Universal Joint Feature Extraction for P300 EEG Classification Using Multi-Task Autoencoder
Universal Joint Feature Extraction for P300 EEG Classification Using Multi-Task Autoencoder
The process of recording Electroencephalography (EEG) signals is onerous and requires massive storage to store signals at an applicable frequency rate. In this work, we propose the EventRelated Potential Encoder Network (ERPENet); a multi-task autoencoder-based model, that can be applied to any ERP-related tasks. The strength of ERPENet lies in …