Deep Learning with ConvNET Predicts Imagery Tasks Through EEG
This work addresses EEG-based brain-computer interface tasks for potential applications in assistive technology, but it is incremental as it applies existing deep learning techniques to a known domain.
The study tackled the problem of predicting imagined left and right movements from raw EEG data using convolutional neural networks (ConvNets) on a subject-independent basis, achieving improved performance over conventional fully-connected neural networks with spectral features.
Deep learning with convolutional neural networks (ConvNets) have dramatically improved learning capabilities of computer vision applications just through considering raw data without any prior feature extraction. Nowadays, there is rising curiosity in interpreting and analyzing electroencephalography (EEG) dynamics with ConvNets. Our study focused on ConvNets of different structures, constructed for predicting imagined left and right movements on a subject-independent basis through raw EEG data. Results showed that recently advanced methods in machine learning field, i.e. adaptive moments and batch normalization together with dropout strategy, improved ConvNets predicting ability, outperforming that of conventional fully-connected neural networks with widely-used spectral features.