A hybrid ensemble voting-based residual attention network for motor imagery EEG Classification

Multi-class motor imagery Electroencephalography (EEG) activity decoding has always been challenging for the development of Brain Computer Interface (BCI) system. Deep learning has recently emerged as a powerful approach for BCI system development using EEG activity. However, the EEG activity analysis and classification should be robust, automated and accurate. Currently, available BCI systems perform well for binary task identification whereas, multi-class classification of EEG activity for BCI applications is still a challenging task. In this work, a hybrid residual attention ensemble voting classifier model is developed for EEG-based Motor Imagery-Brain Computer Interface (MI-BCI) task classification. The Time–Frequency Representation (TFR) of the multi-class EEG activity is generated using Transient Extracting Transform. The TFR spectrogram images are input to the designed residual attention ensemble voting classifier model for training and classification purposes. The model is evaluated using different fusion strategies viz. feature-level and score-level fusion of layers. The proposed model is evaluated on two MI-BCI datasets, BCI competition IV 2a and BCI competition III 3a, yielding the highest classification accuracies of 88.14% and 93.13%, respectively. The results obtained on a large multi-class MI-BCI dataset confirm that the proposed hybrid residual attention ensemble voting classifier model significantly outperforms the conventional algorithm and achieves significantly high classification accuracy for the feature-level fusion of layers. The developed framework aids in identifying different tasks for multi-class MI-BCI EEG activity.