Learning temporal-frequency features of physionet EEG signals using deep convolutional neural network

Maryam Sorkhi, Mohammad Reza Jahed-Motlagh, Behrouz Minaei-Bidgoli and Mohammad Reza Daliri
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Maryam Sorkhi: School of Computer Engineering, Iran University of Science and Technology, Tehran, Iran
Mohammad Reza Jahed-Motlagh: School of Computer Engineering, Iran University of Science and Technology, Tehran, Iran†School of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran
Behrouz Minaei-Bidgoli: School of Computer Engineering, Iran University of Science and Technology, Tehran, Iran
Mohammad Reza Daliri: ��School of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran

International Journal of Modern Physics C (IJMPC), 2023, vol. 34, issue 04, 1-20

Abstract: Since EEG signals encode an individual’s intent of executing an action, scientists have extensively focused on this topic. Motor Imagery (MI) signals have been used by researchers to assistance disabled persons, for autonomous driving and even control devices such as wheelchairs. Therefore, accurate decoding of these signals is essential to develop a Brain–Computer interface (BCI) systems. Due to dynamic nature, low signal-to-noise ratio and complexity of EEG signals, EEG decoding is not simple task. Extracting temporal and spatial features from EEG is accessible via Convolution neural network (CNN). However, enhanced CNN models are required to learn the dynamic correlations existing in MI signals. It is found that good features are extracted via CNN in both deep and shallow models, which indicate that various levels related features can be mined. In this case, spatial patterns from multi-scaled data in different frequency bands are learnt at first and then the temporal and frequency band information from projected signals is extracted. Here, to make use of neural activity phenomena, the feature extraction process employed is based on Multi-scale FBCSP (MSFBCSP). In CNN, the envelope of each spatially filtered signal is extracted in time dimension by performing Hilbert transform. However, to access common morphologies, the convolutional operation across time is performed first and then another convolution layer across channels in the frequency band is used to represent the carried information in a more compact form. Moreover, Bayesian approach is used for mapping hyperparameters to a probability of score on the objective function. The prominent feature of the proposed network is the high capacity of preserving and utilizing the information encoded in frequency bands. Our proposed method significantly improves the efficiency of current classification method in specific dataset of the physionet. According to empirical evaluations, strong robustness and high decoding classification are two distinctive characteristics of our proposed work.

Keywords: Brain computer interface; motor imagery; deep learning; convolution neural network (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1142/S012918312350047X

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