| Author | Search for: Darvishi-Bayazi, Mohammad-Javad; Search for: Ghaemi, Mohammad Sajjad1; Search for: Faubert, Jocelyn; Search for: Rish, Irina |
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| Affiliation | - National Research Council of Canada. Digital Technologies
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| Format | Article |
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| Conference | 1st Workshop on Machine Learning for Cognitive and Mental Health, ML4CMH 2024, February 26, 2024, Vancouver, BC |
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| Subject | artificial neural network; EEG; machine learning; mental health; robustness; sex prediction; transfer learning |
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| Abstract | Previous studies have shown that machine learning can predict biological sex from EEG data with high accuracy. However, the validity and generalizability of these findings across different datasets and tasks still need to be clarified. In this paper, we investigated the robustness and transferability of sex-related patterns in EEG data using a Convolutional neural network (CNN) trained on several corpora of EEG recordings ranging from 221 to 12, 000 participants from healthy and diseased subjects. We evaluated the CNN on datasets from various sources and groups, with varying degrees of shift in their distributions. We found that CNNs can detect sex from EEG data accurately on datasets without fine-tuning or adaptation when the shift is low. However, performance drops where the shift is drastic. These results suggest that sex-related patterns in EEG data are robust and transferable across diverse datasets and relevant tasks. We discuss the implications of these findings for EEG analysis, machine learning applications, and best practices to avoid sex biases that enhance personalized mental health interventions. |
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| Publication date | 2024-02-26 |
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| Publisher | CEUR-WS |
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| Language | English |
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| Peer reviewed | Yes |
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| Export citation | Export as RIS |
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| Report a correction | Report a correction (opens in a new tab) |
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| Record identifier | 07d43c16-e72a-4982-beed-663e3f226fa5 |
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| Record created | 2024-04-04 |
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| Record modified | 2024-04-04 |
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