Temporal Pyramid Pooling for Decoding Motor-Imagery EEG Signals

Detecting a user's intentions is critical in human-computer interactions. Recently, brain-computer interfaces (BCIs) have been extensively studied to facilitate more accurate detection and prediction of the user's intentions. Specifically, various deep learning approaches have been applied to the BCIs for decoding the user's intent from motor-imagery electroencephalography (EEG) signals. However, their ability to capture the important features of an EEG signal remains limited, resulting in the deterioration of performance. In this paper, we propose a multi-layer temporal pyramid pooling approach to improve the performance of motor imagery-based BCIs. The proposed scheme introduces the application of multilayer multiscale pooling and fusion methods to capture various features of an EEG signal, which can be easily integrated into modern convolutional neural networks (CNNs). The experimental results based on the BCI competition IV dataset indicate that the CNN architectures with the proposed multilayer pyramid pooling method enhance classification performance compared to the original networks.