Introduction: Clarification of the relationship between external stimuli and brain response has been an important topic in neuroscience and brain rehabilitation. In the current study, using functional near infrared spectroscopy (fNIRS), we attempted to investigate cortical activation patterns generated during execution of a rehabilitation robotic hand. Methods: Ten normal subjects were recruited for this study. Passive movements of the right fingers were performed using a rehabilitation robotic hand at a frequency of 0.5 Hz. We measured values of oxy-hemoglobin(HbO), deoxy-hemoglobin(HbR) and total-hemoglobin(HbT) in five regions of interest: the primary sensory-motor cortex (SM1), hand somatotopy of the contralateral SM1, supplementary motor area (SMA), premotor cortex (PMC), and prefrontal cortex (PFC). Results: HbO and HbT values indicated significant activation in the left SM1, left SMA, left PMC, and left PFC during execution of the rehabilitation robotic hand(uncorrected, pConclusions: Our results appear to indicate that execution of the rehabilitation robotic hand could induce cortical activation.

Brain signal variation across different subjects and sessions significantly impairs the accuracy of most brain-computer interface (BCI) systems. Herein, we present a classification algorithm that minimizes such variation, using linear programming support-vector machines (LP-SVM) and their extension to multiple kernel learning methods. The minimization is based on the decision boundaries formed in classifiers' feature spaces and their relation to BCI variation. Specifically, we estimate subject/session-invariant features in the reproducing kernel Hilbert spaces (RKHS) induced with Gaussian kernels. The idea is to construct multiple subject/session-dependent RKHS and to perform classification with LP-SVMs. To evaluate the performance of the algorithm, we applied it to oxy-hemoglobin data sets acquired from eight sessions and seven subjects as they performed two different mental tasks. Results show that our classifiers maintain good performance when applied to random patterns across varying sessions/subjects.

In this paper, a new motion artifact correction method is proposed based on multi-channel functional near-infrared spectroscopy (fNIRS) signals. Recently, wavelet transform and hemodynamic response function-based algorithms were proposed as methods of denoising and detrending fNIRS signals. However, these techniques cannot achieve impressive performance in the experimental environment with lots of movement such as gait and rehabilitation tasks because hemodynamic responses have features similar to those of motion artifacts. Moreover, it is difficult to correct motion artifacts in multi-measured fNIRS systems, which have multiple channels and different noise features in each channel. Thus, a new motion artifact correction method for multi-measured fNIRS is proposed in this study, which includes a decision algorithm to determine the most contaminated fNIRS channel based on entropy and a reconstruction algorithm to correct motion artifacts by using a wavelet-decomposed back-propagation neural network. The experimental data was achieved from six subjects and the results were analyzed in comparing conventional algorithms such as HRF smoothing, wavelet denoising, and wavelet MDL. The performance of the proposed method was proven experimentally using the graphical results of the corrected fNIRS signal, CNR that is a performance evaluation index, and the brain activation map.

The purpose of this study is to investigate cerebral cortex activation during active movement and passive movement by using a functional near-infrared spectroscopy (fNIRS). Tasks were the flexion/extension of the right hand finger by active movement and passive movement. Oxy-hemoglobin concentration changes calculated from fNIRS and analyzed the activation and connectivity so as to understand dynamical brain relationship. The results demonstrated that the brain activation in passive movements is similar to motor execution. During active movement, the estimated causality patterns showed significant causality value from the supplementary motor area (SMA) to the primary motor cortex (M1). During the passive movement, the causality from the primary somatosensory cortex (S1) to the primary motor cortex (M1) was stronger than active movement. These results demonstrated that active and passive movements had a direct effect on the cerebral cortex but the stimulus pathway of active and passive movement is different. This study may contribute to better understanding how active and passive movements can be expressed into cortical activation by means of fNIRS.

Passive movement, action observation and motor imagery as well as motor execution have been suggested to facilitate the motor function of human brain. The purpose of this study is to investigate the cortical activation patterns of these four modes using a functional near-infrared spectroscopy (fNIRS) system. Seven healthy volunteers underwent optical brain imaging by fNIRS. Passive movements were provided by a functional electrical stimulation (FES). Results demonstrated that while all movement modes commonly activated premotor cortex, there were considerable differences between modes. The pattern of neural activation in motor execution was best resembled by passive movement, followed by motor imagery, and lastly by action observation. This result indicates that action observation may be the least preferred way to activate the sensorimotor cortices. Thus, in order to show the feasibility of motor facilitation by a brain computer interface (BCI) for an extreme case, we paradoxically adopted the observation as a control input of the BCI. An observation-FES integrated BCI activated sensorimotor system stronger than observation but slightly weaker than FES. This limitation should be overcome to utilize the observation-FES integrated BCI as an active motor training method.

Objectives: Neuromuscular electrical stimulation (NMES) has been used in the field of rehabilitation for a long time. Previous studies on NMES have focused on the peripheral effect, in contrast, relatively little is known about the effect on the cerebral cortex. In the current study, we attempted to investigate the change of cortical activation pattern induced by NMES during execution of hand movements in normal subjects, using functional near infrared spectroscopy (fNIRS). Methods: Twelve healthy normal subjects were randomly assigned to the NMES group (six subjects) and the sham group (six subjects). We measured oxy-hemoglobin (HbO) in six regions of interest (ROI) during pre-NMES and post-NMES motor phase; the left dorsolateral and ventrolateral prefrontal cortex, premotor cortex, primary sensory-motor cortex (SM1), hand somatotopic area of SM1, and posterior parietal cortex. Between the pre-NMES and the post-NMES motor phases, real or sham NMES was applied on finger and wrist extensors of all subjects during a period of 5 minutes. Results: In all groups, during the pre-NMES motor phase, the HbO value in the hand somatotopic area of the left SM1 was higher than those of other ROIs. In the NMES group, during the post-NMES motor phase, HbO value variation in the hand somatotopic area of the left SM1 showed a significant decrease, compared with that of sham group (p < 0.05). However, in the sham group, similar aspect of results in HbO values of all ROIs was observed between pre-NMES and post-NMES motor phases (p > 0.05). Conclusions: Results of this study showed that NMES induced a decrease of cortical activation during execution of hand movements. This finding appears to indicate that application of NMES can increase the efficiency of the cerebral cortex during execution of motor tasks.