Conceptual Neuroadaptive Brain-Computer Interface utilizing Event-related Desynchronization

Abstract

This paper presents evidence for the possibility of a neuroadaptive system, based on electroencephalography (EEG) readings from the motor cortex region, to predict an individual's actions before the onset of motion. Testing for the neuroadaptive system utilized a G.nautilus headset, MATLAB with the EEGLAB toolbox, and a computer with the Processing IDE. Code from the Processing IDE provides an image slideshow which displays faces of various individuals so that the subject presses a keyboard key on a certain image. Three subjects were tested for 60 trials each, 30 trials where the keyboard key was pressed, and 30 trials where they were not, to gather enough data to train and test a classifier by using a machine learning algorithm. Machine learning assessed classification accuracy initially using 10 training trials and increased the training set by 10 trials each time to reassess accuracy until a total of 40 training trials were used. A set of 20 trials were used to assess accuracy without and with machine learning. Additionally, theoretical accuracy was computed by removing unfeasible machine learning features to assess the potential accuracy in a real-time system. The results provided an average accuracy of 52% without machine learning and an average accuracy ranging from 91.66% to 96.66% using the K-Nearest Neighbors(KNN) algorithm. The average theoretical accuracy ranged from to be 60% to 68.33%.