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.
Highlights
Brain-computer interface (BCI) is a methodology that correlates brain activities with external devices
Two classes of signal were obtained by EEG using motor execution from the left and right motor cortex regions, and two classes are
In comparison to other studies in which two control commands are generated based on motor and arithmetic tasks with low classification accuracies results (Stangl et al, 2013), the results have shown potential for control command generation with high classification accuracy
Summary
Brain-computer interface (BCI) is a methodology that correlates brain activities with external devices. The recent research and trend have demonstrated the enormous potential of the BCI approach (Matthews et al, 2008; Nicolas-Alonso and Gomez-Gil, 2012; Ortiz-Rosario and Adeli, 2013). The material advances in the cutting-edge technology, has reduced the cost of BCI equipment. The BCI domain comprehends both invasive and non-invasive methods. Invasive methods such as electrical-corticography (ECoG), though showing promising signal-acquisition results, are not recommended, as they entail very significant risks. Non-invasive methods are much safer alternatives in this regard (Min et al, 2010)
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