Neural Network Architecture for EEG Based Speech Activity Detection

Abstract

In this paper, research focused on speech activity detection using brain EEG signals is presented. In addition to speech stimulation of brain activity, an innovative approach based on the simultaneous stimulation of the brain by visual stimuli such as reading and color naming has been used. Designing the solution, classification using two types of artificial neural networks were proposed: shallow Feed-forward Neural Network and deep Convolutional Neural Network. Experimental results of classification demonstrated F1 score 79.50% speech detection using shallow neural network and 84.39% speech detection using deep neural network based on cross-evaluated classification models.

Keywords

• electroencephalography
• brain-computer interface
• mobile eeg device
• speech detection
• visual stimuli
• feed-forward neural network
• convolutional neural network