Comparison of Feature Vector Compositions to Enhance the Performance of NIRS-BCI-Triggered Robotic Hand Orthosis for Post-Stroke Motor Recovery

Abstract

Recently, brain-computer interfaces, combined with feedback systems and goal-oriented training, have been investigated for their capacity to promote functional recovery after stroke. Accordingly, we developed a brain-computer interface-triggered robotic hand orthosis that assists hand-closing and hand-opening for post-stroke patients without sufficient motor output. In this system, near-infrared spectroscopy is used to monitor the affected motor cortex, and a linear discriminant analysis-based binary classifier estimates hand posture. The estimated posture then wirelessly triggers the robotic hand orthosis. For better performance of the brain-computer interface, we tested feature windows of different lengths and varying feature vector compositions with motor execution data from seven neurologically intact participants. The interaction between a feature window and a delay in the hemodynamic response significantly affected both classification accuracy (Matthew Correlation Coefficient) and detection latency. The 'preserving channels' feature vector was able to increase accuracy by 13.14% and decrease latency by 29.48%, relative to averaging. Oxyhemoglobin combined with deoxyhemoglobin improved accuracy by 3.71% and decreased latency by 6.01% relative to oxyhemoglobin alone. Thus, the best classification performance resulted in an accuracy of 0.7154 and a latency of 2.8515 s. The hand rehabilitation system was successfully implemented using this feature vector composition, which yielded better classification performance. Appl. Sci. 2019, 9, 3845 2 of 14 paralysis [5]. A BCI, integrated with a motion-assistive device, enables patients to execute motor intention-induced movements that resemble active movements. Thus, patients can participate in effective AMTs and expect motor function recovery. Indeed, several clinical studies have reported that BCI-combined neurorehabilitation improves stroke-impaired motor function [6].Near-infrared spectroscopy (NIRS) is a new non-invasive neuroimaging technique [7] that relies on the hemodynamic response to local neuronal activities. NIRS measures real-time changes in oxyhemoglobin (HbO) and deoxyhemoglobin (HbR) concentrations ([HbO] and [HbR] respectively) in local cortical areas. The background principle of NIRS is based on neurovascular coupling [8], which is the interaction between local neuronal activity and local changes in Cerebral Blood Flow (CBF); local CBF increases to meet the metabolic demand of local neuronal activity. The absorption or reflection of near-infrared light by hemoglobin depends on the amount of hemoglobin combined with oxygen in that local area [9]. Accordingly, NIRS can monitor the hemodynamic response. This resultant hemodynamic response is slower than its causing neuronal activity and takes place with a delay on the order, of seconds.A NIRS-Brain-Computer Interface (NIRS-BCI) is a hemodynamic response-based BCI using NIRS as a neuroimaging modality. Hemodynamic responses captured by NIRS are relatively easier to detect and analyze, and more robust ...

Evaluation of Commercial Ropes Applied as Artificial Tendons in Robotic Rehabilitation Orthoses

“…An improvement in daily life activity, arm function and muscle strength has also been observed [15], as well as minor effects on motor control and mild effects on muscle strength compared with other short-term interventions using robotic therapy [14]. A combination of these devices with brain-computer interfaces has been also used to improve the rehabilitation ability of individuals after stroke, thus proving the effectiveness of these robots during therapy sessions [19].…”

Recent Advances and Design Strategies Towards Wearable Near-Infrared Spectroscopy

“…Its ability to reveal brain activities in different cerebral cortex regions may enable mind reading to control machines, haptic devices, and robots without physical motions [90]. NIRS signals from local cortical activation can assist the control of robotic mechanical hand orthosis for post-stroke hand recovery and rehabilitation [91]. Together with EEG and EMG, an NIRS-integrated HMI can realize the control of a lower limb rehabilitation training robot to help patients with central nervous system injury do walking training without therapists [92].…”

A Hybrid GCN and Filter-Based Framework for Channel and Feature Selection: An fNIRS-BCI Study

“…In the case of classifcation accuracy, the selection of appropriate channels and features plays a vital role [22][23][24]. For active channel selection, averaging over all channels [25][26][27], averaging over a region of interest [28,29], t-and z-statistics [29][30][31][32], baseline correction [33], vector-phase analysis [31,[34][35][36], Pearson correlation coefcient [37], the contrast-to-noise ratio [38], LASSO homotopy-based sparse representation [39], and jointchannel-connectivity [40] methods are employed in fNIRSbased BCI studies. Temporal statistical characteristics of fNIRS signals time series (i.e., mean, slope, peak, minimum value, skewness, kurtosis, variance, and standard deviation) are the most commonly used features [6].…”