Noninvasive Brain-Machine Interface To Control Both Mecha Te Robotic Hands Using Emotiv Eeg Neuroheadset

Abstract

Electroencephalogram (EEG) is a noninvasive<br> technique that registers signals originating from the firing of neurons<br> in the brain. The Emotiv EEG Neuroheadset is a consumer product<br> comprised of 14 EEG channels and was used to record the reactions<br> of the neurons within the brain to two forms of stimuli in 10<br> participants. These stimuli consisted of auditory and visual formats<br> that provided directions of ‘right’ or ‘left.’ Participants were<br> instructed to raise their right or left arm in accordance with the<br> instruction given. A scenario in OpenViBE was generated to both<br> stimulate the participants while recording their data. In OpenViBE,<br> the Graz Motor BCI Stimulator algorithm was configured to govern<br> the duration and number of visual stimuli. Utilizing EEGLAB under<br> the cross platform MATLAB®, the electrodes most stimulated during<br> the study were defined. Data outputs from EEGLAB were analyzed<br> using IBM SPSS Statistics® Version 20. This aided in determining<br> the electrodes to use in the development of a brain-machine interface<br> (BMI) using real-time EEG signals from the Emotiv EEG<br> Neuroheadset. Signal processing and feature extraction were<br> accomplished via the Simulink® signal processing toolbox. An<br> Arduino™ Duemilanove microcontroller was used to link the Emotiv<br> EEG Neuroheadset and the right and left Mecha TE™ Hands.