Noninvasive Brain-Machine Interface to Control Both Mecha TE Robotic Hands Using Emotiv EEG Neuroheadset

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

• Adrienne Kline
• Jaydip Desai

• Brain-machine interface
• EEGLAB
• emotiv EEG neuroheadset
• openViBE
• simulink.

[1] E. C. Leuthardt, G. Schalk, J. Roland, A. Rouse, and D.W. Moran,
“Evolution of brain-computer interfaces: Going beyond classic motor
physiology.” Neurosurgical Focus, vol. 27, pp. E4 1-11, July 2009.
[2] S. Fok, R. Schwartz, M. Wronkiewicz, C. Hlmes, J. Zhang, T. Somers,
D. Bundy, and E. Leuthardt, “An EEG-based brain computer interface
for rehabilitation and restoration of hand control following stroke using
ipsilateral cortical physiology.” in Conference Proceedings of the IEEE
Engineering in Medicine and Biology Society, Boston, MA, 2011, pp.
6277-6278. [3] Y. M. Chi, T-P. Jung, and G. Cauwenberghs, “Dry-contact and
noncontact biopotential electrodes: Methodological review.” IEEE
Reviews in Biomedical Engineering, vol. 3, pp. 106-119, Oct. 2010.
[4] R. Homan, J. Herman, and P. Purdy, “Cerebral location of International
10-20 System electrode placement.” Electroencephalography and
Clinical Neurophysiology, vol. 6, pp. 376-382, April 1987.
[5] J.L. Sirvent Blasco, E. Ianez, A. Ubeda et al., “Visual evoked potentialbased
brain-machine interface applications to assist disabled people,”
Expert Systems with Applications, 2012.
[6] G. Dornhege, J. Millan, T. Hinterberger, D. McFarland, K. Muller,
“General Signal Processing and Machine Learning Tools or BCI
Analysis,” in Toward Brain-Computer Interfacing, 2007, Massechusets,
ch. 13, sec. III, pp. 207-234.
[7] J. Wolpaw, E. Wolpaw, “BCI Signal Processing: Feature Extraction,” in
Brain-Computer Interfaces, New York, Oxford Inc, 2012, ch. 7, sec. 3,
pp. 123-146.
[8] B. He, A. Brockmeier, J. Principe, “Decoding Algorithms for Brain-
Machine Interfaces,” Neural Engineering, 2nd ed. New York, Springer,
2013, ch. 4, pp. 223-258.
[9] A. Fukunaga, T. Ohira, M. Kamba, et al., “The Possibility of Left
Dominant Activation of the Sensorimotor Cortex During Lip Protrusion
in Men,” 2009, Springer.