Hyperparameter Bayesian Optimisation applied to ConvNets for Motor Imagery tasks

Brain-computer interfaces allow us to read human neural data and turn it into valuable information for diverse applications. However, those systems present response disparity because of the non-stationarity of the neural data due to the variability of the response from a subject to another. Thus, they force us to tune the hyperparameters for each case rather than using a unique combination. One method that is used for this purpose is the Bayesian optimization for hyperparameters. In this paper, we propose a study that targets raw EEG signals classified with Convolutional Neural Network and Bayesian Optimization. We optimized the hyperparameters that are related to the architecture. The results suggest it is better to use specific hyperparameters for each subject rather than a global set of hyperparameters for all subjects. Also, we found that Lower Confidence Bound is the best acquisition function for our application.