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Decoding of Four Movement Directions Using Hybrid NIRS-EEG Brain-computer Interface

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Specialty Neurology
Date 2014 May 9
PMID 24808844
Citations 76
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Abstract

The hybrid brain-computer interface (BCI)'s multimodal technology enables precision brain-signal classification that can be used in the formulation of control commands. In the present study, an experimental hybrid near-infrared spectroscopy-electroencephalography (NIRS-EEG) technique was used to extract and decode four different types of brain signals. The NIRS setup was positioned over the prefrontal brain region, and the EEG over the left and right motor cortex regions. Twelve subjects participating in the experiment were shown four direction symbols, namely, "forward," "backward," "left," and "right." The control commands for forward and backward movement were estimated by performing arithmetic mental tasks related to oxy-hemoglobin (HbO) changes. The left and right directions commands were associated with right and left hand tapping, respectively. The high classification accuracies achieved showed that the four different control signals can be accurately estimated using the hybrid NIRS-EEG technology.

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References
1.
Rehan M, Hong K . Modeling and automatic feedback control of tremor: adaptive estimation of deep brain stimulation. PLoS One. 2013; 8(4):e62888. PMC: 3634768. DOI: 10.1371/journal.pone.0062888. View

2.
Pivik R, Broughton R, Coppola R, Davidson R, Fox N, Nuwer M . Guidelines for the recording and quantitative analysis of electroencephalographic activity in research contexts. Psychophysiology. 1993; 30(6):547-58. DOI: 10.1111/j.1469-8986.1993.tb02081.x. View

3.
Xu M, Qi H, Wan B, Yin T, Liu Z, Ming D . A hybrid BCI speller paradigm combining P300 potential and the SSVEP blocking feature. J Neural Eng. 2013; 10(2):026001. DOI: 10.1088/1741-2560/10/2/026001. View

4.
Robinson N, Vinod A, Ang K, Tee K, Guan C . EEG-based classification of fast and slow hand movements using Wavelet-CSP algorithm. IEEE Trans Biomed Eng. 2013; 60(8):2123-32. DOI: 10.1109/TBME.2013.2248153. View

5.
Pfurtscheller G, Allison B, Brunner C, Bauernfeind G, Solis-Escalante T, Scherer R . The hybrid BCI. Front Neurosci. 2010; 4:30. PMC: 2891647. DOI: 10.3389/fnpro.2010.00003. View