Automatic classification of eeg signals, based on image interpretation of spatio-temporal information

AbstractBrain-Computer Interface (BCI) applications provide a direct way to map human brain activity onto the control of external devices, without a need for physical movements. These systems, crucial for medical applications and also useful for non-medical applications, predominantly use EEG signals recorded non-invasively, for system control, and require algorithms to translate signals into commands. Traditional BCI applications heavily depend on algorithms tailored to specific behavioral paradigms and on data collection using EEG systems with multiple channels. This complicates usability, comfort, and affordability. Moreover, the limited availability of extensive training datasets limits the development of robust models for classifiying collected data into behavioral intents. To address these challenges, we introduce an end-to-end EEG classification framework that employs a pre-trained Convolutional Neural Network (CNN) and a Transformer, initially designed for image processing, applied here for spatiotemporal representation of EEG data, and combined with a custom developed automated EEG channel selection algorithm to identify the most informative electrodes for the process, thus reducing data dimensionality, and easing subject comfort, along with improved classification performance of EEG data onto subjects intent. We evaluated our model using two benchmark datasets, the EEGmmidb and the OpenMIIR. We achieved superior performance compared to existing state-of-the-art EEG classification methods, including the commonly used EEGnet. Our results indicate a classification accuracy improvement of 7% on OpenMIIR and 1% on EEGmmidb, reaching averages of 81% and 75%, respectively. Importantly, these improvements were obtained with fewer recording channels and less training data, demonstrating a framework that can support a more efficient approach to BCI tasks in terms of the amount of training data and the simplicity of the required hardware system needed for brain signals. This study not only advances the field of BCI but also suggests a scalable and more affordable framework for BCI applications.