An efficient Dual-Band CNN for Motor Imagery EEG Signal Classification

Abstract

Brain-computer interface (BCI) systems have recently gained much attention due to their ability to interpret human thought processes from electroencephalogram (EEG)-based motor imagery (MI) signals. Among various machine learning based methods, instead of using hand-crafted features, end-to-end deep learning (DL) based methods are getting popularity due to their very satisfactory performance. Even in case of non-stationary and subject specific MI-EEG signals, DL methods offer high accuracy in classifying MI tasks. In this paper, a dual band convolutional neural network (DBCNN) based on MI-EEG two band limited efficient signals is proposed. Here, beta wave signals are used to improve classification performance in motor imagery tasks. Beta waves are associated with motor imagery tasks. On the other hand our second signal is the pre-processed bandpass signal. The DBCNN model uses temporal CNN and spatial CNN to explore temporal and spatial features. This model also uses a spatial convolutional network for single channels from multiple channels. Finally the data are classified for MI-EEG signal classification accuracy. An extensive experiment on MI-based BCI IV 2a EEG dataset on nine subjects shows that very satisfactory classification performance is achieved. The proposed model offers higher classification accuracy that is obtained by some state-of-the-art methods.