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7 June 2023 Autoencoders with deformable convolutions for latent representation of EEG spectrograms in classification tasks
Maria Zubrikhina, Dmitrii Masnyi, Rifat Hamoudi, Hamid Alhaj, Bashar Issa, Almira Kustubaeva, Altyngul Kamzanova, Manzura Zholdassova, Alexander Bernstein, Evgeny Burnaev, Alexey Artemov, Maxim Sharaev
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Proceedings Volume 12701, Fifteenth International Conference on Machine Vision (ICMV 2022); 127011D (2023) https://doi.org/10.1117/12.2679806
Event: Fifteenth International Conference on Machine Vision (ICMV 2022), 2022, Rome, Italy
Abstract
Electroencephalogram (EEG) is a set of time series each of which can be represented as a 2D image (spectrogram), so that EEG recording can be mapped to the C-dimensional image (where C denotes the number of channels in the image and equals to the number of electrodes in EEG montage). In this paper, a novel approach for automated feature extraction from spectrogram representation is proposed. The method involves the usage of autoencoder models based on 3-dimensional convolution layers and 2-dimensional deformable convolution layers. Features, extracted by autoencoders, can be used to classify patients with Major Depressive Disorder (MDD) from healthy controls based on resting-state EEG. The proposed approach outperforms baseline ML models trained on spectral features extracted manually.
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Maria Zubrikhina, Dmitrii Masnyi, Rifat Hamoudi, Hamid Alhaj, Bashar Issa, Almira Kustubaeva, Altyngul Kamzanova, Manzura Zholdassova, Alexander Bernstein, Evgeny Burnaev, Alexey Artemov, and Maxim Sharaev "Autoencoders with deformable convolutions for latent representation of EEG spectrograms in classification tasks", Proc. SPIE 12701, Fifteenth International Conference on Machine Vision (ICMV 2022), 127011D (7 June 2023); https://doi.org/10.1117/12.2679806
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KEYWORDS
Electroencephalography
Data modeling
Deformation
Convolution
Feature extraction
3D modeling
Machine learning
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