Automated Motor Imagery Detection Through EEG Analysis and Deep Learning Models for Brain-Computer Interface Applications

Abstract

The classification of motor imagery holds significant importance within brain-computer interface (BCI) research as it allows for the identification of a person's intention, such as controlling a prosthesis. Motor imagery involves the brain's dynamic activities, commonly captured using electroencephalography (EEG) to record nonstationary time series with low signal-to-noise ratios. While various methods exist for extracting features from EEG signals, the application of deep learning techniques to enhance the representation of EEG features for improved motor imagery classification performance has been relatively unexplored. This research introduces a new deep learning approach based on two-dimensional CNNs with different architectures. Specifically, time-frequency domain representations of EEGs obtained by the wavelet transform method with different mother wavelets (Mexicanhat, Cmor, and Cgaus). The BCI competition IV-2a dataset held in 2008 was utilized for testing the proposed deep learning approaches. Several experiments were conducted and the results showed that the proposed method achieved better performance than some state-ofthe-art methods. The findings of this study showed that the architecture of CNN and specifically the number of convolution layers in this deep learning network has a significant effect on the classification performance of motor imagery brain data. In addition, the mother wavelet in the wavelet transform is very important in the classification performance of motor imagery EEG data.