Subject-Independent Classification of Motor Imagery Tasks in EEG Using Multisubject Ensemble CNN

Abstract

Subject-independent (SI) classification is a major area of investigation in Brain-Computer Interface (BCI) that aims to construct classifiers of users&#x2019; mental states based on collected electroencephalogram (EEG) of independent subjects. Significant inter-subject variabilities in the EEG are among the most challenging issues in designing SI BCI systems. In this work, we propose and examine the utility of Multi-Subject Ensemble Convolutional Neural Network (MS-En-CNN) for SI classification of motor imagery (MI) tasks. The base classifiers used in MS-En-CNN have a fixed CNN architecture (referred to as DeepConvNet) that are trained using data collected from multiple subjects during the training process. In this regard, training subjects are divided into <i>K</i>-folds using which <i>K</i> base DeepConvNets are trained based on data from <i>K</i>-1 folds, whereas the hyperparameter optimization is performed using the held-out fold. We evaluate the performance of the MS-En-CNN on the large open-access MI dataset from the literature, which includes 54 participants and a total number of 21,600 trials. The result shows that the MS-En-CNN achieves the highest single-trial SI classification performance reported on this dataset. In particular, we obtained SI classification performances with average and median accuracies of 85.42% and 86.50% (&#x00B1; 10.16%), respectively. This result exhibits a statistically significant improvement (<i>p</i> &#x003C; 0.001) over the best previously reported result with an average and a median accuracy of 84.19% and 84.50% (&#x00B1;10.08%), respectively.