Movement and Mind-Body Therapies Commons^™

An End-To-End Cnn With Attentional Mechanism Applied To Raw Eeg In A Bci Classification Task, Elnaz Lashgari, Jordan Ott, Akima Connelly, Pierre Baldi, Uri Maoz

Psychology Faculty Articles and Research

Objective. Motor-imagery (MI) classification base on electroencephalography (EEG) has been long studied in neuroscience and more recently widely used in healthcare applications such as mobile assistive robots and neurorehabilitation. In particular, EEG-based motor-imagery classification methods that rely on convolutional neural networks (CNNs) have achieved relatively high classification accuracy. However, naively training CNNs to classify raw EEG data from all channels, especially for high-density EEG, is computationally demanding and requires huge training sets. It often also introduces many irrelevant input features, making it difficult for the CNN to extract the informative ones. This problem is compounded by a dearth of training …