# An Olfactory EEG Signal Classification Network Based on Frequency Band
Feature Extraction

Biao Sun1, , Zhigang Wei1, Pei Liang2, and Huirang Hou1,∗ This work was
supported by the National Natural Science Foundation of China under Grant
61971303 and the China Postdoctoral Science Foundation 2021M692390.1 School of
Electrical and Information Engineering, Tianjin University, Tianjin 300072,
China.2 College of Optical and Electronic Technology, China Jiliang
University, Hangzhou 310018 China.∗ Corresponding author: Huirang Hou. Email:
<EMAIL_ADDRESS>authors would like to thank Professor Qinghao Meng
from School of Electrical and Information Engineering of Tianjin University
for sharing the olfactory EEG dataset.

###### Abstract

Classification of olfactory-induced electroencephalogram (EEG) signals has
shown great potential in many fields. Since different frequency bands within
the EEG signals contain different information, extracting specific frequency
bands for classification performance is important. Moreover, due to the large
inter-subject variability of the EEG signals, extracting frequency bands with
subject-specific information rather than general information is crucial.
Considering these, the focus of this letter is to classify the olfactory EEG
signals by exploiting the spectral-domain information of specific frequency
bands. In this letter, we present an olfactory EEG signal classification
network based on frequency band feature extraction. A frequency band generator
is first designed to extract frequency bands via the sliding window technique.
Then, a frequency band attention mechanism is proposed to optimize frequency
bands for a specific subject adaptively. Last, a convolutional neural network
(CNN) is constructed to extract the spatio-spectral information and predict
the EEG category. Comparison experiment results reveal that the proposed
method outperforms a series of baseline methods in terms of both
classification quality and inter-subject robustness. Ablation experiment
results demonstrate the effectiveness of each component of the proposed
method.

###### Index Terms:

EEG, frequency band extraction, attention mechanism, subject-specific, deep
learning

##  I Introduction

Electroencephalogram (EEG) signals are an important source of information for
studying brain activity, and its current research is mainly focused on visual
[1], auditory [2], and motor imagery [3]. As an essential sense, the olfactory
can directly stimulate memory and trigger strong emotions [4]. Due to the
great potential of the olfactory, there have been substantial studies devoted
to exploring olfactory EEG signals. Previous research has shown that the
classification of olfactory EEG signals plays a vital role in many fields,
including neuroscience research [5], disorder treatment [6], multimedia [7],
and brain-computer interfaces [8].

For EEG signal analysis, feature extraction is of particular importance. To
date, various methods of extracting features have been used in EEG signal
analysis [9, 10, 11]. Power-spectral-density (PSD) is a popular method for
feature extraction, which explores spectral-domain information in EEG signals
[12]. Since different frequency bands of the EEG signals contain different
information, many methods have been proposed to extract specific frequency
bands for EEG signal classification. In the common spatial pattern-based
research [13, 14, 15], to identify specific frequency bands, [16] exploits the
mutual information of the frequency bands, and [17] exploits the Fisher ratio
of frequency band power.

In the traditional PSD-based EEG signal classification approach, the EEG
frequency spectrum is divided into five sub-bands based on biological rhythms:
$\delta$ (0.5-4 Hz), $\theta$ (4-7.5 Hz), $\alpha$ (8-13 Hz), $\beta$ (13-30
Hz), $\gamma$ ($>$30 Hz), and the PSD is extracted from these sub-bands [18,
19]. Recently an average frequency band division (AFBD) method is proposed,
which averagely divides the PSD curve into several sub-bands with equal
bandwidth and obtains significant breakthroughs for frequency band division
[20]. These methods of extracting frequency bands are heuristic or manual,
leading the extracted frequency bands to be shared across subjects. However,
EEG signals have large inter-subject variability, and the shared across
subjects frequency bands are general, ignoring informative differences between
subjects. So the existing methods of extracting frequency bands can be
improved.
Recent advances in deep learning technologies have exhibited promising
capabilities in handling inter-subject variability. In [21], the deep learning
technology is used in EEG channel selection aimed to generate the subject-
specific channel combination. In [22], the authors utilize the deep learning
technology to explore the most discriminative temporal periods for each
subject. In [23], based on deep learning technology, a novel regional
attention CNN is designed to adaptively identify the activated area of the
primary sensorimotor for each subject. While there are currently few
explorations on frequency band extraction using deep learning technology, its
potential for extracting frequency bands has only begun to be explored.
In this letter, we present an olfactory EEG signal classification network
(OESCN) based on frequency band feature extraction, which aimed to exploit the
characteristics of EEG signals in spectral-domain to classify olfactory EEG
signals. First, a frequency band generator is designed to extract frequency
bands based on PSD features via the sliding window technique. Then, a
frequency band attention mechanism is proposed to reweight frequency bands for
a specific subject. Finally, a CNN is constructed to extract the spatio-
spectral information of the encoded EEG signals and predict the EEG category.
Comparison experiment results reveal that the proposed method outperforms a
series of baseline methods. Ablation experiment results demonstrate the
effectiveness of each component of the proposed method.

Figure 1: Overview of the proposed olfactory EEG signal classification network
based on frequency band feature extraction. A Welch periodogram is firstly
applied to estimate PSD features from the raw EEG signals. Secondly, a
frequency band generator is designed to extract frequency bands based on PSD
features using the sliding window technique. Thirdly, a frequency band
attention mechanism adaptively optimizes frequency bands for a specific
subject. Lastly a CNN is constructed as the classifier to predict the EEG
category.

##  II METHOD

###  II-A Pipeline Overview

Fig. 1 shows an overview of the proposed OESCN. The goal is to predict the EEG
category based on the raw EEG signals $\textbf{X}\in\mathbb{R}^{C\times T}$
with $C$ EEG channels and $T$ time points. For each channel, a Welch
periodogram is firstly applied to estimate PSD features for frequencies
between 0.5 Hz and 70 Hz. Secondly, a designed frequency band generator uses
the sliding window technique to extract frequency bands containing different
EEG information from PSD features. Thirdly, a frequency band attention
mechanism is proposed to reweight frequency bands to optimize for a specific
subject. Lastly, a CNN is constructed as the classifier to predict the EEG
category.

###  II-B Frequency Band Generator

In the frequency band generator, as shown on the left side of Fig. 1, a Welch
periodogram is first applied to the EEG signals X at each channel to estimate
PSD features $\textbf{F}\in\mathbb{R}^{C\times P}$ between 0.5 Hz and 70 Hz,
where $P$ is the number of PSD features. The Hamming window of the Welch
periodogram is set to 200, and the number of overlap points between adjacent
windows of the Welch periodogram is set to 8.

For each channel of the PSD features $\textbf{F}_{c}\in\mathbb{R}^{1\times
P}$, a sliding window technique splits $\textbf{F}_{c}$ into several frequency
band combinations $\\{{\textbf{A}^{ci}\in\mathbb{R}^{1\times
L_{ci}\times{B_{ci}}},i=1,...,N}\\}$, where $i$ is the index of frequency band
combinations, $N$ is the number of frequency band combinations, ${L_{ci}}$ is
the window length of a frequency band slice and ${B_{ci}}$ is the number of
frequency bands. The ${B_{ci}}$ is calculated by:

${B_{ci}}=\left\lfloor{(P-{L_{ci}})/G}\right\rfloor,$ (1)

where $G$ is the increment between two neighboring frequency band slices.
Then, the averaging method is used to calculate the mean of each frequency
band for each ${\textbf{A}^{ci}}$, the new frequency band combinations are
$\\{{\textbf{D}^{ci}\in\mathbb{R}^{1\times{B_{ci}}},i=1,...,N}\\}$. Then, all
${\textbf{D}^{ci}}$ are concatenated along the channel dimension into a
frequency band combination $\left\\{{\textbf{S}_{c}\in{\mathbb{R}^{1\times
K}}\left|{\textbf{S}_{c}={\rm{Concat}}({\textbf{D}^{ci}}),i=1,...,N}\right.}\right\\}$,
where $K$ is calculated by:

$K=\sum\nolimits_{i}{{B_{ci}}}.$ (2)

Lastly, all ${\textbf{S}_{c}}$ are concatenated along the frequency band
combination dimension into a frequency band combination
$\textbf{S}\in{\mathbb{R}^{C\times K}}$.

In this work, a frequency band generator is designed to extract the frequency
band combination S based on the PSD features F. Since different frequency
bands of the EEG signal may contain different information, our goal is to
extract as many useful frequency bands as possible to form a frequency band
combination. Inspired by the traditional EEG frequency spectrum division
method based on biological rhythm involving five sub-bands [24] and the recent
average frequency band division method in which the PSD features are divided
into several sub-bands with the same frequency bandwidth [20], we select five
window lengths of frequency band slice: 1, 5, 10, 15, and 20 Hz, i.e.,
${{L_{i}}\in\left\\{{1,5,10,15,20}\right\\}}$. The increment between two
neighboring frequency band slices $G$ is consistently 1 Hz.

Figure 2: Schematic diagrams of the (a) self-attention module, (b) head fusion
module, and (c) CNN of the proposed OESCN.

###  II-C Frequency Band Attention Mechanism

As shown on the right side of Fig. 1, inspired by the multi-head attention
mechanism [25], we design a frequency band attention mechanism to reweight
frequency bands, optimizing the frequency band combination suitable for a
specific subject. The frequency band combination S is calculated in parallel
by 1+N heads of frequency band attention mechanism, which allows the model to
attend information from the global and local subspaces.

In the first head, S as a whole is used as the input of the self-attention
module to utilize the global information between frequency bands of all
bandwidths. In the self-attention module [Fig. 2 (a)], S is first linearly
transformed into three latent spaces:

${\textbf{Q}^{\rm{glo}}=}\textbf{S}{\textbf{W}^{\rm{query}}},\quad\,{\textbf{K}^{\rm{glo}}=}\textbf{S}{\textbf{W}^{\rm{key}}},\quad{\textbf{V}^{\rm{glo}}=}\textbf{S}{\textbf{W}^{\rm{value}}},$
(3)

where $\textbf{Q}^{\rm{glo}}$, $\textbf{K}^{\rm{glo}}$,
$\textbf{V}^{\rm{glo}}\in{\mathbb{R}^{C\times K}}$, and
${\textbf{W}^{\rm{query}}}$, ${\textbf{W}^{\rm{key}}}$,
${\textbf{W}^{\rm{value}}}$ are parameter matrices $\in{\mathbb{R}^{K\times
K}}$. We compute the dot products of the $\textbf{Q}^{\rm{glo}}$ with
$\textbf{K}^{\rm{glo}}$, divide each by $C$, where $C$ is the number of EEG
electrode nodes. Finally, we apply a softmax function to obtain the weights on
$\textbf{V}^{\rm{glo}}$. The output of the first head is:

${\textbf{H}^{\rm{glo}}}=\textbf{V}^{\rm{glo}}{\rm{Softmax}}(\frac{{{{\textbf{Q}^{\rm{glo}}}^{T}}\textbf{K}^{\rm{glo}}}}{\sqrt{C}}),\quad{\textbf{H}^{\rm{glo}}}\in{\mathbb{R}^{C\times
K}}.$ (4)

For the rest of N heads, S is firstly divided into multiple frequency band
combinations ${{\textbf{S}^{i}}\in{\mathbb{R}^{C\times B_{ci}}}}$ with the
same bandwidth:
$\left\\{{{\textbf{S}^{i}_{c}}={\textbf{D}^{ci}},i=1,...,N}\right\\}$. Then
each ${{\textbf{S}^{i}}}$ is served as the input of one head of N heads. In
this way, local information between frequency bands of the same bandwidth is
used. In each head, ${{\textbf{S}^{i}}}$ is calculated by self-attention
module, the output of each head is
${\textbf{H}^{\rm{loc\\_i}}\in{\mathbb{R}^{C\times B_{ci}}}}$. Lastly, all
${\textbf{H}^{loc\\_i}}$ are concatenated along the channel dimension:

${\textbf{H}^{\rm{loc}}}={\rm Concat}({{\textbf{H}^{{\rm
loc\\_i}}}}),\quad{\textbf{H}^{\rm{loc}}}\in{\mathbb{R}^{C\times K}}.$ (5)

To effectively fuse the outputs of I+N heads, we design a head fusion module
as shown in Fig. 2 (b). The max-pooling layer and the average pooling layer
can extract different information. The convolutional layer with a kernel size
of (1,1) aggregates features into the same width as the input. The output of
the head fusion module is:

$\textbf{M}={\rm{Conv}}\left\\{\begin{array}[]{l}{\rm{Concat}}({\rm{MaxPooling}}\left[{{\rm{Concat}}({\textbf{H}^{\rm{glo}}};{\textbf{H}^{\rm{loc}}}})\right]\\\
;{\rm{AvePooling}}\left[{{\rm{Concat}}({\textbf{H}^{\rm{glo}}};{\textbf{H}^{\rm{loc}}}})\right])\end{array}\right\\},$
(6)

where ${\textbf{M}}\in{\mathbb{R}^{C\times K}}$. We add the skip connection,
the optimal frequency band combination $\textbf{M}^{\prime}$ is calculated by:

${\textbf{M}^{\prime}}{\rm{=}}\textbf{M}+\textbf{S},\quad{\textbf{M}^{\prime}}\in{\mathbb{R}^{C\times
K}}.$ (7)

###  II-D classifier

A specially designed CNN is constructed as the classifier to predict the EEG
category [Fig. 2 (c)]. To extract the spatio-spectral information of
$\textbf{M}^{\prime}$, including the spatial information in EEG electrode
nodes and the spectral information in frequency bands, the kernel size of
convolutional layers are set as (n,n). The input $\textbf{M}^{\prime}$
parallel goes through three convolutional layers of different kernel sizes
(3$\times$3, 8$\times$8, 15$\times$15) to extract features of different
dimensions, followed by an ELU activation layer to maintain non-linearity.
Then, the three outputs are concatenated along the channel dimension as the
input of the average pooling layer to reduce the number of parameters and to
extract important information. Then, the output goes through the convolutional
layer with kernel size (3$\times$3), the ELU activation layer, and the average
pooling layer to further extra features. Lastly, three fully connected layers
and the softmax activation function are used to predict the EEG category. In
addition, the batch normalization (BN) layer and dropout layer with 0.25 keep
probability are included to prevent overfitting.

##  III EXPERIMENTS AND RESULTS

Figure 3: Schedule for a subject to conduct an experimental record.

In this study, 13 odors were used for stimulation, including five olfactometer
odors (rose, caramel, rotten smell, canned peaches, and excrement) and eight
essential oils (mint, tea, coffee, rosemary, jasmine, lemon, vanilla, and
lavender). Eleven healthy, right-handed subjects (eight males and three
females), aged 24.9$\pm$3.0 years were recruited from the School of Electrical
and Information Engineering at Tianjin University. They reported never having
smoked or abused drugs and having no history of the respiratory, neurological,
or olfactory disease. The experiment was conducted in a room of length 4 m,
width 2 m, and height 2.5 m, which was equipped with an air conditioner to
maintain the indoor temperature as well as an exhaust fan and an air purifier
to eliminate residual indoor odor. The EEG signals were acquired using the
Cerebus system at a sampling rate of 1 kHz. The 32 channels of the EEG cap
were arranged according to the international 10-20 system. Of the 32
electrodes, all but two reference electrodes can be used for signal analysis.
All EEG recording procedures were approved by the Tianjin University Ethics
Committee (No. TJUE-2020-186).

As shown in Fig. 3, for each subject, 13 classes of EEG signals were
collected, corresponding to 13 kinds of odors. For each class of EEG signals,
there were 35 trials. Each trial lasted 10s, and the interval between two
trials was set to 4 seconds to avoid olfactory fatigue. Moreover, the rest
time between two classes of odor stimulation was 2 minutes. Therefore, there
were 455 (455 = 13 $\times$ 35) trials for each subject. Thus, there were 455
$\times$ 11 = 5005 trials in total.

We built and tested the FBEN using Pytorch in a Python 3.7 environment on a
personal computer equipped with a 3.60-GHz Intel Core I7 central processor, an
NVIDIA Titan Xp graphics processor, and 32 Gb of RAM. Data from each subject
were divided into training and validation sets, and method performance was
evaluated in terms of the classification accuracy metric. In every subject,
trials were randomly divided into ten equivalent parts (10-fold cross-
validation), one of which was retained as the validation set to test the
method performance, while the other nine were used as the training set. During
the 10-fold cross-validation, each of the ten parts served once as the
validation set. The ten results were averaged together to produce a single
estimate. Moreover, since the OESCN is applied to the olfactory EEG signal
classification task, the cross-entropy loss function was used. The Adam
optimizer with a learning rate of 0.0001 was used to train the network. The
epoch number was 500, and the batch size was 39.

To evaluate the performance of the proposed OESCN, we benchmarked it against
the EEGNet [26] and the AFBD-SVM method [20]. EEGNet is a classic deep
learning network with excellent performance for processing the EEG signals,
which encapsulated well-known EEG feature extraction concepts for brain-
computer-interface in order to construct a uniform approach for different
brain-computer interface paradigms. AFBD-SVM proposed a manual frequency band
extraction method with excellent performance cooperating SVM as the
classifier.

TABLE I: Classification accuracies of EEGNet, AFBD-SVM, OESCN, OESCN_a1 AND OESCN_a2 Subject |  Accuracy % (mean $\pm$ std)
---|---
EEGNet | AFBD-SVM | OESCN | OESCN_a1 | OESCN_a2
1 | 87.1$\pm$4.9 |  66.2$\pm$4.1 |  86.5$\pm$4.7 |  83.1$\pm$2.7 |  79.7$\pm$5.5
2 |  61.3$\pm$4.3 |  90.1$\pm$3.8 | 97.8$\pm$1.8 |  96.2$\pm$2.5 |  95.7$\pm$2.8
3 |  95.8$\pm$2.6 |  97.8$\pm$1.4 | 99.6$\pm$0.9 |  99.3$\pm$1.1 |  98.8$\pm$1.9
4 |  65.8$\pm$9.9 |  70.8$\pm$6.1 | 99.2$\pm$1.3 |  97.8$\pm$1.5 |  96.1$\pm$3.4
5 |  88.3$\pm$3.3 |  85.3$\pm$5.8 | 97.2$\pm$2.3 |  95.8$\pm$2.9 |  94.0$\pm$3.8
6 |  81.9$\pm$8.0 |  91.9$\pm$3.1 | 99.0$\pm$1.0 |  98.3$\pm$1.2 |  96.9$\pm$3.2
7 |  89.4$\pm$4.4 |  89.5$\pm$3.9 | 95.4$\pm$1.2 |  94.4$\pm$2.7 |  93.1$\pm$3.6
8 |  97.2$\pm$2.5 |  98.5$\pm$1.0 |  99.8$\pm$0.9 | 100$\pm$0 |  99.3$\pm$1.1
9 |  89.0$\pm$5.7 |  89.7$\pm$3.4 | 98.5$\pm$1.8 |  96.5$\pm$2.8 |  93.3$\pm$2.6
10 |  68.9$\pm$5.5 |  81.8$\pm$4.1 | 94.6$\pm$2.8 |  93.7$\pm$3.2 |  91.2$\pm$2.4
11 |  97.4$\pm$2.2 |  98.3$\pm$1.3 | 100$\pm$0 | 100$\pm$0 |  99.6$\pm$0.9
Average | 83.8 | 87.3 | 97.1 | 95.9 | 94.3
Inter-subject std | 12.3 | 10.2 | 3.7 | 4.5 | 5.3

As shown in Table I, we observe that the OESCN outperforms the other two
methods, showing an average accuracy of 97.1${\rm{\%}}$, compared to
83.8${\rm{\%}}$ for EEGNet and 87.3${\rm{\%}}$ for AFBD-SVM. The results
indicate that OESCN provides a 13.3${\rm{\%}}$ improvement with respect to
EEGNet and a 9.8${\rm{\%}}$ improvement with respect to AFBD-SVM in terms of
average accuracy. The inter-subject average standard deviations (std) of
accuracy for three methods are shown in Table I. We observe that the inter-
subject average standard deviation of OESCN (std = 3.7) is further lower than
that of EEGNet (std = 12.3) and that of AFBD-SVM (std = 10.2), indicating the
robustness to inter-subject variation of the proposed method. The intra-
subject standard deviations are also shown in Table I. We observe that the
intra-subject std of OESCN is lower than that of EEGNet and AFBD-SVM.

Next, an ablation study is then performed to evaluate the effectiveness of
each component of the proposed OESCN. The necessity of the frequency band
attention mechanism is first studied, we get a new network for performance
comparison by removing the frequency band attention mechanism module from
OESCN, which named OESCN_a1. The necessity of the frequency band combination
generator is then studied, we get a new network for performance comparison by
removing the frequency band combination generator from OESCN_a1, which named
OESCN_a2. The detailed comparison results with the OESCN_a2 and the OESCN_a1
are shown in Table I, the average accuracies of OESCN_a2, OESCN_a1, and OESCN
are 94.3${\rm{\%}}$, 95.9${\rm{\%}}$, and 97.1${\rm{\%}}$ respectively and the
corresponding inter-subject average std of accuracy for OESCN_a2, OESCN_a1,
and OESCN are 5.3, 4.5, 3.7 respectively. The intra-subject standard
deviations are also shown in Table I. We observe that both the frequency band
attention mechanism and the frequency band generator are effective to improve
the classification accuracy, inter-subject robustness, and intra-subject
robustness.

Figure 4: Attention weights of five local head from subject 1 (top) and
subject 3 (bottom).

We save the attention weights of five local heads from two subjects and draw
the heatmap respectively, as shown in Fig. 4. Results show that for each
subject the different local heads attend different frequency bands, and
generally frequency bands containing high spectral-domain information are more
attended. Previous research has shown that high spectral-domain information is
highly associated with olfactory EEG [27]. In addition, the results also show
that the attention weights of the two subjects are different.

##  IV CONCLUSION

This letter proposes an olfactory EEG signal classification network (OESCN)
based on frequency band feature extraction, which is focused to exploit the
spectral-domain information in olfactory EEG signals fully. The experiment
results show that the proposed method has high classification accuracy and
robustness to inter-subject and intra-subject variations. The proposed
approach is not limited to the PSD feature and shows generality, which is also
applicable to other spectral-domain features.

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