Prediction of brain sex from EEG: using large-scale heterogeneous dataset for developing a highly accurate and interpretable ML model
Abstrac: This study presents a comprehensive examination of sex-related differences in resting-state electroencephalogram (EEG) data, leveraging two different types of machine learning models to predict an individual's sex. We utilized data from the Two Decades-Brainclinics Research Archive for...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2024-01-01
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| Series: | NeuroImage |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1053811923006456 |
| _version_ | 1797359958726017024 |
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| author | Mariam Khayretdinova Ilya Zakharov Polina Pshonkovskaya Timothy Adamovich Andrey Kiryasov Andrey Zhdanov Alexey Shovkun |
| author_facet | Mariam Khayretdinova Ilya Zakharov Polina Pshonkovskaya Timothy Adamovich Andrey Kiryasov Andrey Zhdanov Alexey Shovkun |
| author_sort | Mariam Khayretdinova |
| collection | DOAJ |
| description | Abstrac: This study presents a comprehensive examination of sex-related differences in resting-state electroencephalogram (EEG) data, leveraging two different types of machine learning models to predict an individual's sex. We utilized data from the Two Decades-Brainclinics Research Archive for Insights in Neurophysiology (TDBRAIN) EEG study, affirming that gender prediction can be attained with noteworthy accuracy. The best performing model achieved an accuracy of 85% and an ROC AUC of 89%, surpassing all prior benchmarks set using EEG data and rivaling the top-tier results derived from fMRI studies. A comparative analysis of LightGBM and Deep Convolutional Neural Network (DCNN) models revealed DCNN's superior performance, attributed to its ability to learn complex spatial-temporal patterns in the EEG data and handle large volumes of data effectively. Despite this, interpretability remained a challenge for the DCNN model. The LightGBM interpretability analysis revealed that the most important EEG features for accurate sex prediction were related to left fronto-central and parietal EEG connectivity. We also showed the role of both low (delta and theta) and high (beta and gamma) activity in the accurate sex prediction. These results, however, have to be approached with caution, because it was obtained from a dataset comprised largely of participants with various mental health conditions, which limits the generalizability of the results and necessitates further validation in future studies. . Overall, the study illuminates the potential of interpretable machine learning for sex prediction, alongside highlighting the importance of considering individual differences in prediction sex from brain activity. |
| first_indexed | 2024-03-08T15:31:27Z |
| format | Article |
| id | doaj.art-3ac32331aecb44a1a6adf83f68fce090 |
| institution | Directory Open Access Journal |
| issn | 1095-9572 |
| language | English |
| last_indexed | 2024-03-08T15:31:27Z |
| publishDate | 2024-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | NeuroImage |
| spelling | doaj.art-3ac32331aecb44a1a6adf83f68fce0902024-01-10T04:35:11ZengElsevierNeuroImage1095-95722024-01-01285120495Prediction of brain sex from EEG: using large-scale heterogeneous dataset for developing a highly accurate and interpretable ML modelMariam Khayretdinova0Ilya Zakharov1Polina Pshonkovskaya2Timothy Adamovich3Andrey Kiryasov4Andrey Zhdanov5Alexey Shovkun6Corresponding authors.; Brainify.AI, Dover, Delaware, United StatesCorresponding authors.; Brainify.AI, Dover, Delaware, United StatesBrainify.AI, Dover, Delaware, United StatesBrainify.AI, Dover, Delaware, United StatesBrainify.AI, Dover, Delaware, United StatesBrainify.AI, Dover, Delaware, United StatesBrainify.AI, Dover, Delaware, United StatesAbstrac: This study presents a comprehensive examination of sex-related differences in resting-state electroencephalogram (EEG) data, leveraging two different types of machine learning models to predict an individual's sex. We utilized data from the Two Decades-Brainclinics Research Archive for Insights in Neurophysiology (TDBRAIN) EEG study, affirming that gender prediction can be attained with noteworthy accuracy. The best performing model achieved an accuracy of 85% and an ROC AUC of 89%, surpassing all prior benchmarks set using EEG data and rivaling the top-tier results derived from fMRI studies. A comparative analysis of LightGBM and Deep Convolutional Neural Network (DCNN) models revealed DCNN's superior performance, attributed to its ability to learn complex spatial-temporal patterns in the EEG data and handle large volumes of data effectively. Despite this, interpretability remained a challenge for the DCNN model. The LightGBM interpretability analysis revealed that the most important EEG features for accurate sex prediction were related to left fronto-central and parietal EEG connectivity. We also showed the role of both low (delta and theta) and high (beta and gamma) activity in the accurate sex prediction. These results, however, have to be approached with caution, because it was obtained from a dataset comprised largely of participants with various mental health conditions, which limits the generalizability of the results and necessitates further validation in future studies. . Overall, the study illuminates the potential of interpretable machine learning for sex prediction, alongside highlighting the importance of considering individual differences in prediction sex from brain activity.http://www.sciencedirect.com/science/article/pii/S1053811923006456Resting state EEGSex-related brain differencesDCNNLightGBMFeature importance analysis |
| spellingShingle | Mariam Khayretdinova Ilya Zakharov Polina Pshonkovskaya Timothy Adamovich Andrey Kiryasov Andrey Zhdanov Alexey Shovkun Prediction of brain sex from EEG: using large-scale heterogeneous dataset for developing a highly accurate and interpretable ML model NeuroImage Resting state EEG Sex-related brain differences DCNN LightGBM Feature importance analysis |
| title | Prediction of brain sex from EEG: using large-scale heterogeneous dataset for developing a highly accurate and interpretable ML model |
| title_full | Prediction of brain sex from EEG: using large-scale heterogeneous dataset for developing a highly accurate and interpretable ML model |
| title_fullStr | Prediction of brain sex from EEG: using large-scale heterogeneous dataset for developing a highly accurate and interpretable ML model |
| title_full_unstemmed | Prediction of brain sex from EEG: using large-scale heterogeneous dataset for developing a highly accurate and interpretable ML model |
| title_short | Prediction of brain sex from EEG: using large-scale heterogeneous dataset for developing a highly accurate and interpretable ML model |
| title_sort | prediction of brain sex from eeg using large scale heterogeneous dataset for developing a highly accurate and interpretable ml model |
| topic | Resting state EEG Sex-related brain differences DCNN LightGBM Feature importance analysis |
| url | http://www.sciencedirect.com/science/article/pii/S1053811923006456 |
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