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

Subject-independent (SI) classification is a major area of investigation in Brain-Computer Interface (BCI) that aims to construct classifiers of users’ 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 $K$ -folds using which $K$ base DeepConvNets are trained based on data from $K-1$ 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% (± 10.16%), respectively. This result exhibits a statistically significant improvement ( ${p} < 0.001$ ) over the best previously reported result with an average and a median accuracy of 84.19% and 84.50% (±10.08%), respectively.