This repo contains the source codes used in our research paper entitled "A Systematic Deep Learning Model Selection for P300-Based Brain-Computer Interfaces".
The abstract:
Predicting human mental intent has emerged as a major area of investigation in Brain-Computer Interface (BCI) research due to the desire to translate neural activities into useful control and communication commands. Such studies involve collecting electroencephalographic (EEG) data from subjects to train classifiers of users' mental states. However, various sources of inter- or intra-subject variabilities in brain signals render training classifiers in BCI systems challenging. From a machine learning perspective, this model training generally follows a common methodology: 1) apply some type of feature extraction, which can be time-consuming and may require domain knowledge; and 2) train a classifier using extracted features. The advent of deep learning technologies has offered unprecedented opportunities to not only construct remarkably accurate classifiers but also to integrate the feature extraction stage into the classifier construction. Although integrating feature extraction, which is generally domain-dependent, into the classifier construction is a considerable advantage of deep learning models, the process of architecture selection for BCIs generally depends on domain knowledge. In this study, we examine the feasibility of conducting a systematic model selection combined with mainstream deep learning architectures to construct accurate classifiers for decoding P300 event-related potentials.
In particular, we present the results of 232 CNNs (4 datasets