EEG Motor Imagery Classification using Fusion Convolutional Neural Network

Brain-Computer Interfaces (BCIs) are systems that can help people with limited motor skills interact with their environment without the need for outside help. Therefore, the signal is representative of a motor area in the active brain system. It is used to recognize MI-EEG tasks via a deep learning techniques such as Convolutional Neural Network (CNN), which poses a potential problem in maintaining the integrity of frequency-time-space information and then the need for exploring the CNNs fusion. In this work, we propose a method based on the fusion of three CNN (3CNNs). Our proposed method achieves an interesting precision, recall, F1-score, and accuracy of 61.88%, 62.50%, 61.47%, 64.75% respectively when tested on the 9 subjects from the BCI Competition IV 2a dataset. The 3CNNs model achieved higher results compared to the state-of-the-art.