Exploring a Deeper Convolutional Neural Network Architecture with high dropout for Motor Imagery BCI Decoding

In recent years, brain-computer interfaces (BCI) have gained lots of popularity as an assistive method for individuals with disabilities to regain their communication or mobility and interact with the world more naturally. The utility of such systems rely on their accuracy in decoding user intentions. In this research, we build on previous BCI research that explored CNN models for decoding motor imagery tasks. Inspired by denoising autoencoders, we introduce a deeper CNN model with very high drop out rates that performs better at motor imagery tasks by reducing the effects of EEG data artefacts on the decoder. We tested the model on a hand squeeze data-set where our model has outperformed the previous best classifiers. Additionally, given the deeper nature of this model, we have explored its limitations by comparing its performance as both the individual sample size and the number of training data change.