Decoding of four movement directions using hybrid NIRS-EEG brain-computer interface

Декодирование четырех направлений движения с использованием гибридного интерфейса мозг–компьютер на основе NIRS–ЭЭГ
Muhammad Jawad Khan, Melissa Jiyoun Hong, Keum‐Shik Hong
2014-04-28

electroencephalographyfour-direction movement decodinghybrid NIRS-EEG brain-computer interfacenear-infrared spectroscopyoxyhemoglobin (HbO) changes
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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A hybrid NIRS-EEG brain-computer interface decoded four movement directions: forward, backward, left, and right.
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EEG over the left and right motor cortices detected right- and left-hand tapping for left and right commands, respectively.
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Experiments with 12 subjects achieved high classification accuracies, indicating that hybrid NIRS-EEG can reliably estimate four distinct control signals.
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NIRS over the prefrontal cortex captured oxy-hemoglobin changes during arithmetic mental tasks for forward and backward commands.

Four-direction movement control signals decoded by a hybrid NIRS-EEG brain-computer interface

Classification and decoding accuracy of control signals associated with mental arithmetic and hand-tapping brain responses

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2014-04-28
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Muhammad Jawad Khan
Melissa Jiyoun Hong
Keum‐Shik Hong
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