Classification of Non-Time-Locked Rapid Serial Visual Presentation Events for Brain-Computer Interaction Using Deep Learning
University of Texas at San Antonio San Antonio United States
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Deep learning solutions based on deep neural networks DNN and deep stack networks DSN were investigated for classifying target images in a non-time-Iocked rapid serial visual presentation RSVP image target identification task using EEG. Several feature extraction methods associated with this task were implemented and tested for deep learning, where a sliding window method using the trained classifier was used to predict the occurrence of target events in a non-time-locked fashion.. The deep learning algorithms explored based on deep stacking networks were able to improve the error rate by about 5 over existing algorithms such as linear discriminant analysis LDA for this task. Initial test results also showed that this method based on deep stacking networks for non-time-Iocked classification can produce an error rate close to that achieved for time-locked classification, thus illustrating the power of deep learning for complex feature spaces.