Sang Hyeon Jin

The beginning of neurohaptics: Controlling cognitive interaction via brain haptic interface

This study showed an example of neurohaptic interface which can be the direct connector between haptics and brain. We investigated the neural activities of motion which are essential tasks for haptic interaction. Eating was adopted to explore the neural activities from the functional near-infrared spectroscopy (fNIRS) imaging. Subjects carried out real motion, action observation, and motor imagery. From this study we convinced that the action observation and motor imagery may create the similar neural activities to the active movement. In addition, we showed the feasibility of an fNIRS integrated brain haptic interaction based on the neural mechanism of action observation. Implications of this study suggested that the neurohaptics can play a more active role in realistic haptic interaction for the real applications including brain computer interface.

Baseline drift detection index using wavelet transform analysis for fNIRS signal

The general linear model (GLM) as a standard model for fMRI analysis has been applied to functional near-infrared spectroscopic (fNIRS) imaging analysis as well. The GLM has drawback of failure in fNIRS signals, when they have drift globally. Wavelet based de-trending technique is very popular to correct the baseline drift (BD) in fNIRS. However, this method globally distorted the total multichannel signals even if just one channels signal was locally drifted. This paper suggests BD detection index to indicate BD as an objective index. The experiments show the performance of the proposed detection index as graphic results with current de-trending algorithm.

Neuro-feedback using real-time near infrared spectroscopy enhances brain plasticity during treadmill walking: A pilot study

Despite of the findings that motor imagery and motor execution are supposed to share common neural networks, previous studies using motor imagery based rehabilitation have revealed inconsistent results. In this study, we implemented a real-time neuro-feedback system based on near infrared spectroscopy (NIRS), and investigate the effect of neuro-feedback for gait rehabilitation. Four healthy volunteers performed individually given tasks. In the non-feedback task, subjects were performed treadmill walking without feedback. In the feedback task was used in subjects to evaluate whether real-time cortical oxygenated hemoglobin signal feedback during treadmill walking task. Results demonstrated that neuro-feedback induced significantly greater activation of the premotor area and supplementary motor area compare with non-feedback treadmill walking. This result suggested the feasibility of NIRS based real-time neuro-feedback system on gait rehabilitation.

Cortical activities during a stand to sit movement using fNIRS

Recently, a functional near-infrared spectroscopy (fNIRS) is frequently reported optical brain imaging method from the standpoint of clinical feasibility. This paper was aimed at examining whether fNIRS can be an appropriate brain imaging modality for checking the progress of rehabilitation treatments or not. Two healthy adults performed the given task. Stand to sit task was offered in this study. The results showed that stand to sit movement commonly activated the medial primary motor cortex and primary sensory motor cortex. A fNIRS accurately pointed the brain activity coincided with neurophysiological evidences which were commonly accepted. The results from this study we saw the possibility of the utilizing NIRS into the field of rehabilitation medicine and may contribute to better understanding how motor executions can be expressed into cortical activations.

Robust functional near infrared spectroscopy denoising using multiple wavelet shrinkage based on a hemodynamic response model

Functional near infrared spectroscopy can measure hemodynamic signals, and the results are similar to functional magnetic resonance imaging of blood-oxygen-level-dependent signals. Thus, functional near infrared spectroscopy can be employed to investigate brain activity by measuring the absorption of near infrared light through an intact skull. Recently, a general linear model, which is a standard method for functional magnetic resonance imaging, was applied to functional near infrared spectroscopy imaging analysis. However, the general linear model fails when functional near infrared spectroscopy signals retain noise, such as that caused by the subjects movement during measurement. Although wavelet-based denoising and hemodynamic response function smoothing are popular denoising methods for functional near infrared spectroscopy signals, these methods do not exhibit impressive performances for very noisy environments and a specific class of noise. Thus, this paper proposes a new denoising algorithm that uses multiple wavelet shrinkage and a multiple threshold function based on a hemodynamic response model. Through the experiments, the performance of the proposed algorithm is verified using graphic results and objective indexes, and it is compared with existing denoising algorithms.

fNIRS motion artifact correction for overground walking using entropy based unbalanced optode decision and wavelet regression neural network

Functional near-infrared spectroscopy (fNIRS) can be employed to investigate brain activation by measuring the absorption of near-infrared light through an intact skull. fNIRS can measure hemoglobin signals, which are similar to functional magnetic resonance imaging (fMRI) blood-oxygen-level-dependent (BOLD) signals. The general linear model (GLM), which is a standard method for fMRI imaging, has been applied for fNIRS imaging analysis. However, when the subject moves, the fNIRS signal can contain artifacts during the measurement. These artifacts are called motion artifacts. However, the GLM has a drawback of failure because of motion artifacts. Recently, wavelet and hemodynamic response function based algorithms are popular detrending methods of motion artifact correction for fNIRS signals. However, these methods cannot show impressive performance in harsh environments such as overground walking tasks. This paper suggests a new motion artifact correction method that uses an entropy based unbalanced optode decision rule and a wavelet regression based back propagation neural network. Through the experiments, the performance of the proposed method was proven using graphic results, a brain activation map, and an objective performance index when compared with conventional detrending algorithms.

An application of common spatial pattern algorithm for accuracy improvement in classification of cortical activation pattern according to finger movement

In this paper, we tried to suggest feature extraction method using CSP Algorithm to improve the accuracy of classifier according to finger movement to develop the Upper Limb Rehabilitation Robot System based on brain signal. Four subjects participated in the experiment and they conducted four kind of tasks in three times. The task is divided by two kind of movement(Digit Flexion/Extension, Thumb Flexion/Extension), and two kind of mode(Active, Passive). We measured brain signal according to finger movement using fNIRS(functional Near Infrared Spectroscopy, FOIRE-3000, Shimadzu, Japan). Also, sampling rate of measured sign is 7.6923Hz and there are 24 channels. We conducted preprocessing process using HRF(Hemodynamic Response Function) and Wavelet-MDL(minimum description length) to remove the noise and global bias in selected signal. After preprocessing process, we extracted feature using CSP(Common Spatial Pattern) Algorithm and calculated the accuracy of classification in each task using Support Vector Machine(SVM). There is accuracy of classification result by applying CSP Algorithm. Accuracy of classification using SVM is about 69.53% and after applying CSP Algorithm is about 71.82%. we can find that it improved 2.29%.

Cortical activation pattern for finger movement: A feasibility study towards a fNIRS based BCI

Functional near-infrared spectroscopy (fNIRS) has become an established tool to investigate brain function and it is an interesting modality for brain computer interfaces (BCIs). This paper was aimed at examining whether fNIRS can be an appropriate BCIs modality. Brain activities of three subjects performing a right-hand finger movement (flex and extend the fingers and thumb) tasks were measured by fNIRS. And finger movement was carried out with active and passive modes. Passive movements were provided by a functional electrical stimulation (FES). Results demonstrated that while all movement modes commonly activated primary motor cortex, there were considerable differences between finger and thumb movement. The pattern of neural activation in active movement was resembled by passive movement. This result indicates that finger and thumb movement can be classified and then we saw possibility of the utilizing fNIRS into the field of BCIs.

Applications of Functional Near Infrared Spectroscopy as a Brain Optical Imaging Modality for Rehabilitation

A functional near-infrared spectroscopy (fNIRS) which is a non-invasive modality to measure hemodynamics of cortices-is today the frequently reported optical brain imaging method for rehabilitation. Neuroimaging studies such as with functional magnetic resonance imaging (fMRI) or positron emission tomography (PET) have been very confined due to a motion limitation of subject at their measuring facilities. But fNIRS can be quite suitable to study in the motor function task because it can measure brain activations on allowing subjects motion. The purpose of this paper is to provide a comprehensive approach for optimal strategies of motor rehabilitation. First, we will introduce the general information of fNIRS and the applications of fNIRS in rehabilitation tasks and effects are reviewed in this field so far. And we showed a feasibility study conducted to explore the experience of fNIRS in several motor executions and fNIRS integrated brain computer interaction. Finally, we provide a brief discussion of suggestions for fNIRS in rehabilitation. fNIRS is still a new technique in stroke rehabilitation, this review and our preliminary studies indicate that brain imaging using fNIRS has great potential to provide finding of brain function and organization under various motor recovery rehabilitation tasks after stroke.

Feasibility Study of a Functional Near Infrared Spectroscopy as a Brain Optical Imaging Modality for Rehabilitation Medicine

A functional near-infrared spectroscopy (fNIRS)—which is a non-invasive modality to measure hemodynamics of cortices—is today the frequently reported optical brain imaging method comparing with fMRI from the standpoint of clinical feasibility. The aim of this study was to explore the experience of fNIRS in several motor executions which cannot be implemented in an fMRI experimental condition, to describe their cortical activations, and consequently to examine the feasibility of fNIRS as an acceptable brain imaging technology for rehabilitation medicine. Five healthy men performed the individually given tasks. Five tasks were offered in this study: active hand grasping (hand flexion and extension), active arm raising (shoulder flexion and extension), active eating (ADL task of upper extremity), active knee bending (leg flexion and extension in sliding bench), and active walking (ADL task of lower extremity in treadmill). The fNIRS cortical map of each tasks coincided with the cortical areas where should be activated at each motor functions shown in many related literatures approving the same neurophysiological fact by fMRI or other brain imaging modalities. The results from this study may contribute to better understanding how motor executions can be expressed into cortical activation patterns via fNIRS measurement. The ability of fNIRS to image cortical activations at satisfactory spatiotemporal resolutions makes fNIRS a potentially powerful non-invasive brain imaging modality for diagnosis and evaluation of the motor performance for patients in rehabilitation medicine.