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

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.

Imagined Hand Clenching Force and Speed Modulate Brain Activity and Are Classified by NIRS Combined With EEG

“…Compared with studies of combined NIRS-EEG decoding of motor imagery or movement directions [10], [29][30][31], the present study attempted to identify imagined force and speed (such as imagined hand clenching force and speed) by simultaneously collecting NIRS and EEG data from the same motor area. The averaged and maximum accuracy for decoding imagined hand clenching force and speed (six classes) using the fusion features of HbO-HbD & IA-IP-IF were 0.74 ± 0.02 and 0.78 ± 0.01, respectively.…”

“…It is well known that the advantage of using EEG and NIRS in a hybrid BCI system is either to improve classification accuracy or to increase the number of control commands. Some progress in decoding motor attempt and imagery or movement directions using NIRS-EEG has been made [10], [29]- [31]. Yin et al separated hand clenching force from speed motor imagery and achieved clear results [10].…”

Driver Fatigue Detection System Using Electroencephalography Signals Based on Combined Entropy Features

“…Though functional magnetic resonance imaging (fMRI) was widely used to study the operational organization of the human brain (with considerable clinical significance), it could imply high expense and operate inconveniently for driving fatigue in real driving conditions [1]. Recently, a relatively new classification techniques for functional near-infrared spectroscopy (fNIRS) was also widely used to monitor the occurrence of neuro-plasticity after neuro-rehabilitation and neuro-stimulation, it has low cost, portability, safety, low noise (compared to fMRI), and ease of use [2,3], For example, Khan used fNIRS to discriminate the alert and drowsy states for a passive brain-computer interface, obtaining average accuracies in the right dorsolateral prefrontal cortex of 83.1%, 83.4,% and 84.9% in different time windows respectively [4]. However, fNIRS is mainly at present a confirmatory study with shortcomings of poor time resolution compared with EEG/ERP (event-related potential) and signal acquisition without covering the whole brain.…”

Classification of Hemodynamic Responses Associated With Force and Speed Imagery for a Brain-Computer Interface

“…They provided a new experimental paradigm for establishing the multi-class fNIRS-BCI system. In addition, hybrid measurement technique using fNIRS-EEG [27] can also produce more control commands for BCI system. However, there is no research involved in kinematic parameter imagery for building multiclass fNIRS-BCI systems.…”