# Assessing gender fairness in EEG-based machine learning detection of
Parkinson’s disease:
A multi-center study

Anna Kurbatskaya1, Alberto Jaramillo-Jimenez234, John Fredy Ochoa-Gomez5,
Kolbjørn Brønnick23 and Alvaro Fernandez-Quilez16
1Department of Electrical Engineering and Computer Science, University of
Stavanger, Stavanger, Norway
2Centre for Age-Related Medicine (SESAM), Stavanger University Hospital,
Stavanger, Norway
3Department of Public Health, Faculty of Health Sciences, University of
Stavanger, Stavanger, Norway
4Grupo de Neurociencias de Antioquia, Universidad de Antioquia, School of
Medicine, Medellín, Colombia
5Grupo Neuropsicología y Conducta, Universidad de Antioquia, School of
Medicine, Medellín, Colombia
6Stavanger Medical Imaging Laboratory (SMIL), Department of Radiology,
Stavanger University Hospital, Stavanger, Norway
Email<EMAIL_ADDRESS><EMAIL_ADDRESS><EMAIL_ADDRESS><EMAIL_ADDRESS><EMAIL_ADDRESS>
###### Abstract

As the number of automatic tools based on machine learning (ML) and resting-
state electroencephalography (rs-EEG) for Parkinson’s disease (PD) detection
keeps growing, the assessment of possible exacerbation of health disparities
by means of fairness and bias analysis becomes more relevant. Protected
attributes, such as gender, play an important role in PD diagnosis
development. However, analysis of sub-group populations stemming from
different genders is seldom taken into consideration in ML models’ development
or the performance assessment for PD detection. In this work, we perform a
systematic analysis of the detection ability for gender sub-groups in a multi-
center setting of a previously developed ML algorithm based on power spectral
density (PSD) features of rs-EEG. We find significant differences in the PD
detection ability for males and females at testing time (80.5$\%$ vs. 63.7$\%$
accuracy) and significantly higher activity for a set of parietal and frontal
EEG channels and frequency sub-bands for PD and non-PD males that might
explain the differences in the PD detection ability for the gender sub-groups.

###### Index Terms:

fairness analysis, Parkinson’s disease, EEG, machine learning, classification,
multi-center

##  I Introduction

Machine learning (ML) advances are facilitating and accelerating the
development of automatic tools aimed to aid clinicians in their daily tasks
[1]. As the number of ML tools continues to grow and affect high-stake
decisions, fairness analysis and bias assessment are becoming equally
important to ensure that algorithms do not create or amplify existing biases
in the population under analysis. On the contrary, calls have been made to
emphasize getting an equally accurate performance for sub-populations stemming
from, for example, different genders or ethnicities [2, 3].

Parkinson’s disease (PD) is a slowly progressive neurodegenerative disorder,
primarily manifesting in typical parkinsonian motor symptoms [4]. Currently,
no diagnostic tests allow clinicians to make a definitive early diagnosis.
Electroencephalography (EEG) presents a non-invasive and low-cost alternative
method that has been shown to be a reliable clinical research tool for PD [5].
In particular, quantitative EEG (qEEG) and spectral features, such as power
spectral density (PSD), have garnered considerable interest due to the
association of their changes with the progression of PD [6, 7].

Gender, along with aging, genetics, environment, and the immune system, play
an essential role in the development and phenotypical expression of PD [8].
Research has shown that the risk of PD is higher for men [9], although there
is a higher mortality rate and faster progression of the disease for women
[10]. Further, men and women with PD tend to have different motor and non-
motor profiles [11]. For instance, imaging studies have reported significant
differences for PD males and females in executive functions, more atrophy in
cortical regions for males, cortical thinning in post-central and pre-central
regions [12], and reduced global cognition for PD males than females [13].

Although a considerable amount of work has been carried out in rs-EEG and ML
for PD [14, 15], fairness and sub-group analysis of the developed tools is
seldom conducted, despite the relevance of protected attributes such as gender
[9]. Works in other fields, such as medical imaging, have already shown
significant disparities in the diagnosis performance across datasets, tasks,
and protected attributes [16]. For example, a significant underdiagnosis was
found in chest X-ray classification algorithms for black females, potentially
resulting in delays in treatment [16]. Moreover, a significant decrease in
diagnosis performance was reported for different genders using chest X-ray
images [17].

Figure 1: Breakdown of subject classes in train ($N$=118) and test ($N$=51)
sub-samples. The population is divided into two gender sub-groups (males and
females) followed by a division into disease-stage groups based on UPDRS III
stage scores and disease duration in months.

In this work, we systematically study the detection ability for the different
genders of a previously developed PD detection model based on rs-EEG and PSD
features [18], where the analysis of gender sub-groups is motivated by their
relevance from a clinical perspective. To the best of our knowledge, this is
the first study attempting to understand the effect of a protected attribute
such as gender in rs-EEG and ML models.

We make the following contributions: (i) a systematic analysis of an ML model
performance [18] trained in a gender-balanced regime for different gender sub-
populations (Fig. 1); (ii) a qualitative analysis of the influence of Unified
Parkinson’s Disease Rating Scale (UPDRS) III score and PD duration on the
exhibited disparities; (iii) an analysis of the contribution of the different
EEG channels and frequency sub-bands in the disparities. We make our code
publicly available on GitHub.

We find significant differences in the detection ability for males and females
at testing time. Further, we find that those differences might be explained by
higher activity in a set of frequency sub-bands of CP1, CP2, CP5, CP6, Fp1,
Fp2, and P3 channels for PD males when compared to PD females and in CP5, FC2,
Fp1, and Fp2 for non-PD males and females. Our findings suggest that different
frequency sub-bands and channels might have a higher expressiveness for PD
detection depending on gender.

##  II Materials and Methods

###  II-A Data

####  II-A1 Study population

We include rs-EEG recordings of 169 subjects (84 PD and 85 non-PD) from four
datasets collected at different research centers in three countries: Colombia
(Medellín) [19], Finland (Turku) [20], and the USA (Iowa City and San Diego)
[21, 22]. PD and non-PD subjects were matched by age in all the datasets.
Furthermore, subjects were matched by gender, level of education, and
cognitive performance in all the datasets except Turku.

TABLE I: Demographic and clinical characteristics (mean$\pm$SD) of PD and non-
PD subjects of each dataset. y=years, m=months.

| Subjects | Age, y | # $\female$ (%) | Duration, m | UPDRS III
---|---|---|---|---|---
Iowa City | PD ($n$=14) | 70.5 (8.6) | 8 (57.2) | 66.9 (38.7) | 13.4 (6.6)
non-PD ($n$=14) | 70.5 (8.6) | 8 (57.2) | - | -
Medellín | PD ($n$=36) | 63.5 (8) | 12 (33) | 61.6 (37.4) | 30.8 (12)
non-PD ($n$=36) | 63.3 (6.2) | 12 (33) | - | -
San Diego | PD ($n$=15) | 63.3 (8.2) | 8 (53.3) | 53.6 (40.5) | 32.7 (10.4)
non-PD ($n$=16) | 63.5 (9.7) | 9 (56.2) | - | -
Turku | PD ($n$=19) | 69.6 (7.7) | 11 (57.9) | 80.5 (63) | 27.6 (16.9)
non-PD ($n$=19) | 67.5 (6.4) | 12 (63.2) | - | -

We present each dataset’s available demographic and clinical characteristics
of PD and non-PD subjects in Table I as mean and standard deviation (SD).
Worthy of note that there is no reported disease duration for two female
subjects from the Turku dataset.

####  II-A2 Acquisition

The rs-EEG recordings were acquired with the eyes closed for Medellín, San
Diego, and Turku subjects. In contrast, the subjects from Iowa City had their
eyes open. For PD subjects from Iowa City, Medellín, and San Diego, the rs-EEG
recordings were from the ON phase of levodopa sessions, whilst, for Turku,
only 6 PD subjects participated in EEG acquisition in the ON phase, while the
remaining were in the OFF phase. All the datasets had 29 channels in common
that were included in the analysis: AF3, AF4, C3, C4, CP1, CP2, CP5, CP6, Cz,
F3, F4, F7, F8, FC1, FC2, FC5, FC6, Fp1, Fp2, Fz, O1, O2, Oz, P3, P4, P7, P8,
T7, and T8 (Fig. 2).

Figure 2: Common EEG channels included in the analysis.

####  II-A3 Pre-processing

We applied a robust average re-referencing and adaptive line-noise correlation
combined with detecting and interpolating noisy channels. The recordings were
high-pass filtered at 1 Hz and decomposed using wavelet-based independent
component (ICA) analysis to eliminate artifacts. We applied a low-pass filter
at 30 Hz followed by segmentation of the signals into epochs of five-second
length. Finally, we applied an additional rejection of artifact-compromised
epochs. The common number of remaining epochs across all subjects within each
dataset was included in the analysis.

TABLE II: Analysis of logistic regression (LR) model’s performance (mean [95$\%$ CI]) for different gender sub-groups. Each result is presented in terms of global and center-wise performances, which are presented together with the number of subjects in their corresponding test sub-samples. f=females and m=males. |  | Accuracy, $\%$ | Recall, $\%$ | Specificity, $\%$ | Precision, $\%$ |  $F_{1}$, $\%$ | AUC
---|---|---|---|---|---|---|---
mixed | global (19f, 32m) | 73.2 [66.5 79.9] | 68.9 [59.8 78.0] | 77.4 [67.3 87.5] | 75.0 [66.2 83.8] | 71.6 [64.4 78.7] | 0.73 [0.66 0.80]
Iowa City (3f, 5m) | 62.7 [52.2 73.3] | 25.5 [4.5 46.5] | 100 [100 100] | 78.0 [26.6 129.4] | 37.6 [9.4 65.8] | 0.63 [0.52 0.73]
Medellín (5f, 17m) | 68.2 [57.2 79.2] | 72.6 [57.2 88.1] | 63.8 [46.5 81.2] | 67.5 [55.7 79.2] | 69.4 [58.5 80.3] | 0.68 [0.57 0.79]
San Diego (3f, 6m) | 77.3 [66.1 88.5] | 75.0 [75.0 75.0] | 79.2 [59.0 99.4] | 77.3 [57.8 96.9] | 75.4 [66.0 84.7] | 0.77 [0.67 0.87]
Turku (8f, 4m) | 84.2 [72.1 96.4] | 81.2 [62.5 99.8] | 87.3 [73.3 101] | 87.2 [73.6 101] | 83.3 [69.7 96.9] | 0.84 [0.72 0.96]
males | global (32m) | 80.5 [72.0 89.0] | 88.4 [78.5 98.2] | 74.3 [61.6 87.1] | 73.6 [63.2 84.0] | 80.0 [71.8 88.1] | 0.81 [0.73 0.90]
Iowa City (5m) | 80.6 [63.6 97.6] | 51.5 [9.0 94.0] | 100 [100 100] | 78.0 [26.6 130] | 60.3 [17.0 104] | 0.76 [0.54 0.97]
Medellín (17m) | 70.9 [57.8 84.1] | 87.0 [71.9 102] | 59.7 [39.5 79.9] | 61.5 [48.4 74.5] | 71.4 [59.8 83.0] | 0.73 [0.61 0.86]
San Diego (6m) | 83.3 [66.8 99.9] | 100 [100 100] | 66.7 [33.6 99.8] | 78.1 [58.5 97.7] | 86.8 [74.6 99.1] | 0.83 [0.67 1.0]
Turku (4m) | 100 [100 100] | 100 [100 100] | 100 [100 100] | 100 [100 100] | 100 [100 100] | 1.0 [1.0 1.0]
females | global (19f) | 63.7 [54.1 73.3] | 46.1 [32.0 60.2] | 88.0 [74.8 101] | 85.0 [68.9 101] | 58.9 [45.6 72.3] | 0.67 [0.58 0.76]
Iowa City (3f) | 33.3 [33.3 33.3] | 0.0 [0.0 0.0] | 100 [100 100] | 0.0 [0.0 0.0] | 0.0 [0.0 0.0] | 0.50 [0.50 0.50]
Medellín (5f) | 60.8 [38.1 83.5] | 51.0 [22.6 79.4] | 100 [100 100] | 94.0 [64.5 124] | 64.1 [35.4 92.8] | 0.76 [0.61 0.90]
San Diego (3f) | 66.7 [66.7 66.7] | 0.0 [0.0 0.0] | 100 [100 100] | 0.0 [0.0 0.0] | 0.0 [0.0 0.0] | 0.50 [0.50 0.50]
Turku (8f) | 74.5 [57.0 92.0] | 74.2 [48.1 100] | 74.8 [49.3 100] | 76.9 [55.5 98.2] | 73.7 [54.3 93.1] | 0.74 [0.57 0.92]

###  II-B Parkinson’s Disease (PD) Detection Model

A detailed description of the steps followed to obtain the PD detection model
used in this work can be found in [18]. For the sake of simplicity, we provide
a brief description to give a general idea of the model development and
design:

####  II-B1 Feature Extraction and Harmonization

For each epoch, relative PSD values were computed by dividing PSD values
within a given frequency sub-band by the total signal power. In total, six
sub-bands were applied: $\delta$ [1$-$4) Hz, $\theta$ [4$-$8) Hz (additionally
divided into slow-$\theta$ [4$-$5.5) Hz and fast-$\theta$ [5.5$-$8) Hz,
$\alpha$ [8$-$13) Hz, and $\beta$ [13$-$30) Hz. Besides, $\alpha$/$\theta$ PSD
ratio is obtained. The resulting PSD values of all the epochs were further
averaged, log-transformed, and harmonized using the modified ComBat
harmonization model, producing 203 features (7 PSD values$\times$29 channels)
per subject.

####  II-B2 Classification Framework

The overall sample was randomly divided into train and test sub-samples with a
70/30$\%$ split stratified by center and diagnosis (Fig. 1). The best-
performing model was selected from several ML classifiers based on the
validation accuracy of a $subject$-wise binary classification of PD or non-PD
obtained by a nested 5-fold cross-validation (CV) loop. In addition, we
performed feature selection by means of a univariate $k$-feature selection
based on ANOVA $F$-value. Model evaluation, hyperparameter space, and optimal
number of features were evaluated in terms of average and 95$\%$ confidence
intervals (CIs) for accuracy, recall, specificity, precision, $F_{1}$ score,
and area under the receiver operating characteristic (ROC) curve (AUC) for the
multi-center dataset.

###  II-C Fairness Analysis

####  II-C1 Gender sub-group performance

We analyze the PD detection performance for male and female gender sub-groups
(Fig. 1). At this point, we would like to emphasize that developing a
detection model is not the primary concern of the study, nor do we claim any
contribution in that regard.

We start by replicating the original train and test sub-samples obtained by a
70/30$\%$ stratified split by center and diagnosis. We note that the original
dataset has an equal presence of the gender sub-groups (Fig. 1), resulting in
a balanced representation of genders in both train and test sub-samples. In
the remainder of the article, we focus on the test sub-sample. We perform the
gender sub-group analysis by means of a stratified bootstrapping approach with
n=100 replicates with replacement.

Debiasing sub-group estimation via bootstrapping: Limitations in some of the
existing sub-group analyses include random splitting of the original dataset
that was already presenting bias in the form of a higher prevalence of some of
the sub-groups. We explore the use of gender, diagnosis, and center as
stratification parameters to construct a test sub-group representative of the
population of interest via bootstrapping. We perform n=100 replicates with
replacement and analyze the results for male and female sub-groups in terms of
accuracy, recall, specificity, precision, $F_{1}$ score, and AUC.

We present the results for each gender sub-group and the initial splitting
results [18]. Further, we present the rounded confusion matrix for the
original classifier, male sub-group, and female sub-group averaged over the
n=100 bootstrap replicates.

(a) both genders

(b) males

(c) females

Figure 3: Confusion matrices demonstrating the test accuracy of the trained
logistic regression (LR) model to classify PD and non-PD subjects using mixed
3(a), only males 3(b), and only females 3(c) test sub-samples.

(a) males

(b) females

Figure 4: Bar charts representing the number of misclassified PD divided into
different groups based on UPDRS III scores (first row) and disease duration
(second row). Misclassified non-PD subjects are also presented.

####  II-C2 Qualitative sub-group analysis of other attributes

We seek to understand whether misclassifications in gender sub-groups are
correlated with differences in UPDRS III stage scores and disease duration.
Since some of the included research centers use different versions of the
UPDRS III stage, we apply a simple conversion rule [23] to obtain Movement
Disorder Society UPDRS (MDS-UPDRS) III from UPDRS III stage scores. Further,
for the sake of simplicity, we trichotomize subjects based on the scores:
$onset$ stage if score $\leqslant$ 20 [24], $mild$ stage if 20 $<$ score $<$
35, and $severe$ stage if score $\geqslant$ 35\. Similarly, subjects are
trichotomized based on the PD duration (in months): $onset$ stage if duration
$\leqslant$ 50 months, $mild$ stage if 50 $<$ duration $<$ 100 months, and
$severe$ stage if duration $\geqslant$ 100 months.

####  II-C3 rs-EEG feature relevance for gender sub-groups

In the last step of our fairness analysis, we focus on explaining the gender
sub-group differences through the expressiveness in the detection framework of
the PSD features extracted from the rs-EEG for each sub-group. With that in
mind, we re-train the original model with a data sub-set containing only
males, and we repeat the same process for females, obtaining two models
specialized in each gender sub-group. The proportions of the training and
testing datasets are kept at 70/30$\%$, and we apply the original model
development stratification strategy: center and diagnosis. We fix the male and
female model hyperparameters and focus, exclusively, on the feature selection
process by means of ANOVA F-value. We obtain the $k$-highest scoring features
for the gender-specialized models with the best validation results through a
5-fold CV process.

At this point, given our original biased model and our gender-specific models
that keep all the original model design choices except for the feature
selection process, we hypothesize that there are significant differences in
rs-EEG features from the frontal and parietal regions of the brain [25, 26]
and that the three classifiers commonly select these features as high-scoring
features for the PD classification. Further, we also hypothesize that the
features that present differences in average values between male PD, female
PD, male non-PD, and female non-PD can explain the differences in sub-group
performance in the original classifier.

To corroborate our hypotheses, we perform $t$-test followed by a false
discovery rate (FDR) correction for male and female sub-groups of the common
$k$-highest scoring features from the frontal and parietal regions of the
brain. Before accounting for corrections, we consider p $<$ 0.05 to be
significant. We apply FDR at the 0.05 significance level. Finally, we analyze
the rs-EEG features with respect to the scalp position (channel position) and
PSD sub-bands. We present the results in terms of mean and 95$\%$ CI.

##  III Results and Discussion

As depicted in Table II, we present the bootstrapped sub-group PD detection
analysis in terms of the original classifier with matched genders and gender
as a protected attribute, only male and only female sub-group subjects. We
present the results for all the datasets together (global) and the subjects of
each dataset separately. Overall, there is an evident degradation in PD
detection performance for females compared to males, reflected by a lower
accuracy, recall, $F_{1}$ score, and AUC. The same trend is observed for all
the individual contributing centers. Confusion matrices (Fig. 3) reveal that
the model’s PD detection for the male sub-group results in a high type I error
(28$\%$), two times as high as the type I error in the female sub-sample
(13$\%$). In comparison, detecting PD in the female sub-group results in a
high type II error (55$\%$), almost four times as high as the type II error in
the male sub-group (15$\%$).

The qualitative analysis presented in Fig. 4 reveals that for males, the
misclassified PD subjects belong to either the mild stage in terms of disease
duration or onset stage based on UPDRS III scores. All the subjects from the
severe stage groups are correctly detected by the model. This might indicate a
lower model’s detection of early-mild stages of PD for males. On the other
hand, the misclassified PD female subjects have either the onset or mild
stages based on UPDRS III scores and onset, mild or severe stages based on the
disease duration. Regardless of the disease stage, the model presents a clear
underperformance for PD detection in female subjects when compared to males.

The best validation results are achieved with k=188 and k=181 highest scoring
features for the male and female gender sub-group models, respectively. Out of
these features, 169 are selected as common features for both models, and
channels from both the frontal and parietal brain regions are further analyzed
to limit pairwise comparisons and on the basis of previous research results.
Table III presents mean and 95$\%$ CI values of the significantly different
(before FDR correction) common PSD features with their corresponding channel
and frequency sub-band for PD and non-PD samples for each gender sub-group. We
note that after FDR correction, no significance is found in the differences.

Given the log-transformation of our values and the amplitude range of the
original rs-EEG recordings, higher mean absolute values might be indicative of
lower activity for PD and non-PD in the channels and sub-bands whilst smaller
mean absolute values might be indicative of higher activation in the channel
and sub-band under analysis. In particular, we can observe that overall, PD
males have higher activity when compared to PD females in the parietal and
frontal channels and resulting sub-bands of the analysis (Table III). In a
similar way, non-PD males show a higher activity in selected channels and sub-
bands when compared to non-PD females (Table III), but not by a large margin.

TABLE III: Mean$\pm$SD values of the significantly different common features with their respective channels and frequency sub-bands for male and female subjects. $p$-values are presented non-corrected. | Channel | Sub-band | Gender | Mean$\pm$SD | 95$\%$ CI
---|---|---|---|---|---
PD | CP1 | $\theta$ | male | -0.77$\pm$0.13 | [-0.93 -0.61]
female | -0.96$\pm$0.22 | [-1.24 -0.69]
fast-$\theta$ | male | -0.91$\pm$0.17 | [-1.12 -0.71]
female | -1.13$\pm$0.23 | [-1.41 -0.85]
$\beta$ | male | -0.67$\pm$0.25 | [-0.97 -0.36]
female | -0.45$\pm$0.19 | [-0.68 -0.22]
$\alpha/\theta$ | male | 0.26$\pm$0.25 | [-0.06 0.56]
female | 0.47$\pm$0.24 | [0.17 0.76]
CP2 | $\theta$ | male | -0.88$\pm$0.27 | [-1.22 -0.55]
female | -1.00$\pm$0.25 | [-1.31 -0.70]
fast-$\theta$ | male | -1.00$\pm$0.31 | [-1.38 -0.62]
female | -1.14$\pm$0.27 | [-1.47 -0.81]
$\beta$ | male | -0.62$\pm$0.30 | [-0.99 -0.24]
female | -0.42$\pm$0.15 | [-0.60 -0.23]
$\alpha/\theta$ | male | 0.27$\pm$0.26 | [-0.05 0.59]
female | 0.49$\pm$0.22 | [0.22 0.75]
CP5 | $\alpha/\theta$ | male | -0.72$\pm$0.16 | [-0.91 -0.53]
female | -0.89$\pm$0.30 | [-1.27 -0.52]
CP6 | $\alpha/\theta$ | male | -0.90$\pm$0.22 | [-1.17 -0.64]
female | -1.00$\pm$0.26 | [-1.35 -0.71]
Fp1 | $\alpha/\theta$ | male | -0.81$\pm$0.25 | [-1.11 -0.50]
female | -0.62$\pm$0.24 | [-0.93 -0.32]
Fp2 | $\alpha$ | male | -0.84$\pm$0.16 | [-1.05 -0.63]
female | -0.99$\pm$0.29 | [-1.35 -0.63]
$\alpha/\theta$ | male | 0.21$\pm$0.32 | [-0.19 0.61]
female | 0.43$\pm$0.37 | [0.03 0.89]
P3 | $\theta$ | male | -0.56$\pm$0.29 | [-0.92 -0.21]
female | -0.46$\pm$0.16 | [-0.66 -0.26]
slow-$\theta$ | male | 0.30$\pm$0.30 | [-0.07 0.66]
female | 0.45$\pm$0.23 | [0.17 0.73]
fast-$\theta$ | male | -0.79$\pm$0.16 | [-0.99 -0.59]
female | -0.92$\pm$0.20 | [-1.17 -0.67]
$\beta$ | male | -0.91$\pm$0.18 | [-1.13 -0.69]
female | -1.04$\pm$0.24 | [-1.33 -0.75]
non-PD | CP5 | $\delta$ | male | -1.11$\pm$0.30 | [-1.48 -0.74]
female | -1.09$\pm$0.15 | [-1.28 -0.90]
Fp1 | $\beta$ | male | -1.10$\pm$0.26 | [-1.42 -0.78]
female | -1.12$\pm$0.17 | [-1.33 -0.92]
Fp2 | slow-$\theta$ | male | -0.97$\pm$0.23 | [-1.26 -0.69]
female | -0.98$\pm$0.12 | [-1.12 -0.84]

##  IV Conclusion

Different motor and non-motor profiles of PD and non-PD in men and women might
induce different rs-EEG profiles, which are harder to detect in women due to
their overall lower activity in parietal and frontal channels. Our findings
corroborate the exacerbation of gender disparities found in other studies. As
depicted by our systematic analysis, measures to ensure fair ML development
for PD detection are required. The findings can help to provide a more
personalized diagnosis using rs-EEG and ML by accounting for gender
differences in rs-EEG parietal and frontal activity. Limitations of the study
include the use of retrospective data, a limited amount of patients, and
statistical power.

## Acknowledgments

We would like to thank all the researchers and staff at the University of
California San Diego, the University of Turku and the Turku University
Hospital, the University of Iowa, and the University of Antioquia. We
especially thank Dr. A. P. Rockhill, Dr. H. Railo, and Dr. N. S. Narayanan for
making their datasets publicly available.

## References

  * [1] T. Davenport and R. Kalakota “The potential for artificial intelligence in healthcare” In _Future healthcare journal_ 6.2 Royal College of Physicians, 2019, pp. 94
  * [2] D.S. Char, N.H. Shah and D. Magnus “Implementing machine learning in health care—addressing ethical challenges” In _The New England journal of medicine_ 378.11 NIH Public Access, 2018, pp. 981
  * [3] A. Fernandez-Quilez “Deep learning in radiology: ethics of data and on the value of algorithm transparency, interpretability and explainability” In _AI and Ethics_ Springer, 2022, pp. 1–9
  * [4] L.. Kalia and A.. Lang “Parkinson’s disease” In _The Lancet_ 386.9996 Elsevier, 2015, pp. 896–912
  * [5] Claudio Babiloni et al. “International Federation of Clinical Neurophysiology (IFCN)–EEG research workgroup: Recommendations on frequency and topographic analysis of resting state EEG rhythms. Part 1: Applications in clinical research studies” In _Clinical Neurophysiology_ 131.1 Elsevier, 2020, pp. 285–307
  * [6] MY Neufeld et al. “EEG frequency analysis in demented and nondemented parkinsonian patients” In _Dementia and Geriatric Cognitive Disorders_ 5.1 Karger Publishers, 1994, pp. 23–28
  * [7] Laurent Pezard, Robert Jech and Evžen Růžička “Investigation of non-linear properties of multichannel EEG in the early stages of Parkinson’s disease” In _Clinical Neurophysiology_ 112.1 Elsevier, 2001, pp. 38–45
  * [8] S. Cerri, L. Mus and F. Blandini “Parkinson’s disease in women and men: what’s the difference?” In _Journal of Parkinson’s disease_ 9.3 IOS Press, 2019, pp. 501–515
  * [9] M. Baldereschi et al. “Parkinson’s disease and parkinsonism in a longitudinal study: two-fold higher incidence in men” In _Neurology_ 55.9 AAN Enterprises, 2000, pp. 1358–1363
  * [10] N. Dahodwala et al. “Sex disparities in access to caregiving in Parkinson disease” In _Neurology_ 90.1 AAN Enterprises, 2018, pp. e48–e54
  * [11] I.. Miller and A. Cronin-Golomb “Gender differences in Parkinson’s disease: clinical characteristics and cognition” In _Movement disorders_ 25.16 Wiley Online Library, 2010, pp. 2695–2703
  * [12] J. Oltra et al. “Sex differences in brain and cognition in de novo Parkinson’s disease” In _Frontiers in Aging Neuroscience_ 13 Frontiers, 2022, pp. 943
  * [13] T.. Reekes et al. “Sex specific cognitive differences in Parkinson disease” In _npj Parkinson’s Disease_ 6.1 Nature Publishing Group UK London, 2020, pp. 7
  * [14] R. Yuvaraj, U Rajendra Acharya and Y. Hagiwara “A novel Parkinson’s Disease Diagnosis Index using higher-order spectra features in EEG signals” In _Neural Computing and Applications_ 30 Springer, 2018, pp. 1225–1235
  * [15] S. Vanneste, J.-J. Song and D. De Ridder “Thalamocortical dysrhythmia detected by machine learning” In _Nature communications_ 9.1 Nature Publishing Group UK London, 2018, pp. 1103
  * [16] L. Seyyed-Kalantari et al. “Underdiagnosis bias of artificial intelligence algorithms applied to chest radiographs in under-served patient populations” In _Nature medicine_ 27.12 Nature Publishing Group US New York, 2021, pp. 2176–2182
  * [17] A.. Larrazabal et al. “Gender imbalance in medical imaging datasets produces biased classifiers for computer-aided diagnosis” In _Proceedings of the National Academy of Sciences_ 117.23 National Acad Sciences, 2020, pp. 12592–12594
  * [18] A. Kurbatskaya et al. “Machine Learning-Based Detection of Parkinson’s Disease From Resting-State EEG: A Multi-Center Study” In _arXiv_ , 2023
  * [19] J.. Carmona Arroyave et al. “Resting functional connectivity and mild cognitive impairment in Parkinson’s disease. An electroencephalogram study” In _Future Neurology_ 14.2, 2019, pp. FNL18
  * [20] H. Railo et al. “Resting state EEG as a biomarker of Parkinson’s disease: Influence of measurement conditions” In _bioRxiv_ Cold Spring Harbor Laboratory, 2020
  * [21] M.. Anjum et al. “Linear predictive coding distinguishes spectral EEG features of Parkinson’s disease” In _Parkinsonism & related disorders_ 79 Elsevier, 2020, pp. 79–85
  * [22] A.. Rockhill et al. “UC San Diego Resting State EEG Data from Patients with Parkinson’s Disease” In _OpenNeuro_ , 2021
  * [23] J.. Hentz et al. “Simplified conversion method for unified Parkinson’s disease rating scale motor examinations” In _Movement Disorders_ 30.14 Wiley Online Library, 2015, pp. 1967–1970
  * [24] S.. Holden, T. Finseth, S.. Sillau and B.. Berman “Progression of MDS-UPDRS scores over five years in de novo Parkinson disease from the Parkinson’s progression markers initiative cohort” In _Movement disorders clinical practice_ 5.1 Wiley Online Library, 2018, pp. 47–53
  * [25] H. Bin Yoo et al. “The functional alterations in top-down attention streams of Parkinson’s disease measured by EEG” In _Scientific reports_ 8.1 Springer, 2018, pp. 1–11
  * [26] H. Teramoto et al. “Relation between resting state front-parietal EEG coherence and executive function in Parkinson’s disease” In _BioMed research international_ 2016 Hindawi, 2016