Classification of EEG Motor Imagery Tasks Utilizing 2D Temporal Patterns with Deep Learning

This study aims to explore the decoding of human brain activities using EEG signals for Brain Computer Interfaces by utilizing a multi-view spatiotemporal hierarchical deep learning method. In this study, we explored the transformation of 1D temporal EEG signals into 2D spatiotemporal EEG image sequences as well as we explored the use of 2D spatiotemporal EEG image sequences in the proposed multi-view hierarchical deep learning scheme for recognition. For this work, the PhysioNet EEG Motor Movement/Imagery Dataset is used. Proposed model utilizes Conv2D layers in a hierarchical structure, where a decision is made at each level individually by using the decisions from the previous level. This method is used to learn the spatiotemporal patterns in the data. Proposed model achieved a competitive performance compared to the current state of the art EEG Motor Imagery classification models in the binary classification paradigm. For the binary Imagined Left Fist versus Imagined Right Fist classification, we were able to achieve 82.79% average validation accuracy. This level of validation accuracy on multiple test dataset proves the robustness of the proposed model. At the same time, the models clearly show an improvement due to the use of the multi-layer and multi-perspective approach.