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 ...
There is a demand for a new neurorehabilitation modality with a brain-computer interface for stroke patients with insufficient or no remaining hand motor function. We previously developed a robotic hand rehabilitation system triggered by multichannel near-infrared spectroscopy (NIRS) to address this demand. In a preliminary prototype system, a robotic hand orthosis, providing one degree-of-freedom motion for a hand's closing and opening, is triggered by a wireless command from a NIRS system, capturing a subject's motor cortex activation. To examine the feasibility of the prototype, we conducted a preliminary test involving six neurologically intact participants. The test comprised a series of evaluations for two aspects of neurorehabilitation training in a real-time manner: classification accuracy and execution time. The effects of classification-related factors, namely the algorithm, signal type, and number of NIRS channels, were investigated. In the comparison of algorithms, linear discrimination analysis performed better than the support vector machine in terms of both accuracy and training time. The oxyhemoglobin versus deoxyhemoglobin comparison revealed that the two concentrations almost equally contribute to the hand motion estimation. The relationship between the number of NIRS channels and accuracy indicated that a certain number of channels are needed and suggested a need for a method of selecting informative channels. The computation time of 5.84 ms was acceptable for our purpose. Overall, the preliminary prototype showed sufficient feasibility for further development and clinical testing with stroke patients.
The S-STAMP method using a rigid template on the soft surface yields a significantly smaller TRE than that of conventional, manually scanned surface matching registration. This strategy provides an alternative option to improve the accuracy of IGS without loading patients with additional invasive procedures.
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