Incep-EEGNet: A ConvNet for Motor Imagery Decoding

The brain-computer interface consists of connecting the brain with machines using the brainwaves as a mean of communication for several applications that help to improve human life. Unfortunately, Electroencephalography that is mainly used to measure brain activities produces noisy, non-linear and non-stationary signals that weaken the performances of Common Spatial Pattern (CSP) techniques. As a solution, deep learning waives the drawbacks of the traditional techniques, but it still not used properly. In this paper, we propose a new approach based on Convolutional Neural Networks (ConvNets) that decodes the raw signal to achieve state-of-the-art performances using an architecture based on Inception. The obtained results show that our method outperforms state-of-the-art filter bank common spatial patterns (FBCSP) and ShallowConvNet on based on the dataset IIa of the BCI Competition IV.