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: | Mariam Khayretdinova, Ilya Zakharov, Polina Pshonkovskaya, Timothy Adamovich, Andrey Kiryasov, Andrey Zhdanov, Alexey Shovkun |
---|---|
Format: | Article |
Language: | English |
Published: |
Elsevier
2024-01-01
|
Series: | NeuroImage |
Subjects: | |
Online Access: | http://www.sciencedirect.com/science/article/pii/S1053811923006456 |
Similar Items
-
Predicting age from resting-state scalp EEG signals with deep convolutional neural networks on TD-brain dataset
by: Mariam Khayretdinova, et al.
Published: (2022-12-01) -
Clinical data-driven approach to identifying COVID-19 and influenza from a gradient-boosting model
by: Duong Thi Kim Chi, et al.
Published: (2023-12-01) -
EEG-Based Emotion Classification Using Stacking Ensemble Approach
by: Subhajit Chatterjee, et al.
Published: (2022-11-01) -
Financial Technical Indicator and Algorithmic Trading Strategy Based on Machine Learning and Alternative Data
by: Andrea Frattini, et al.
Published: (2022-11-01) -
Gradient Boosting Machine, Random Forest dan Light GBM untuk Klasifikasi Kacang Kering
by: Indrawata Wardhana, et al.
Published: (2022-02-01)