one
difference. We employed the CLEAN parameter “multiscale” for all spectral
lines that had a signal-to-noise ratio $>$15 and a peak intensity $>$20 mJy
beam-1 (see Table 1) in order to improve our sensitivity to data taken with
short baselines in our interferometric observations.

##  3 Results

###  3.1 Morphology of the Molecular Emission in M0.10$-$0.08

Figure 2 presents the peak intensity emission of 11 molecular transitions
detected in M0.10$-$0.08 (see Table 1 for imaging parameters). The remaining
four detected molecular transitions in M0.10$-$0.08 are relatively faint
($<$9$\sigma$) and are therefore $not$ shown in Figure 2. In the following
sections we examine the bright, diffuse molecular emission (§3.1.1: NH3 &
HC3N). We focus on the kinematics of the NH3 emission and fit the averaged gas
profile in Section 3.2. The CH3OH ($4_{\textrm{-}1}$$-$$3_{0}$) class I maser
transition is discussed in detail in Section 3.3.

####  3.1.1 Morphology of the Diffuse Molecular Emission: NH3 and HC3N

The top two rows in Figure 2 show the detected NH3 (1,1)$-$(7,7) emission in
M0.10$-$0.08. The distribution of the metastable NH3 emission is similar
across all seven transitions. The speckled morphology observed in the NH3
(7,7) transition is likely an artifact of cleaning with delta functions (see
Section 2 and Table 1 for a discussion on the cleaning process).

Most of the NH3 emission in the M0.10$-$0.08 cloud is concentrated within a
square arcminute region near the center of the field. At high-resolution (3″)
the M0.10$-$0.08 cloud has a wedge-like appearance that is narrow at lower
Galactic longitude (10″; $l$=0$\fdg$095) and widens with increasing Galactic
longitude (50″; $l$=0$\fdg$11). This wedge-like structure is also noticeable
in both transitions of HC3N: 3$-$2 and 4$-$3 (bottom row in Figure 2).
Additionally, there is a diffuse ‘filamentary extension’ toward the southern
region of M0.10$-$0.08, as indicated in the bottom right-most panel of Figure
2 (i.e., ‘Features’ panel). This filamentary extension is detected in both the
NH3 and HC3N transitions, but not in the CH3OH (4$-$3) transition. The longest
extent of M0.10$-$0.08 is 75″ ($\sim$3 pc), indicating that this cloud is
among the more compact of molecular clouds observed in the Galactic center
(diameters of 3$-$10 pc; e.g., Güsten et al., 1981; Bally et al., 1987;
Kauffmann et al., 2017; Mills & Battersby, 2017).

Figure 3:  Intensity-weighted velocity distribution (1st moment map) of the
NH3 (3,3) transition for emission above the 10$\sigma$ level, integrated over
a velocity range of -20$-$100 km s-1. The black contours correspond to
emission at 10, 20, 40, 80, & 140 $\times$ 2.3 mJy beam-1 (rms level). The
black dashed lines show the orientation of the Galactic plane at $b=-0\fdg
075$ and $b=-0\fdg 09$.

Within M0.10$-$0.08 there are several ($\sim$5) compact clumps (D$<$10″) of
brighter NH3 emission ($>$0.2 Jy beam-1) that are most prominent in the (3,3)
transition. Most of these compact clumps are concentrated toward the northeast
region of the cloud, with the brightest NH3 clump located at:
$\alpha$(2000)=17h46m12$\fs$3, $\delta$(2000)= $-$28°53′18″. The brightest
clump (i.e., ‘Main Clump’; see ‘Features’ panel in Figure 2) contains emission
in all 11 transitions shown in Figure 2, including the fainter CH3CN (2$-$1)
transition. Further, the Main Clump is the only location where we detect CH3CN
emission. Directly south of the Main Clump is a lower intensity emission
region (‘Depression;’ labeled in the Features panel of Figure 2). This
depression region is $\sim$10″ across and is located at
$\alpha$(2000)=17h46m12$\fs$5, $\delta$(2000)=$-$28°53′25″. The Depression is
detected in both NH3 and HC3N, but is most prominent in the HC3N (4$-$3)
transition.999There is a second lower level emission region to the west of the
Main Clump and north of the Filamentary Extension (see Figure 2). However,
because we detect emission above the noise level in this region in the HC3N
(4$-$3) transition, we do not characterize this feature as a second
‘depression’. Although this feature is detected in all of our extended
emission lines (NH3, HC3N), it could be produced by spatial filtering in our
interferometer data. Future observations at different wavelengths are
necessary to determine whether the Depression is some kind of cavity.

Figure 4: NH3 (5,5) velocity spectrum averaged over the entire field of view.
The NH3 (5,5) line was chosen as a representative spectrum to show the
multiple components toward this region. In the (5,5) line all three components
are detected and at this higher J transition, the hyperfine lines are
suppressed. The black line shows the data. The blue Gaussians show the
individual components (presented in Table 2), with the red line showing the
sum of the three Gaussian components. The solid green line at -0.05 K shows
the residuals of the three-Gaussian-component fit. The dashed green line at
-0.05 K shows the residuals of a two-Gaussian-component fit (vc $\simeq$ 10 km
s-1 and 50 km s-1). In the two Gaussian fit there is consistent excess of
emission around $\sim$20$-$40 km s-1 (six spectral cube channels; dashed green
line). This excess emission around 20$-$40 km s-1 is brighter than the
emission in the 10.6 km s-1 component and is detected in the HC3N transitions
as well. Therefore, we interpret the excess emission as an intermediate-
velocity component.

###  3.2 Kinematics of the NH3 Emission

Figure 3 shows the centroid velocity distribution (moment 1) of the NH3 (3,3)
transition. Most of the bright NH3 emission ($>$10$\sigma$) is at a velocity
of 35$-$65 km s-1. However, as we will show in the following section, faint
($<$10$\sigma$) molecular emission is detected at lower velocities of $\sim$10
km s-1.

We note an asymmetry in the velocity distribution that results in roughly a 10
km s-1 pc-1 gradient (where 1 pc is $\sim$25″). Most of the higher-velocity
NH3 (3,3) emission ($v$ $\geq$ 55 km s-1) is located toward the north-western
side of M0.10$-$0.08 (around b=$-$0$\fdg$075) and the lower-velocity emission
($v$ $\leq$ 45 km s-1) is generally located toward the south and south-eastern
sides of M0.10$-$0.08 (around b=$-$0$\fdg$09). The orientation of the
described velocity gradient is perpendicular to the direction of orbital
motion in the Kruijssen et al. (2015) orbital model. The filamentary
extension, described in Section 3.1, contains mainly lower velocity emission
(35$-$45 km s-1) and is oriented roughly parallel to the described velocity
gradient.

Table 2: Kinematics of the NH3 (5,5) transition Parameter101010$v_{c}$ is the central velocity of the component, $\sigma$ is the velocity dispersion and $T_{B}$ is the peak brightness temperature. |  | Value
---|---|---
Low Velocity Component
$v_{c}$ |  | 10.6 $\pm$ 2.5 km s-1
$\sigma$ |  | 5.8 $\pm$ 2.6 km s-1
$T_{B}$ |  | 0.01 $\pm$ 0.003 K
Intermediate Velocity Component
$v_{c}$ |  | 37.6 $\pm$ 5.7 km s-1
$\sigma$ |  | 8.0 $\pm$ 3.0 km s-1
$T_{B}$ |  | 0.05 $\pm$ 0.02 K
High Velocity Component
$v_{c}$ |  | 51.5 $\pm$ 0.6 km s-1
$\sigma$ |  | 6.9 $\pm$ 0.3 km s-1
$T_{B}$ |  | 0.34 $\pm$ 0.03 K

####  3.2.1 Multiple velocity components toward M0.10$-$0.08

Moment 1 maps, like the one presented in Figure 3, have the advantage of
showing the predominant velocity distribution across a cloud or region.
However, these maps can be misleading since they can average over multiple
components and be weighted by the brighter emission components. Integrating
the emission across a cloud or region, and analyzing the spectra using fitting
programs like pyspeckit (Ginsburg & Mirocha, 2011; Ginsburg et al.,
2022)111111The $pyspeckit$ python program is available online at
https://github.com/pyspeckit/pyspeckit.  can help identify and distinguish
multiple components. Once these velocity components are disentangled we can
map their spatial distribution and morphology by isolating channels associated
with the individual velocity component. By analyzing the gas kinematics using
numerous methods we can understand the relative structure of the two clouds
towards this complex region. In this section we will identify the velocity
components towards the M0.10$-$0.08 cloud by analyzing the NH3 (5,5) line.

Figure 5: Molecular morphology of the three velocity components presented in
Table 2 (integrated intensity, moment 0, in NH3 (3,3)). These panels were made
using Vc $\pm$ $\sigma_{v}$ (to the closest channel). The red contour in all
three panels shows the 20$\sigma$ level from the ‘Features’ panel in Figure 2
for spatial reference. Annotated on the 10.6 and the 51.5 km s-1 panels are
the M0.11$-$0.11 and M0.10$-$0.08 clouds, respectively The black box shows the
region used for the position-velocity slice in Figure 7.

Figure 4 shows the raw integrated spectrum (black histogram) of the NH3 (5,5)
line. We chose to analyze the J=5 NH3 transition because the hyperfine
satellite lines are quite weak and do not contribute significantly to the
spectrum. We initially fit the NH3 (5,5) line with two main Gaussian
components at $\sim$10$-$15 km s-1 and $\sim$50$-$55 km s-1. The residuals
from this initial fit showed an excess around 20$-$40 km s-1 (dashed green
line; Figure 4). This excess emission, which appears as a lower-velocity wing
to the brighter $\sim$50$-$55 km s-1 velocity component, is detected in nearly
all of our observed lines (e.g., NH3, HC3N). Since this excess is detected in
multiple molecules and transitions, we interpret it to be an intermediate
velocity component. Including a third component in our fitting process greatly
reduced the residuals to produce the solid green residual in Figure 4. The
final fit parameters used to produce the three Gaussian components in Figure 4
are listed in Table 2.

Figure 6: Distribution of the 36.2 GHz CH3OH ($4_{\textrm{-}1}$$-$$3_{0}$)
masers in M0.10$-$0.08 showing the maximum intensity emission (left), from
Figure 2, and central velocities (right) for emission above the 12$\sigma$
level. The overlaid contours show 12, 30, 100, and 200 $\times$ 50 mJy b-1
(rms noise in brightest channel). The 15 brightest masers from Table
LABEL:MaserTable are marked on the left panel.

The lowest velocity component, which has a central velocity of 10.6 km s-1, is
the faintest of the three components. This velocity component is detected in
both the HC3N transitions and in the NH3 lower J-transitions (J$<$7). The
highest velocity component, fit with a central velocity of 51.5 km s-1, is the
brightest of the three components and is detected in all of our observed
molecular lines. This velocity component appears to dominate the moment 1 map,
shown in Figure 3. The intermediate velocity component, which is best fit with
a central velocity of 37.6 km s-1 in the NH3 (5,5) line, is shown to be
slightly spatially offset from the high velocity component in Figure 3. We
note that, while present, the central velocity of the intermediate velocity
component did vary between the different molecular transitions, ranging from
$\sim$30$-$45 km s-1. Therefore, the error estimates on the central velocity
of the intermediate component are much larger than those shown in the Low and
High velocity components to reflect this uncertainty.

We can further analyze the morphology of the molecular emission by isolating
the channels associated with each component. Figure 5 shows the distribution
of the NH3 (3,3) emission in each velocity component, labeled by their
respective central velocities from Table 2. We are using the NH3 (3,3) line
for this analysis due to the faintness of the low-velocity component in the
NH3 (5,5) transition. When integrating over the field we were sensitive enough
to detect the 10.6 km s-1 component, but for a spatial mapping the NH3 (5,5)
line is not bright enough to perform a pixel by pixel analysis of that
component. We are aware that the hyperfine satellite lines of the NH3 (3,3)
emission will be more prominent than in the (5,5) transition and will
acknowledge where those lines may contribute in the following discussion.

In general, the observed gas morphology is unique for each velocity component.
The 51.5 km s-1 velocity gas is concentrated toward the center of the field
and closely follows the bright NH3 emission in Figure 2, with the exception of
the filamentary extension (e.g., see the wedge-shaped distribution in the red
contour, Figure 5). The 10.6 km s-1 component is distributed throughout the
field-of-view and contains several elongated structures (e.g., black box in
Figure 5). Further, this component does not appear to have similar morphology
with the 51.5 km s-1 component, suggesting this gas could be independent from
the 51.5 km s-1 emission. The morphology of the 37.6 km s-1 component has
similar attributes to both the 10.6 and 51.5 km s-1 components. Unlike the
51.5 km s-1 component, the 37.6 km s-1 component $is$ associated with the
filamentary extension. Further, the filamentary extension closely follows the
elongated structure in the 10.6 km s-1 component (Figure 5). The 37.6 km s-1
component also contains concentrated emission toward the north which spatially
overlaps with emission in the 51.5 km s-1 component. Because of this spatial
overlap, some of the 37.6 km s-1 emission could be from the hyperfine lines in
the 51.5 km s-1 component.

###  3.3 36.2 GHz CH3OH Masers in M0.10$-$0.08

Our Ka-band observations included the 36.2 GHz CH3OH
($4_{\textrm{-}1}$$-$$3_{0}$) maser transition. This class I maser is known to
trace shocks, as it is collisionally excited (Morimoto et al., 1985; Menten,
1991; Sjouwerman et al., 2010). The 36.2 GHz CH3OH
($4_{\textrm{-}1}$$-$$3_{0}$) maser transition has previously been detected
towards this region (Yusef-Zadeh et al., 2013; Cotton & Yusef-Zadeh, 2016).
Our data suggest there are at least one hundred compact CH3OH sources in
M0.10$-$0.08 (Figure 2, second row, last panel). The compact CH3OH sources in
M0.10$-$0.08 are located within a square arcminute region, and closely follow
the bulk of the NH3 and HC3N emission at velocities from 40$-$60 km s-1
(Figure 5, bottom panel).

Figure 6 shows the spatial distribution (left) and the velocity distribution
(right) of the bright, above 0.6 Jy beam-1 (12$\sigma$), 36.2 GHz CH3OH
emission. The CH3OH emission is not uniformly distributed throughout
M0.10$-$0.08. Most of the CH3OH emission appears to be distributed throughout
the wedge-like structure (discussed in Section 3.1.1). We do not detect any
compact emission, above 12$\sigma$, from the filamentary extension (e.g., see
Figures 2 and 6).

The velocity of the CH3OH emission in M0.10$-$0.08 ranges from $\sim$35 to 65
km s-1. This corresponds to the velocity range of the bright NH3 emission
(Figure 3). This velocity range indicates that most of the CH3OH maser
emission is associated with the 37.6 and 51.5 km s-1 velocity components.

In order to characterize the nature of the point-like emission and to evaluate
whether these detections represent maser emission, we used the source
detection algorithm, Clumpfind (Williams et al., 1994), to distinguish the
emission both spectrally and spatially. Clumpfind identifies local maxima and
uses saddle-points in position and velocity space around the local maxima to
determine the boundaries of the sources. Clumpfind then produces a list of
clumps with uniform criteria, which was used to construct a catalog (for more
details on maser identification using the Clumpfind algorithm, see the
description of this technique in Mills et al., 2015, Section 5.1). Sixty-four
of the compact CH3OH sources have brightness temperatures over 400 K (i.e.,
‘CH3OH masers’). The properties of the 64 detected ‘CH3OH masers’ identified
with Clumpfind are listed in Table LABEL:MaserTable. The spectral profiles of
these masers are shown in Figure A.1. The 15 brightest masers in M0.10$-$0.08
are labeled in Figure 6 (left).

With Clumpfind we also detect 31 compact CH3OH sources that have a brightness
temperature between 100$-$400 K, which we regard as ‘maser candidates.’ These
sources are considered to be candidate masers based on their brightness
temperature, which are similar to observed gas temperatures in CMZ clouds (50
- 400 K, Mills & Morris, 2013; Krieger et al., 2017) Therefore, we assume that
any emission above this upper 400 K limit is likely non-thermal (i.e., maser
emission, sources in Table LABEL:MaserTable) and any emission that is below
100 K is most likely thermal. Therefore, we classify CH3OH point-sources that
have brightness temperatures between 100-400 K as ‘maser candidates’. The
properties of all 31 maser candidates are listed in Table LABEL:CanTable, with
their spectra shown in Figure A.2. These maser candidates are also located
within the same square arcminute region as the detected CH3OH masers and have
a similar velocity range (41$-$63 km s-1).

##  4 Discussion

In the following section we present a discussion and interpretation of our
kinematics results on the M0.10$-$0.08 cloud (Section 3). Here, we attempt to
explain the complicated and multiple-component velocity structure detected in
the vicinity of M0.10$-$0.08 (Sections 4.1 & 4.2).

###  4.1 Locations and Origins of M0.10$-$0.08 and M0.11$-$0.11

The bulk emission in this region of the Galactic center has a velocity around
51.5 km s-1 (Section 3.2.1). The morphology and gas kinematics of this
velocity component are consistent with those found in previous studies of
M0.10$-$0.08 (e.g., Tsuboi et al., 2011). M0.10$-$0.08 appears to be part of a
larger structure of molecular gas that has a velocity of around 50 km s-1
(Fukui et al., 1977; Tsuboi et al., 2011). Tsuboi et al. (2011) detected
H13CO+ emission around 50 km s-1 extending from $+$0.15∘ to $-$0.05∘
($d$$\sim$27 pc; see their Figure 10). Within this extended diffuse structure
they detect three concentrated regions of H13CO+ emission that coincide with
the M0.10$-$0.08, M0.07$-$0.07, and 50 km s-1 cloud (M$-$0.02$-$0.07)
molecular clouds (see Figure 1 for locations of these clouds). The presence of
all three clouds within this larger diffuse structure could be evidence that
all three clouds are co-located within a single lower-density envelope that
has a velocity of around 50 km s-1.

This large diffuse gas structure, observed in H13CO+ by Tsuboi et al. (2011),
may be Orbital Stream 1 in the Kruijssen et al. (2015) orbital model. The 50
km s-1 cloud is argued to be associated with Orbital Stream 1 (Kruijssen et
al., 2015). Therefore, if M0.10$-$0.08 and M$-$0.02$-$0.07 are associated
within the same gas stream, and 50 km s-1 cloud is located on Orbital Stream
1, then by extension we can infer that M0.10$-$0.08 is also located on Orbital
Stream 1. We note, however, that in the Kruijssen et al. (2015) orbital stream
model, gas at the closest angular location to the M0.10$-$0.08 cloud
($l=$0$\fdg$09, $b=-$0$\fdg$07; as the position of M0.10$-$0.08 is slightly
offset from Orbital Stream 1 by $\sim$1′) is predicted to have a line-of-sight
velocity of 60 to 65 km s-1. Although this suggested line-of-sight velocity is
slightly higher then the central velocity of M0.10$-$0.08 that we measured in
Figure 4, we do detect some gas at velocities of around 60 to 65 km s-1 (see
Figures 3 & 6, right).

The M0.20$-$0.033 expanding shell is also hypothesized to be located on
Orbital Stream 1 (Butterfield et al., 2018, see our Figure 1 for spatial
location of shell relative to other GC clouds). In Butterfield et al. (2018)
we reported a systemic velocity of $\sim$53 km s-1 for the M0.20$-$0.033
expanding shell and advocate that the shell is also located on Orbital Stream
1, based on position-velocity analysis. Indeed, the adjacent locations of
M0.10$-$0.08 and the M0.20$-$0.033 expanding shell (see Figure 1) and their
similar velocities are consistent with both clouds being on the same orbital
stream. Additionally, based on the orbital direction of stream 1, the
M0.10$-$0.08 cloud would be located ‘upstream’ from the M0.20$-$0.033
expanding shell. Based on the orbital solution in Kruijssen et al. (2015),
M0.10$-$0.08 would orbit into the current location of the M0.20$-$0.033
expanding shell in $\sim$0.05 Myrs.

The 10.6 km s-1 component (Figure 4; Section 3.2) covers a velocity range of
$\sim$0$-$20 km s-1 based on analysis of the NH3 (5,5) emission and additional
analysis of the HC3N lines. This velocity range is similar to observed
velocities of the adjacent M0.11$-$0.11 molecular cloud ($\sim$10$-$30 km s-1;
Jones et al., 2012; Clavel et al., 2013, see their Figure 2), and nearby gas
velocities associated with with Kruijssen et al. (2015) orbital stream 3
($\sim$0–5 km s-1). However, there are discrepancies in the literature
concerning the velocity of the M0.11$-$0.11 cloud. Tsuboi et al. (1997), Handa
et al. (2006), and Tsuboi et al. (2011) report a slightly higher velocity
range of 15$-$45 km s-1. These velocity values of the M0.11$-$0.11 cloud in
Tsuboi et al. (1997), Handa et al. (2006), and Tsuboi et al. (2011) are closer
to those of the intermediate velocity component in our observations (37.6 km
s-1; see Section 3.2.1). In our Figure 4 this velocity component appears as a
lower velocity ‘wing’ of the main 51.5 km s-1 component, rather than a
distinct cloud. Further, the morphology of the 37.6 km s-1 component in Figure
5 appears to overlap with the 51.5 km s-1 component with the exception of the
filamentary extension. Therefore, based on the previous work of Jones et al.
(2012) and Clavel et al. (2013) and our analysis above, we interpret the 10.6
km s-1 component as extended emission associated with M0.11$-$0.11.

####  4.1.1 Similar X-ray fluorescence detected in both M0.10$-$0.08 and
M0.11$-$0.11

Observed X-ray fluorescence can be beneficial in determining radial distances,
which, when combined with their projected separation from the Galactic center,
can be used to infer inter-cloud distances (e.g., Clavel et al., 2013; Terrier
et al., 2018). In our Galactic center, fluorescent iron emission at 6.4 keV is
created in molecular clouds by K-shell photo-ionization and Compton scattering
of neutral iron atoms from a previous, gigantic X-ray flare, presumably from
Sgr A∗. By observing the time delay of the detected X-ray reflection across
multiple molecular clouds, we can constrain the locations of clouds from the
geometrical path-length between the clouds and Sgr A∗ and determine their
location along our line-of-sight (e.g., Cramphorn & Sunyaev, 2002). Further,
the time delay between the detected reflections provides a measurement of the
total path traveled by the photons, assuming they were emitted simultaneously.
This path length then gives an indication of the relative locations of the
clouds.

Molecular clouds that show similar illumination at a similar timeframe are
located along the same 3-dimensional ‘parabola’, assuming the illumination
feature is produced by the same, single flaring event (e.g., Sunyaev &
Churazov, 1998). Along this 3-dimensional parabola, the path length of the
propagating light signal (from Sgr A∗ to the cloud and then to the Earth) is
the same at each location and therefore the time delay of the propagating
signal is the same as well. Clavel et al. (2013) detected a similar X-ray
fluorescence signature in both M0.10$-$0.08 and M0.11$-$0.11 (sources Br2 and
G0.11$-$011 in their study).121212The data presented in Clavel et al. (2013)
used Chandra observations from 1999 to 2011 (see their Table 1 for
observational information). These X-ray observations had a resolution of 4″,
and are therefore fairly comparable to the observations presented in this
paper, Table 1. This detection of similar X-ray fluorescence illumination in
M0.10$-$0.08 and M0.11$-$0.11 indicates the two clouds are located along the
same 3-dimensional parabola, assuming the fluorescence in both clouds is from
the same event. Further, because the two clouds are aligned along the same
line of sight, and have a similar X-ray fluorescence light curve, Clavel et
al. (2013) argue the two clouds must be at the same physical position, even
with their differences in velocity. If the two clouds are almost at the same
physical location, then we would expect to see evidence of this interaction.

Figure 7: Position-velocity distribution across the spatial slice shown in
Figure 5, for NH3 (3,3) (top) and HC3N (3$-$2) (bottom). Annotations in the
top panel show the gas associated with M0.11$-$0.11 and M0.10$-$0.08 (see
Section 4.1). The black dashed line shows the magnitude and orientation of the
$\sim$10 km s-1 pc-1 velocity gradient described in Section 3.2. The blue
regions in the top panel show the general locations of the hyperfine satellite
lines ($\pm$20$-$30 km s-1 from the main component) of M0.10$-$0.08. The red
shaded region highlights the ‘bridge’-like feature discussed in Section 4.2.

###  4.2 Proposed Physical Interaction between M0.10$-$0.08 and M0.11$-$0.11

Previous studies have hinted at a possible connection between M0.10$-$0.08 and
M0.11$-$0.11 (Handa et al., 2006; Clavel et al., 2013). However, because of
the large velocity difference between the two clouds along this line-of-sight
($\Delta$v$\sim$30 km s-1), other investigators have suggested these
components are physically separated (Ponti et al., 2010; Kruijssen et al.,
2015). The high-resolution data presented in this paper can provide insight
into this discrepancy in the literature. In this section we perform a detailed
position-velocity analysis on this region to investigate a possible connection
between M0.10$-$0.08 and M0.11$-$0.11.

Figure 7 shows the position-velocity distribution of NH3 (3,3) (top) and HC3N
(3$-$2) (bottom) across the filamentary extension (black box in Figure 5).
This slice was selected to maximize the relatively faint signal of the
M0.11$-$0.11 cloud (top panel in Figure 5) and illustrate a possible
connection to M0.10$-$0.08. The slice contains emission in all three velocity
components (Table 2 and Figure 5). Emission associated with the M0.10$-$0.08
cloud is clearly the brightest component in this region (50$-$60 km s-1), with
possible hyperfine lines above and below the main emission region (blue shaded
region in Figure 7, top). These hyperfine lines have a fixed known separation
from the main component of $\pm$21.1 km s-1 and $\pm$29.1 km s-1 for the NH3
(3,3) transition (e.g., Krieger et al., 2017). The NH3 emission at $\sim$80 km
s-1 is not observed in HC3N (3$-$2) (Figure 7, bottom), suggesting it is
hyperfine line emission. Across this slice there is clear, extended emission
in M0.11$-$0.11 (10.6 km s-1 component; Figure 7, top). The emission in
M0.11$-$0.11 is relatively faint compared to M0.10$-$0.08 and spans a velocity
range from 5 to 25 km s-1.

The 37.6 km s-1 component appears as a lower velocity wing to the 51.5 km s-1
component in the integrated spectrum (Figure 4). When isolating velocity
channels associated with each component we see that some of the gas in the
37.6 km s-1 component is spatially offset from the 51.5 km s-1 component
(Figure 5). We also observe this offset in position-velocity space, where some
of the gas in the 37.6 km s-1 component appears to be spatially offset from
the bulk of the 51.5 km s-1 component (Figure 7, top). Additionally, the 37.6
km s-1 component is mainly associated with emission along the velocity
gradient (see Section 3.2) and appears to be a distinct feature in position-
velocity space. Further, at the southern edge of the velocity gradient there
is a bridge feature with emission between velocities 20$-$40 km s-1 (red
shaded region in Figure 7, top). Including both the bridge feature and the
velocity gradient results in continuous emission between 20$-$50 km s-1,
thereby showing that M0.10$-$0.08 and M0.11$-$0.11 have an apparent connection
in position-velocity space. Analysis of the HC3N (3$-$2) line shows a similar
velocity gradient and bridge-like features across the slice (Figure 7,
bottom). However, the HC3N emission is $\sim$5$-$10$\times$ fainter than the
NH3 (3,3) line, so these features appear loosely connected and barely above
the noise level.

Recent studies simulating Galactic cloud-cloud interactions predict broad
‘bridge’-like feature in position-velocity space, where the two clouds are
physically connected (e.g., Takahira et al., 2014; Haworth et al., 2015; Torii
et al., 2017).131313This labeling of the ‘bridge’-like feature in position-
velocity space, defined in Haworth et al. (2015), should not be confused with
the X-ray definition of the Bridge, labeled as Br1 and Br2 (Ponti et al.,
2010; Clavel et al., 2013), which spatially connects M$-$0.02$-$0.07 to
M0.10$-$0.08. In these studies, there is intermediate-velocity gas between the
two main cloud components, which produces the ‘bridge’ in position-velocity
space. Such bridge features have since been detected in numerous molecular
clouds throughout the galaxy (e.g., Fukui et al., 2016; Torii et al., 2017).

Large-scale observations ($\sim$45″ resolution) of the intermediate velocity
component (15$-$45 km s-1) from Tsuboi et al. (1997) and Handa et al. (2006)
show the gas is extended and dense. Tsuboi et al. (1997) extracted a position-
velocity slice at $b$=$-$6′ (4′$\leq$$l$$\leq$14′) from their CS data cubes
and observed two vertical features in velocity space that spanned 15 km s-1 to
40 km s-1, and were separated by $\sim$2′ (see their Figure 2). Handa et al.
(2006) also saw similar vertical features in velocity space in their H13CO+
and SiO data cubes. Tsuboi et al. (1997) attribute the vertical velocity
features to be an expanding shell centered on a lower emission region near the
centroid of the cloud, where the bright vertical features are the limb
brightened edges of the shell. However, these vertical features could
alternatively be signatures of the bridge feature, discussed above, on larger
scales. At the low spatial resolution of the Handa et al. (2006) and Tsuboi et
al. (1997) observations ($\sim$45″), the detailed substructure we observed at
-45″ to 0″ in Figure 7 (top) would blend into a single pixel. Therefore the
high-resolution gradient and bridge features shown in Figure 7 would appear as
broad, continuous emission at 45″ resolution.

Thus, based on the close physical proximity of M0.10$-$0.08 and M0.11$-$0.11
from X-ray fluorescence data, along with continuous emission connecting them
in position-velocity space via a ‘bridge’ feature, we argue the two clouds are
physical interacting. Furthermore, this would imply that M0.11$-$0.11 is
located on the same stream as M0.10$-$0.08 and not on a separate stream as
indicated by the Kruijssen et al. (2015) orbital model.

###  4.3 Gas Kinematics in CMZ Clouds

Disentangling the molecular gas kinematics in CMZ clouds can be complex. As we
have shown in this paper, the multiple velocity components toward the
M0.10$-$0.08 cloud can make isolating the individual components challenging.
For example, extensive efforts were conducted to fit the three components,
with similar Gaussian fit parameters (Vc & $\sigma$), across multiple NH3
transitions. However, we were unable to get converging values that satisfied
the multiple transitions. The lower J transitions had brighter hyperfine
structure for each component resulting in over nine blended profiles within
the $\sim$0$-$70 km s-1 velocity range which could be fit with numerous
solutions. At the higher J transitions, the 10.6 km s-1 component was not
bright enough to fit the spectrum.

The 37.6 km s-1 component was also especially challenging to fit. Since this
component appears as a low-velocity wing to the 51.5 km s-1 component, there
were numerous solutions to the profile that varied depending on the initial
guesses and range limits in the $pyspeckit$ program, with central velocity
values that ranged from $\sim$30$-$45 km s-1. However, the presence of an
intermediate velocity component between $\sim$30$-$45 km s-1 was clear in all
of our NH3 and HC3N transitions (illustrated by the dashed green residuals in
Figure 4). The fit solution we present in this paper (see Table 2) was the
best fit parameters that accurately reflected the uncertainty in the 37.6 km
s-1 component. However, we note that determining a simple kinematic solution
to complex kinematics in the CMZ can be problematic and requires multiple
methods to disentangle the velocity components (i.e., spectral fitting,
position-velocity analysis, moment images of components, etc).

Furthermore, the complex kinematics in the CMZ can make understanding the gas
flows and unusual orbits more challenging. We have attempted to disentangle
the kinematics towards this complicated region using high spatial and spectral
resolution observations. While we were able to identify the three velocity
components towards this region using a variety of methods, providing a simple
solution that satisfies the kinematics observed in this dataset is more
difficult. Future observations of complex kinematic regions should use a
variety of methods to isolate the velocity components. If possible, future
observations should also use absorption observations toward radio continuum
regions to constrain the line-of-sight arrangement, similar to the approach we
used in Butterfield et al. (2018). Despite the complexity of disentangling the
kinematics of multiple components, the analysis is necessary constrain models
of the large-scale gas structures. Models for the 3-dimensional orientations
of these gas structures can be influenced by assumptions made in complex
kinematic regions. Therefore, applying the solutions in complex kinematic
regions in the models may help resolve some of the contingencies in future
orbital solutions.

##  5 Summary

We present high-resolution ($\sim$3″) VLA radio observations of the compact (3
pc) M0.10$-$0.08 molecular cloud, finding that it is composed of multiple
compact molecular clumps (5+ clumps; Dclumps $\leq$ 0.4 pc; Section 3.1). We
detect 15 molecular transitions in M0.10$-$0.08 (Table 1); including eight
transitions of NH3, two HC3N transitions, OCS, CH3CN, HC5N, and abundant 36.2
GHz CH3OH masers (see Section 3.3 and Appendix A for details on the detected
masers).

The main focus of this paper is on the molecular gas kinematics toward
M0.10$-$0.08. We present the following results from this study:

1) Three velocity components detected toward M0.10$-$0.08: The averaged NH3
(5,5) spectrum reveals three velocity components centered at 10.6, 37.6, and
51.5 km s-1 (see Section 3.2.1, Figures 4 & 5, and Table 2). Initially, the
NH3 (5,5) spectrum was fit was two Gaussian components at $\sim$10$-$15 km s-1
and $\sim$50$-$55 km s-1. However, the residuals of this fit showed excess
emission around 20$-$40 km s-1, which we interpreted to be a third velocity
component (see green dashed line in Figure 4) In our high-resolution data the
51.5 km s-1 component is the brightest emission in this region. The 10.6 km
s-1 component is relatively faint compared to the other two components in the
field. We have also analyzed the gas morphology in each component by isolating
channels associated with each component (Figure 5). The morphology in all
three components is unique.

2) Relationship between M0.10$-$0.08 and Orb- ital Stream 1: M0.10$-$0.08 is
part of a larger structure of gas that contains the M$-$0.02$-$0.07 and
M0.07$-$0.07 molecular clouds and has a velocity of around 50 km s-1 (Tsuboi
et al., 2011). The central velocity of M0.10$-$0.08 (51.5 km s-1; Section 4.1)
indicates that M0.10$-$ 0.08 is likely located on Orbital Stream 1 in the
Kruijssen et al. (2015) model.

3) Resolving the Kinematics of M0.11$-$0.11: Discrepant reports regarding the
central velocity of M0.11$-$ 0.11 range from 10 to 45 km s-1. In our high-
resolution data, we detect two components in this velocity range: 10.6 and
37.6 km s-1. We argue that gas in the 10.6 km s-1 component is associated with
M0.11$-$0.11 as the morphology is distinct from that of the M0.10$-$0.08 cloud
(Figure 5). Additionally, position-velocity analysis towards this region of
the CMZ shows extended emission ($>$70″; $>$2.7 pc) from 0 to 20 km s-1
(Figure 7), that we suggest is associated with M0.11$-$0.11.

4) Physical Interaction Between M0.10$-$0.08 and M0.11$-$0.11: Past X-ray
fluorescence observations by Clavel et al. (2013) show similar time delay
signatures from both M0.10$-$0.08 and M0.11$-$0.11 and argue the two clouds
are in the same physical position of the Galactic center. The intermediate
morphology of the 37.6 km s-1 velocity component could be indicative of a
physical interaction between M0.10$-$0.08 and M0.11$-$0.11. Indeed, all three
velocity components appear to be connected in position-velocity space (Figure
7). The intermediate velocity component, which has similar features to both
M0.10$-$0.08 and M0.11$-$0.11, could be gas from where these two are
physically connected.

##  6 Acknowledgements

This material is based upon work supported by grants from the National Science
Foundation (NSF; no. AST-0907934, AST-15243000, AST-1614782, AST-2008101,
CAREER-2142300). Support for this work was also provided by the NSF through
the Grote Reber Fellowship Program administered by Associated Universities,
Inc./National Radio Astronomy Observatory.

NOB would like to thank Dr. Farhad Yusef-Zadah (Northwestern University), Dr.
Robert Mutel (University of Iowa), Dr. Steven Spangler (University of Iowa),
and Dr. Kennith Gayley (University of Iowa) for their helpful critique on this
thesis analysis. The authors would also like to thank Dr. Allison Costa
(University of Virginia), Dr. Monica Sanchez (University of New Mexico), and
Dr. Diederik Kruijssen (Max-Planck Institute) for their helpful insight on
this work.

The authors would like to thank Dr. Elisabeth Mills (University of Kansas) for
her helpful insight on the spectral line fitting and analysis of the results
presented in this paper. The authors would also like to thank Dr. John Bally
for his inspiration in creating the 3-color image shown in Figure 1. The
authors would also like to thank the anonymous referee for their helpful
insight on this manuscript.

##  Appendix A Catalogue of CH3OH masers in M0.10$-$0.08

To catalogue the properties of these masers we used the Clumpfind algorithm
(Williams et al., 1994). We define all compact sources with peak brightness
temperatures above 400 K as CH3OH masers in this paper, following the
classification used in Mills et al. (2015). Table LABEL:MaserTable presents
the properties of these 64 masers, with their spectra shown in Figure A.1.
Table LABEL:CanTable lists the maser candidates with their spectra shown in
Figure A.2. Results of all 95 detected compact CH3OH sources (both masers and
candidate masers) are discussed in Section 3.3.

Table A.1:  36.2 GHz CH3OH Masers in M0.10$-$0.08 ID | Maser Name | $\alpha$ | $\delta$ | $v$ | FWHM | Ipeak | Flux | Tb | Resolved?
---|---|---|---|---|---|---|---|---|---
|  | (J2000) | (J2000) | km s-1 | km s-1 | Jy b-1 | Jy km s-1 | K |
M1 | M0.1039644-0.0802624 | 17h46m10$\fs$85 | -28°53′21$\farcs$1 | 50.60 | 3.911 | 46.969 | 265.944 | 12438 | YES
M2 | M0.1072762-0.0845517 | 17h46m12$\fs$33 | -28°53′18$\farcs$9 | 43.25 | 7.833 | 29.621 | 252.342 | 7844 | YES
M3 | M0.1033757-0.0817884 | 17h46m11$\fs$12 | -28°53′25$\farcs$7 | 46.40 | 4.332 | 24.917 | 100.982 | 6598 | NO
M4 | M0.1100239-0.0810107 | 17h46m11$\fs$89 | -28°53′03$\farcs$8 | 59.00 | 3.289 | 20.721 |  89.420 | 5487 | YES
M5 | M0.1038130-0.0832737 | 17h46m11$\fs$53 | -28°53′27$\farcs$2 | 52.70 | 4.527 | 19.008 | 131.418 | 5033 | YES
M6 | M0.1096832-0.0812181 | 17h46m11$\fs$89 | -28°53′05$\farcs$3 | 61.10 | 3.197 | 16.980 |  70.044 | 4496 | YES
M7 | M0.1090134-0.0860631 | 17h46m12$\fs$93 | -28°53′16$\farcs$4 | 55.85 | 4.772 | 16.369 | 141.360 | 4334 | YES
M8 | M0.1035684-0.0824885 | 17h46m11$\fs$32 | -28°53′26$\farcs$4 | 51.65 | 4.394 | 15.666 | 108.233 | 4148 | YES
M9 | M0.1078915-0.0897819 | 17h46m13$\fs$64 | -28°53′26$\farcs$8 | 54.80 | 2.835 | 15.543 |  62.866 | 4116 | NO
M10 | M0.0997717-0.0798958 | 17h46m10$\fs$17 | -28°53′33$\farcs$3 | 50.60 | 2.093 | 13.577 | 41.198 | 3595 | NO
M11 | M0.1068280-0.0840072 | 17h46m12$\fs$14 | -28°53′19$\farcs$3 | 51.65 | 6.166 | 11.870 | 133.134 | 3143 | YES
M12 | M0.1072164-0.0806180 | 17h46m11$\fs$40 | -28°53′11$\farcs$7 | 51.65 | 4.871 | 11.623 | 82.500 | 3078 | YES
M13 | M0.1026273-0.0797920 | 17h46m10$\fs$55 | -28°53′24$\farcs$3 | 54.80 | 5.653 | 11.497 | 121.184 | 3044 | YES
M14 | M0.1081318-0.0846147 | 17h46m12$\fs$46 | -28°53′16$\farcs$4 | 55.85 | 5.166 | 10.121 | 82.795 | 2680 | YES
M15 | M0.1078793-0.0799809 | 17h46m11$\fs$34 | -28°53′08$\farcs$5 | 55.85 | 4.417 | 10.026 | 70.744 | 2655 | YES
M16 | M0.1117873-0.0839075 | 17h46m12$\fs$82 | -28°53′03$\farcs$8 | 46.40 | 3.953 | 9.862 | 48.569 | 2611 | NO
M17 | M0.1032901-0.0791549 | 17h46m10$\fs$50 | -28°53′21$\farcs$1 | 52.70 | 5.703 | 9.169 | 86.949 | 2428 | YES
M18 | M0.1054681-0.0800475 | 17h46m11$\fs$02 | -28°53′16$\farcs$0 | 52.70 | 6.421 | 7.465 | 55.618 | 1977 | YES
M19 | M0.1048914-0.0886890 | 17h46m12$\fs$96 | -28°53′34$\farcs$0 | 55.85 | 3.140 | 6.689 | 23.222 | 1771 | NO
M20 | M0.0961419-0.0793034 | 17h46m09$\fs$51 | -28°53′43$\farcs$3 | 57.95 | 9.415 | 6.398 | 75.184 | 1694 | YES
M21 | M0.1090096-0.0854816 | 17h46m12$\fs$79 | -28°53′15$\farcs$3 | 50.60 | 7.226 | 5.984 | 61.843 | 1584 | YES
M22 | M0.1089767-0.0909895 | 17h46m14$\fs$08 | -28°53′25$\farcs$7 | 44.30 | 4.955 | 5.751 | 51.748 | 1522 | YES
M23 | M0.1090577-0.0849852 | 17h46m12$\fs$68 | -28°53′14$\farcs$2 | 49.55 | 6.802 | 5.465 | 58.627 | 1447 | YES
M24 | M0.1115057-0.0826777 | 17h46m12$\fs$49 | -28°53′02$\farcs$4 | 60.05 | 2.935 | 5.460 | 22.068 | 1445 | NO
M25 | M0.1066804-0.0902855 | 17h46m13$\fs$59 | -28°53′31$\farcs$5 | 44.30 | 4.414 | 5.459 | 40.981 | 1445 | YES
M26 | M0.1034462-0.0820959 | 17h46m11$\fs$21 | -28°53′26$\farcs$1 | 59.00 | 5.071 | 5.279 | 38.491 | 1398 | YES
M27 | M0.1084206-0.0843221 | 17h46m12$\fs$44 | -28°53′15$\farcs$0 | 48.50 | 6.924 | 5.277 | 77.437 | 1397 | YES
M28 | M0.1051877-0.0895595 | 17h46m13$\fs$20 | -28°53′34$\farcs$7 | 39.05 | 2.862 | 5.217 | 18.355 | 1381 | YES
M29 | M0.1101091-0.0809589 | 17h46m11$\fs$89 | -28°53′03$\farcs$5 | 54.80 | 3.356 | 5.175 | 31.380 | 1370 | YES
M30 | M0.1062866-0.0796660 | 17h46m11$\fs$04 | -28°53′12$\farcs$8 | 52.70 | 5.321 | 4.661 | 36.543 | 1234 | YES
M31 | M0.0958678-0.0792367 | 17h46m09$\fs$46 | -28°53′44$\farcs$0 | 53.75 | 3.893 | 4.447 | 20.439 | 1177 | NO
M32 | M0.1096798-0.0833221 | 17h46m12$\fs$38 | -28°53′09$\farcs$2 | 59.00 | 3.828 | 4.372 | 26.852 | 1157 | NO
M33 | M0.1107909-0.0832296 | 17h46m12$\fs$52 | -28°53′05$\farcs$6 | 54.80 | 4.924 | 4.322 | 29.242 | 1144 | NO
M34 | M0.1095427-0.0832887 | 17h46m12$\fs$36 | -28°53′09$\farcs$6 | 52.70 | 4.907 | 4.176 | 46.350 | 1105 | YES
M35 | M0.1064915-0.0901670 | 17h46m13$\fs$53 | -28°53′31$\farcs$8 | 41.15 | 6.140 | 4.170 | 35.602 | 1104 | YES
M36 | M0.1022351-0.0825996 | 17h46m11$\fs$15 | -28°53′30$\farcs$8 | 52.70 | 5.473 | 3.974 | 49.319 | 1052 | YES
M37 | M0.1065278-0.0825552 | 17h46m11$\fs$75 | -28°53′17$\farcs$5 | 44.30 | 6.437 | 3.715 | 33.159 | 983 | YES
M38 | M0.1019943-0.0823959 | 17h46m11$\fs$07 | -28°53′31$\farcs$1 | 50.60 | 4.645 | 3.549 | 28.575 | 939 | YES
M39 | M0.1093989-0.0901487 | 17h46m13$\fs$94 | -28°53′22$\farcs$9 | 42.20 | 4.945 | 3.496 | 25.923 | 925 | YES
M40 | M0.1111797-0.0825258 | 17h46m12$\fs$41 | -28°53′03$\farcs$1 | 43.25 | 3.953 | 3.494 | 12.724 | 925 | NO
M41 | M0.1114205-0.0827295 | 17h46m12$\fs$49 | -28°53′02$\farcs$8 | 55.85 | 4.550 | 3.452 | 32.194 | 914 | NO
M42 | M0.1058716-0.0789844 | 17h46m10$\fs$82 | -28°53′12$\farcs$8 | 46.40 | 4.554 | 3.103 | 19.923 | 821 | YES
M43 | M0.1074210-0.0884336 | 17h46m13$\fs$26 | -28°53′25$\farcs$7 | 47.45 | 1.695 | 3.022 | 7.059 | 800 | NO
M44 | M0.1055756-0.0807994 | 17h46m11$\fs$21 | -28°53′17$\farcs$1 | 41.15 | 3.012 | 2.965 | 9.531 | 785 | YES
M45 | M0.1087352-0.0827294 | 17h46m12$\fs$11 | -28°53′11$\farcs$0 | 48.50 | 3.934 | 2.695 | 26.288 | 713 | YES
M46 | M0.1074951-0.0893226 | 17h46m13$\fs$48 | -28°53′27$\farcs$2 | 50.60 | 2.287 | 2.534 | 5.071 | 671 | NO
M47 | M0.1083131-0.0835702 | 17h46m12$\fs$25 | -28°53′13$\farcs$9 | 48.50 | 8.930 | 2.459 | 32.152 | 651 | YES
M48 | M0.1044687-0.0841590 | 17h46m11$\fs$84 | -28°53′26$\farcs$8 | 51.65 | 4.615 | 2.431 | 20.260 | 643 | YES
M49 | M0.1050284-0.0887224 | 17h46m12$\fs$98 | -28°53′33$\farcs$6 | 46.40 | 7.745 | 2.388 | 27.822 | 632 | YES
M50 | M0.1079693-0.0912524 | 17h46m14$\fs$00 | -28°53′29$\farcs$3 | 42.20 | 4.190 | 2.353 | 22.536 | 623 | YES
M51 | M0.1090804-0.0911599 | 17h46m14$\fs$13 | -28°53′25$\farcs$7 | 40.10 | 3.113 | 2.304 | 11.128 | 610 | NO
M52 | M0.0995238-0.0812144 | 17h46m10$\fs$44 | -28°53′36$\farcs$5 | 50.60 | 3.536 | 2.292 | 16.356 | 606 | YES
M53 | M0.1026091-0.0822551 | 17h46m11$\fs$12 | -28°53′29$\farcs$0 | 45.35 | 4.354 | 2.184 | 13.540 | 578 | NO
M54 | M0.1087914-0.0887671 | 17h46m13$\fs$53 | -28°53′22$\farcs$1 | 52.70 | 1.771 | 2.105 | 4.209 | 557 | NO
M55 | M0.1099096-0.0844667 | 17h46m12$\fs$68 | -28°53′10$\farcs$7 | 48.50 | 5.256 | 2.009 | 26.867 | 531 | YES
M56 | M0.1071396-0.0898893 | 17h46m13$\fs$56 | -28°53′29$\farcs$3 | 60.05 | 2.557 | 1.847 | 5.353 | 489 | NO
M57 | M0.1055655-0.0897965 | 17h46m13$\fs$31 | -28°53′34$\farcs$0 | 42.20 | 6.025 | 1.838 | 18.726 | 486 | YES
M58 | M0.1105910-0.0840519 | 17h46m12$\fs$68 | -28°53′07$\farcs$8 | 52.70 | 4.022 | 1.737 | 18.043 | 460 | YES
M59 | M0.1017160-0.0790623 | 17h46m10$\fs$25 | -28°53′25$\farcs$7 | 51.65 | 2.259 | 1.690 | 9.070 | 447 | YES
M60 | M0.1061836-0.0848665 | 17h46m12$\fs$25 | -28°53′22$\farcs$9 | 53.75 | 4.861 | 1.662 | 13.114 | 440 | NO
M61 | M0.1053243-0.0842220 | 17h46m11$\fs$97 | -28°53′24$\farcs$3 | 54.80 | 3.553 | 1.640 | 11.119 | 434 | YES
M62 | M0.1007572-0.0815144 | 17h46m10$\fs$69 | -28°53′33$\farcs$3 | 62.15 | 2.780 | 1.592 | 5.730 | 421 | NO
M63 | M0.1094015-0.0799883 | 17h46m11$\fs$56 | -28°53′03$\farcs$8 | 56.90 | 7.673 | 1.539 | 17.278 | 407 | YES
M64 | M0.0068760-0.0835109 | 17h46m12$\fs$03 | -28°53′18$\farcs$2 | 43.25 | 6.249 | 1.538 | 26.383 | 407 | YES
Table A.2:  36.2 GHz CH3OH Maser Candidates in M0.10$-$0.08 ID | Maser Name | $\alpha$ | $\delta$ | $v$ | FWHM | Ipeak | Flux | Tb | Resolved?
---|---|---|---|---|---|---|---|---|---
|  | (J2000) | (J2000) | km s-1 | km s-1 | Jy b-1 | Jy km s-1 | K |
CM1 | M0.1017240-0.0829108 | 17h46m11$\fs$15 | -28°53′32$\farcs$9 | 55.85 | 6.634 | 1.498 | 20.043 | 396 | YES
CM2 | M0.1073500-0.0827553 | 17h46m11$\fs$92 | -28°53′15$\farcs$3 | 45.35 | 6.884 | 1.436 | 17.249 | 380 | YES
CM3 | M0.1087496-0.0796846 | 17h46m11$\fs$40 | -28°53′05$\farcs$3 | 63.20 | 6.767 | 1.418 | 17.226 | 375 | YES
CM4 | M0.1057278-0.0831589 | 17h46m11$\fs$78 | -28°53′21$\farcs$1 | 48.50 | 8.387 | 1.366 | 14.389 | 361 | YES
CM5 | M0.1114133-0.0842520 | 17h46m12$\fs$85 | -28°53′05$\farcs$6 | 50.60 | 3.705 | 1.365 | 8.593 | 361 | YES
CM6 | M0.0994242-0.0843109 | 17h46m11$\fs$15 | -28°53′42$\farcs$6 | 61.10 | 2.678 | 1.333 | 2.716 | 352 | NO
CM7 | M0.1073396-0.0890670 | 17h46m13$\fs$39 | -28°53′27$\farcs$2 | 44.30 | 1.908 | 1.323 | 2.607 | 350 | NO
CM8 | M0.1061099-0.0893484 | 17h46m13$\fs$28 | -28°53′31$\farcs$5 | 52.70 | 2.900 | 1.252 | 5.884 | 331 | YES
CM9 | M0.1073796-0.0820367 | 17h46m11$\fs$75 | -28°53′13$\farcs$9 | 41.15 | 6.005 | 1.210 | 10.429 | 320 | NO
CM10 | M0.1069128-0.0812699 | 17h46m11$\fs$51 | -28°53′13$\farcs$9 | 46.40 | 6.136 | 1.199 | 9.117 | 317 | YES
CM11 | M0.1054022-0.0856925 | 17h46m12$\fs$33 | -28°53′26$\farcs$8 | 53.75 | 1.864 | 1.165 | 3.439 | 308 | NO
CM12 | M0.1037539-0.0847109 | 17h46m11$\fs$86 | -28°53′30$\farcs$0 | 53.75 | 2.552 | 1.063 | 6.657 | 281 | YES
CM13 | M0.1025205-0.0844109 | 17h46m11$\fs$62 | -28°53′33$\farcs$3 | 53.75 | 1.525 | 1.020 | 1.581 | 270 | NO
CM14 | M0.1086098-0.0871261 | 17h46m13$\fs$12 | -28°53′19$\farcs$6 | 59.00 | 8.039 | 0.989 | 8.824 | 261 | YES
CM15 | M0.1034571-0.0811550 | 17h46m10$\fs$99 | -28°53′24$\farcs$3 | 56.90 | 5.577 | 0.987 | 9.074 | 261 | YES
CM16 | M0.0998050-0.0797587 | 17h46m10$\fs$14 | -28°53′32$\farcs$9 | 56.90 | 3.739 | 0.945 | 5.708 | 250 | NO
CM17 | M0.1070237-0.0799179 | 17h46m11$\fs$21 | -28°53′11$\farcs$0 | 60.05 | 7.663 | 0.930 | 11.283 | 246 | YES
CM18 | M0.1080322-0.0903968 | 17h46m13$\fs$80 | -28°53′27$\farcs$5 | 41.15 | 4.784 | 0.889 | 7.060 | 235 | YES
CM19 | M0.1053015-0.0807328 | 17h46m11$\fs$15 | -28°53′17$\farcs$8 | 45.35 | 4.598 | 0.860 | 7.262 | 227 | YES
CM20 | M0.0989385-0.0806366 | 17h46m10$\fs$22 | -28°53′37$\farcs$2 | 53.75 | 5.362 | 0.855 | 6.528 | 226 | YES
CM21 | M0.1002794-0.0816885 | 17h46m10$\fs$66 | -28°53′35$\farcs$1 | 49.55 | 2.898 | 0.824 | 6.111 | 218 | YES
CM22 | M0.1051058-0.0848221 | 17h46m12$\fs$08 | -28°53′26$\farcs$1 | 52.70 | 3.773 | 0.819 | 5.464 | 216 | YES
CM23 | M0.1093610-0.0816478 | 17h46m11$\fs$94 | -28°53′07$\farcs$1 | 55.85 | 4.041 | 0.811 | 4.470 | 214 | YES
CM24 | M0.1043426-0.0831849 | 17h46m11$\fs$59 | -28°53′25$\farcs$4 | 45.35 | 8.092 | 0.730 | 5.279 | 193 | YES
CM25 | M0.1015946-0.0840405 | 17h46m11$\fs$40 | -28°53′35$\farcs$4 | 48.50 | 2.788 | 0.678 | 2.904 | 179 | YES
CM26 | M0.1090351-0.0814959 | 17h46m11$\fs$86 | -28°53′07$\farcs$8 | 52.70 | 4.308 | 0.668 | 2.702 | 176 | NO
CM27 | M0.1083018-0.0818256 | 17h46m11$\fs$84 | -28°53′10$\farcs$6 | 45.35 | 4.275 | 0.586 | 4.017 | 155 | NO
CM28 | M0.1074053-0.0807365 | 17h46m11$\fs$45 | -28°53′11$\farcs$4 | 47.45 | 4.038 | 0.572 | 3.719 | 151 | YES
CM29 | M0.1016794-0.0813032 | 17h46m10$\fs$77 | -28°53′30$\farcs$0 | 48.50 |  $<$1.05 | 0.540 | 0.553 | 143 | NO
CM30 | M0.1987015-0.0810144 | 17h46m10$\fs$28 | -28°53′38$\farcs$7 | 43.25 |  $<$1.05 | 0.504 | 0.365 | 133 | NO
CM31 | M0.1044869-0.0816958 | 17h46m11$\fs$26 | -28°53′22$\farcs$1 | 62.15 | 4.628 | 0.421 | 4.661 | 111 | YES

Figure A.1: Spectra of detected 36 GHz CH3OH masers in M0.10$-$0.08.  Figure
A.1: (Continued) Figure A.1: (Continued)

Figure A.1: (Continued) Figure A.2: maser candidates CM1(M65) to CM16(M60)
Figure A.2: (Continued)

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# Classification of Emerging Neural Activity from Planning to Grasp Execution
using a Novel EEG-Based BCI Platform
††thanks: Supported by RI-INBRE (NIH P20GM103430) & NSF award ID 2245558.

Anna Cetera, Ali Rabiee, Sima Ghafoori, Reza Abiri  Department of Electrical,
Computer and Biomedical Engineering, University of Rhode Island, Kingston, RI,
USA
Emails: {annacetera, ali.rabiee, sima.ghafoori<EMAIL_ADDRESS>
###### Abstract

There have been different reports of developing Brain-Computer Interface (BCI)
platforms to investigate the noninvasive electroencephalography (EEG) signals
associated with plan-to-grasp tasks in humans. However, these reports were
unable to clearly show evidence of emerging neural activity from the planning
(observation) phase - dominated by the vision cortices - to grasp execution -
dominated by the motor cortices. In this study, we developed a novel vision-
based-grasping BCI platform that distinguishes different grip types (power and
precision) through the phases of plan-to-grasp tasks using EEG signals. Using
our platform and extracting features from Filter Bank Common Spatial Patterns
(FBCSP), we show that frequency-band specific EEG contains discriminative
spatial patterns present in both the observation and movement phases. Support
Vector Machine (SVM) classification (power vs precision) yielded high accuracy
percentages of 74% and 68% for the observation and movement phases in the
alpha band, respectively.

###### Index Terms:

brain-computer interface (BCI), reach-to-grasp tasks, vision control, object
isolation, filter bank common spatial pattern, grip type classification, alpha
Frequency band, support vector machine.

##  I Introduction

The ability to independently navigate and interact with the surrounding
environment through reaching and grasping is fundamental to an individual’s
autonomy and quality of life. Neural correlates associated with natural reach-
and-grasp actions can be decoded and identified through invasive
electrocorticography (ECoG) [1], offering insights into the emergence of
neural activity before and during movement onset.

While current ECoG-based Brain-Computer Interface (BCI) systems have been
explored for assisting individuals with grasp disabilities, existing control
methods often lack a natural and intuitive feeling of control [2, 3]. Common
laboratory experimental set-ups attempt to employ a naturalistic reach-to-
grasp set-up by presenting multiple objects to the participant simultaneously
[4, 5]. In these setups, the lack of object isolation may introduce bias to
the data, creating difficulties in understanding the relationship between the
visual and motor cortices linked to individual objects and their specific grip
types.

To eliminate the possibility of data bias, we employed a novel EEG vision-
based-grasping platform that distinguishes the neural activity between the
observation (planning) phase and movement (grasp execution) phase through
vision control and object isolation. Gaining control over the timing at which
the participants observe the object presented before them enables us to
analyze the emergence of neural activity across different phases of plan-to-
grasp tasks. Meanwhile, isolating the object presented before them allows for
the exploration of distinct neural patterns associated with the object and its
specific grip type (power vs precision).

In this study, we aim to achieve two primary goals. Firstly, to obtain results
that are consistent with the frequency-band-specific neural spatial patterns
reported in ECoG studies to validate our platform, particularly within the
alpha band due to its strong association with motor planning [6, 7, 8].
Secondly, to introduce the possibility of additional frequency-band-specific
features that may be involved with motor planning during the observation phase
without explicit motor imagery instructions [9, 10]. By implementing FBCSP, we
examine the frequency-specific-spatial patterns during plan-to-grasp tasks
while classifying between grasp types using SVM during the observation and
movement phases within specific frequency ranges.

Investigating the emerging neural activity from the planning (observation)
phase - dominated by the vision cortices - to grasp execution - dominated by
the motor cortices within specific frequency bands may offer new insight for a
naturalistic control strategy for noninvasive BCI systems and improve the
quality of life for those with impaired hand dexterity.

##  II Materials and Methods

###  II-A Data Collection Platform

In this study, two distinct objects were selected to execute two specific
reach-to-grasp actions most used in daily life: the precision and power grasp
types. The objects were (i) a pen for precision grasp execution and (ii) a
water bottle for power grasp execution (Figure 2b). The object was presented
to the participant on a sectioned, motorized turntable while wearing a pair of
developed ”smart glasses” capable of transparency alternation (Figure 1a). To
eliminate the possibility of data bias, no object was presented between object
A and object B, the selection of the objects was randomized, and the smart
glasses transitioned from a transparent to an opaque state during the rotation
of the turntable.

Hardware

The presentation of object A, object B, and no object required a novel, PC-
controlled, motorized turntable divided into 3 sections. Each object was
individually placed in one of the three sections (Figure 1b). Two Arduino Uno
microcontrollers were programmed to control the motor driver (TB6600 4A 9-42V
Stepper motor driver), the motor (Bipolar 1.7A Nema 17 Stepper Motor), the
audio cue, and the “smart glasses.” The developed “smart glasses”
enabled/disabled object visibility due to the transparent/opaque capabilities
of PDLC electronic smart film.

Software

The developed software ensured synchrony between the EEG data acquisition and
the hardware components for accurate event logging. PySerial was utilized to
send numerical commands from Python to Arduino IDE to control audio cues and
change the states of the motorized turntable/smart glasses. A simple, real-
time graphical user interface (tkinter) was additionally developed to run in
synchrony with the platform at the time of data collection for the researcher
(Figure 1c).

Figure 1: Experimental setup and EEG electrode placement. (a) Participant
wearing EEG headset and ”smart glasses” seated in front of a motorized
turntable with an object to perform a reach-to-grasp task. (b) 3D-designed
motorized turntable with object A, object B, and no object sections. (c)
Synchronized graphical user interface during data collection. (d) Standard EEG
10-20 system electrode placement with highlighted electrodes (red) used for
analysis.

###  II-B Data Collection

Participants

Data was collected from five human subjects (3 female, 2 male) aged between 20
and 35. Each participant was right-hand dominant with no known motor deficits
or neurological impairments. Each participant attended a single data
collection session (approximately 75 minutes).

Data Acquisition

EEG signals were acquired using the 8-channel Unicorn Hybrid Black headset
with a wet electrode setup (manufactured by g.tec) at a sampling rate of 250
Hz. Electrodes were placed in the following positions according to the
international 10/20 system: Fz, C3, Cz, C4, Pz, PO7, Oz, PO8 (Figure 1d).
Reference and ground electrodes were placed on the left and right mastoid
respectively. Quality testing of EEG signals was performed before each data
recording session within the Unicorn Suite Hybrid Black software environment
(g.tec).

Protocol Design

In our study, the participant was instructed to perform a reach-to-grasp task
with enabled vision capabilities and motor movement execution. The participant
was positioned 30cm from the center of the object with their palms facing
downwards. The structure of the data collection protocol is centered on an
audio-cue-based paradigm, where each data recording session included 5 blocks,
each containing 10 trials, resulting in a total of 50 trials for each object
presented (Figure 2).

Before the audio cue, the participant was instructed to observe the object for
two seconds. Following the audio cue, the participant performed a naturalistic
reach-to-grasp task toward the object presented by the motorized turntable for
four seconds. If no object was presented, the participant was instructed to
execute no movement.

Figure 2: Structure of data collection session and singular EEG trials. (a)
One collection session for each subject involves multiple blocks, rest
periods, and the sequence of events within a single trial including the
periods of turntable rotation, object observation, reach-to-grasp execution,
and auditory cues. (b) Presented objects on the motorized turntable during the
experiment: object A (a water bottle), object B (a pen), and the ’no object’
condition.

###  II-C Data Processing and Feature Extraction

The acquired EEG data was initially filtered with a 60 Hz notch filter to
suppress power line noise. To eliminate low-frequency drift, a zero-phase,
4th-order Butterworth bandpass filter with cutoff frequencies of 0.5 and 40 Hz
was applied. To implement the Filter Bank Common Spatial Pattern algorithm,
subject-specific and object-specific single trial EEG were decomposed into the
following filter banks: (delta: 0-4Hz, theta: 4-8Hz, alpha: 8-13Hz, beta:
13-30Hz, gamma: 30-40Hz), using zero-phase 4th-order Butterworth bandpass
filters.

Two windows of interest were extracted for binary classification: two seconds
before the audio cue (observation phase) and two seconds after the audio cue
(movement phase). The filtered, epoched data was used to calculate the CSP
projection matrix for each object within each filter bank. The first and last
two filter components were extracted to spatially filter the raw, single-trial
EEG data. The feature set included the logarithms of normalized variances from
the most and least discriminative spatial components, which respectively
maximize and minimize variances for each class. In each filtered trial, four
CSP features were derived and implemented for binary classification.

###  II-D Classification

The FBCSP features were utilized for binary classification to distinguish
between the power grasp and precision grasp during the observation and
movement phase within different frequency bands. The Support Vector Machine
(SVM) algorithm was used to perform binary classification. Table I displays
subject-based binary classification results (percent accuracy) within each
frequency band during both the observation and movement phases of the
experiment.

##  III Results

###  III-A Common Spatial Pattern

The CSPs extracted from the alpha band during the observation and movement
phases are shown in Figure 3. During the observation phase, CSP #1 displays an
increase of alpha power within the temporal and occipital regions of the brain
(indicated by red) while being surrounded by lower alpha power (indicated by
blue) in the frontal regions of the brain. CSP #2 features lateralized
activity as lower and higher alpha band power are localized over the left and
right hemisphere regions respectively. Both CSP #3 and CSP #4 display reduced
alpha activity in the occipital region, while CSP #1 and CSP #2 display
increased alpha activity in the same location.

The first common spatial pattern during the movement phase (CSP #1) exhibits
localized alpha activity in the central region of the brain. CSP #3 and CSP #4
display an increase of alpha activity in the occipital region, however,
opposite alpha activities occur across the motor cortex region between the two
patterns.

Figure 3: Topographical plots of extracted common spatial patterns (CSPs) in
the alpha band during different task phases for subject 3. The upper panel
illustrates CSPs #1 to #4 during the observation phase. The lower panel
displays the corresponding CSPs during the movement phase. Each map is scaled
from -0.50 to +0.50, reflecting changes in amplitude within the alpha band

###  III-B Classification

The classification results in Table I display that analysis of the mean
accuracies for binary classification between power grasp and precision grasp
during both the observation and movement phases yields insightful trends,
particularly in the alpha, delta, and gamma frequency bands. Across all
subjects, the features within the alpha band contributed to the highest mean
accuracy 74% in the observation phase and 67% in the movement phase.
Furthermore, mean accuracies in the delta and gamma bands demonstrate an
increase during the observation phase compared to the movement phase, with
delta band accuracies rising from 55% to 68% and gamma from 63% to 65%.

The box-and-whisker plots in Figure 4 depict the distribution of SVM
classification accuracies across different EEG filter bank frequency bands
during the observation and movement phases. Upon initial observation, there is
an increase in overall percent accuracy across all subjects during the
observation phase (except for the gamma band), contributing to the separation
between the two phases. The alpha band exhibits the highest median accuracy
during both phases across all frequency bands, suggesting that features
derived from the alpha band are the most discriminative for classifying grip
types. The median accuracies in the alpha band surpass those in the delta,
theta, beta, and gamma bands, which indicates it as a consistent feature with
less variability in the classification performance in distinguishing between
the power and precision grasps across different trials or subjects.

TABLE I: Subject-Based Grip Type Classification during Observation and
Movement Phase

| Observation Phase (%) | Movement Phase (%)
---|---|---
Subjects | Delta | Theta | Alpha | Beta | Gamma | Delta | Theta | Alpha | Beta | Gamma
s1 | 45 | 55 | 80 | 50 | 60 | 45 | 50 | 65 | 45 | 70
s2 | 75 | 60 | 70 | 60 | 50 | 65 | 40 | 60 | 50 | 45
s3 | 80 | 65 | 70 | 75 | 65 | 60 | 65 | 80 | 80 | 75
s4 | 80 | 60 | 85 | 60 | 70 | 65 | 75 | 75 | 65 | 60
s5 | 60 | 65 | 65 | 75 | 55 | 40 | 65 | 55 | 55 | 65
Mean | 68 | 61 | 74 | 64 | 65 | 55 | 59 | 67 | 59 | 63

Figure 4: Box-and-whisker plots illustrating the distribution of SVM
classification accuracies across five frequency bands during the observation
and movement phases. Blue boxes represent accuracies during the observation
phase, while red boxes correspond to the movement phase.

##  IV Discussion

Our novel platform’s ability to separate phases during plan-to-grasp tasks and
to isolate objects allows us to observe distinct trends in neural activity
during the observation and movement phases of reach-to-grasp tasks respective
to the specific object presented. The derived CSP topographies, specifically
in the alpha band during the observation phase, could have significant
implications for understanding motor planning. Alpha activity is known to be
associated with motor planning [7], and the trends observed in our study are
consistent with this connection since the detected ERD during the observation
phase is evident in our results. ERD is present within the occipital regions
of the brain (responsible for processing visual stimuli) where decreased
levels of alpha activity are present within the third and fourth CSPs during
the observation phase.

The SVM classification accuracies derived from the FBCSP features, especially
within the alpha band, demonstrated the highest percent accuracy across all
subjects during the observation phase is also consistent with previous
literature in that this particular frequency band plays a pivotal role in
discriminating between the 2 grasps types. These findings validate our novel
platform, while also introducing the possibility of exploring delta and gamma
frequency bands that may be involved in motor planning without explicit motor
imagery instructions due to their high accuracy percentages based on our
findings. Although these accuracy percentages are less significant than those
observed in the alpha band, they could be indicative of the delta band’s
association with integrative sensory processing and the gamma band’s link to
higher-level cognitive functions. The increased accuracies in these bands
during the movement phase may reflect the increased demand on sensorimotor
integration and the heightened cognitive engagement required for executing the
motor task.

##  V Conclusion

The ability to control vision and separate the observation phase from the
movement phase during reach-to-grasp tasks with our novel platform enabled us
to uncover neural mechanisms and activity associated with object-specific
visually guided tasks using noninvasive EEG. Isolating the object presented
introduced the possibility of novel findings regarding how delta and gamma
frequency activity might play a role in motor planning for grasp
classification. Without explicit motor imagery instructions, we were able to
detect significant neural patterns during the observation phase.

The outcomes gained from performing FBCSP/SVM on the EEG data collected from
our novel platform for grip type classification open the possibility of
exploration of frequency band-specific neural activity that is solely
associated with the singular object presented to the participant. This
platform facilitates the investigation for understanding the interplay between
different brain regions at various frequency bands during motor planning and
execution, particularly in the context of complex tasks like power and
precision grasps. This novel vision-based-grasping platform presents a new
direction for non-invasive BCI systems by exploring the emergence of neural
activity during motor planning and visually guided tasks.

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