Feasibility of local interpretable model-agnostic explanations (LIME) algorithm as an effective and interpretable feature selection method: comparative fNIRS study.

Many feature selection methods have been evaluated in functional near-infrared spectroscopy (fNIRS)-related studies. The local interpretable model-agnostic explanation (LIME) algorithm is a feature selection method for fNIRS datasets that has not yet been validated; the demand for its validation is increasing. To this end, we assessed the feature selection performance of LIME for fNIRS datasets in terms of classification accuracy.

Classifying Children with ADHD Based on Prefrontal Functional Near-infrared Spectroscopy Using Machine Learning.

Objective: : Attention deficit hyperactivity disorder (ADHD) is a common neurodevelopmental disorder in children and adults characterized by cognitive and emotional self-control deficiencies. Previous functional near-infrared spectroscopy (fNIRS) studies found significant group differences between ADHD children and healthy controls during cognitive flexibility tasks in several brain regions. This study aims to apply a machine learning approach to identify medication-naive ADHD patients and healthy control (HC) groups using task-based fNIRS data.

Inter-Brain Synchrony Pattern Investigation on Triadic Board Game Play-Based Social Interaction: An fNIRS Study.

Recent advances in functional neuroimaging techniques, including methodologies such as fNIRS, have enabled the evaluation of inter-brain synchrony (IBS) induced by interpersonal interactions. However, the social interactions assumed in existing dyadic hyperscanning studies do not sufficiently emulate polyadic social interactions in the real world. Therefore, we devised an experimental paradigm that incorporates the Korean folk board game "Yut-nori" to reproduce social interactions that emulate social activities in the real world.

Subdividing Stress Groups into Eustress and Distress Groups Using Laterality Index Calculated from Brain Hemodynamic Response.

A stress group should be subdivided into eustress (low-stress) and distress (high-stress) groups to better evaluate personal cognitive abilities and mental/physical health. However, it is challenging because of the inconsistent pattern in brain activation. We aimed to ascertain the necessity of subdividing the stress groups.

Inter-Brain Synchrony Levels According to Task Execution Modes and Difficulty Levels: An fNIRS/GSR Study.

Hyperscanning is a brain imaging technique that measures brain synchrony caused by social interactions. Recent research on hyperscanning has revealed substantial inter-brain synchrony (IBS), but little is known about the link between IBS and mental workload. To study this link, we conducted an experiment consisting of button-pressing tasks of three different difficulty levels for the cooperation and competition modes with 56 participants aged 23.

Prediction of Oxygen Desaturation by Using Sound Data From a Noncontact Device: A Proof-of-Concept Study.

Objectives/hypothesis: Prediction of the apnea-hypopnea index (AHI) from breathing sounds during sleep could be used to prescreen for obstructive sleep apnea (OSA). In addition, the oxygen desaturation index (ODI) is a known risk factor for developing cardiovascular disease in OSA patients. This study focused on estimation of ODI from a noncontact manner from sleep breathing sounds.

Random Subspace Ensemble Learning for Functional Near-Infrared Spectroscopy Brain-Computer Interfaces.

The feasibility of the random subspace ensemble learning method was explored to improve the performance of functional near-infrared spectroscopy-based brain-computer interfaces (fNIRS-BCIs). Feature vectors have been constructed using the temporal characteristics of concentration changes in fNIRS chromophores such as mean, slope, and variance to implement fNIRS-BCIs systems. The mean and slope, which are the most popular features in fNIRS-BCIs, were adopted.

Adverse events related to electroacupuncture: a systematic review of single case studies and case series.

Objective: Electroacupuncture (EA) is used in the treatment of various diseases through the use of electrical stimulation. Reports of adverse events (AEs) associated with acupuncture are relatively consistent, but the safety of EA has been less well reported. In this systematic review, we provide a summary of the types of AEs related to EA in clinical practice.

Performance Improvement of Near-Infrared Spectroscopy-Based Brain-Computer Interface Using Regularized Linear Discriminant Analysis Ensemble Classifier Based on Bootstrap Aggregating.

Ensemble classifiers have been proven to result in better classification accuracy than that of a single strong learner in many machine learning studies. Although many studies on electroencephalography-brain-computer interface (BCI) used ensemble classifiers to enhance the BCI performance, ensemble classifiers have hardly been employed for near-infrared spectroscopy (NIRS)-BCIs. In addition, since there has not been any systematic and comparative study, the efficacy of ensemble classifiers for NIRS-BCIs remains unknown.

Toward a compact hybrid brain-computer interface (BCI): Performance evaluation of multi-class hybrid EEG-fNIRS BCIs with limited number of channels.

It has been demonstrated that the performance of typical unimodal brain-computer interfaces (BCIs) can be noticeably improved by combining two different BCI modalities. This so-called "hybrid BCI" technology has been studied for decades; however, hybrid BCIs that particularly combine electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) (hereafter referred to as hBCIs) have not been widely used in practical settings. One of the main reasons why hBCI systems are so unpopular is that their hardware is generally too bulky and complex.

Prediction of Apnea-Hypopnea Index Using Sound Data Collected by a Noncontact Device.

Objective: To predict the apnea-hypopnea index (AHI) in patients with obstructive sleep apnea (OSA) using data from breathing sounds recorded using a noncontact device during sleep.

Study Design: Prospective cohort study.

Setting: Tertiary referral hospital.

Water Resistances of Ag₂O-, BaO-, and CuO-Doped V₂O-P₂O-TeO₂ Glass Sealants.

V₂O-P₂O-TeO₂, a low-temperature vanadate-based glass sealant, was doped with metal oxides (MO = Ag₂O, BaO, or CuO), which generate Ag, Ba, and Cu ions, respectively, to strengthen the glass structure and improve its water resistance. These ions reduce the number of nonbridging oxygen atoms in the glass structure by forming V-O-M or P-O-M crosslinks in the V₂O-P₂O glass system. Structural analysis using Fourier-transform infrared spectroscopy indicated that the numbers of P-O-P, V═O, and V-O-V bonds decreased with increasing metal oxide content.

Prediction of Obstructive Sleep Apnea Based on Respiratory Sounds Recorded Between Sleep Onset and Sleep Offset.

Objectives: To develop a simple algorithm for prescreening of obstructive sleep apnea (OSA) on the basis of respiratory sounds recorded during polysomnography during all sleep stages between sleep onset and offset.

Methods: Patients who underwent attended, in-laboratory, full-night polysomnography were included. For all patients, audio recordings were performed with an air-conduction microphone during polysomnography.

On the Feasibility of Using an Ear-EEG to Develop an Endogenous Brain-Computer Interface.

Brain-computer interface (BCI) studies based on electroencephalography (EEG) measured around the ears (ear-EEGs) have mostly used exogenous paradigms involving brain activity evoked by external stimuli. The objective of this study is to investigate the feasibility of ear-EEGs for development of an endogenous BCI system that uses self-modulated brain activity. We performed preliminary and main experiments where EEGs were measured on the scalp and behind the ears to check the reliability of ear-EEGs as compared to scalp-EEGs.

Performance Prediction for a Near-Infrared Spectroscopy-Brain-Computer Interface Using Resting-State Functional Connectivity of the Prefrontal Cortex.

One of the most important issues in current brain-computer interface (BCI) research is the prediction of a user's BCI performance prior to the main BCI session because it would be useful to reduce the time required to determine the BCI paradigm best suited to that user. In electroencephalography (EEG)-BCI research, whether a user has low BCI performance toward a specific BCI paradigm has been estimated using a variety of resting-state EEG features. However, no previous study has attempted to predict the performance of near-infrared spectroscopy (NIRS)-BCI using resting-state NIRS data recorded before the main BCI experiment.

Improvement of Information Transfer Rates Using a Hybrid EEG-NIRS Brain-Computer Interface with a Short Trial Length: Offline and Pseudo-Online Analyses.

Electroencephalography (EEG) and near-infrared spectroscopy (NIRS) are non-invasive neuroimaging methods that record the electrical and metabolic activity of the brain, respectively. Hybrid EEG-NIRS brain-computer interfaces (hBCIs) that use complementary EEG and NIRS information to enhance BCI performance have recently emerged to overcome the limitations of existing unimodal BCIs, such as vulnerability to motion artifacts for EEG-BCI or low temporal resolution for NIRS-BCI. However, with respect to NIRS-BCI, in order to fully induce a task-related brain activation, a relatively long trial length (≥10 s) is selected owing to the inherent hemodynamic delay that lowers the information transfer rate (ITR; bits/min).

Eyes-closed hybrid brain-computer interface employing frontal brain activation.

Brain-computer interfaces (BCIs) have been studied extensively in order to establish a non-muscular communication channel mainly for patients with impaired motor functions. However, many limitations remain for BCIs in clinical use. In this study, we propose a hybrid BCI that is based on only frontal brain areas and can be operated in an eyes-closed state for end users with impaired motor and declining visual functions.

Change in radial artery pulse wave in stroke hemiplegic patients: Protocol for a case-control study.

Introduction: About 55% to 75% of stroke survivors have motor disorders and problems that affect their quality of life. The prevention of secondary neurological damages through relapse prevention and the rehabilitation of stroke patients suffering from morbidities are crucial to improve the prognosis of patients with stroke. Pulse examinations can be used to determine the stroke progression.

A Ternary Hybrid EEG-NIRS Brain-Computer Interface for the Classification of Brain Activation Patterns during Mental Arithmetic, Motor Imagery, and Idle State.

The performance of a brain-computer interface (BCI) can be enhanced by simultaneously using two or more modalities to record brain activity, which is generally referred to as a hybrid BCI. To date, many BCI researchers have tried to implement a hybrid BCI system by combining electroencephalography (EEG) and functional near-infrared spectroscopy (NIRS) to improve the overall accuracy of binary classification. However, since hybrid EEG-NIRS BCI, which will be denoted by hBCI in this paper, has not been applied to ternary classification problems, paradigms and classification strategies appropriate for ternary classification using hBCI are not well investigated.

Simultaneous acquisition of EEG and NIRS during cognitive tasks for an open access dataset.

We provide an open access multimodal brain-imaging dataset of simultaneous electroencephalography (EEG) and near-infrared spectroscopy (NIRS) recordings. Twenty-six healthy participants performed three cognitive tasks: 1) n-back (0-, 2- and 3-back), 2) discrimination/selection response task (DSR) and 3) word generation (WG) tasks. The data provided includes: 1) measured data, 2) demographic data, and 3) basic analysis results.

Performance enhancement of a brain-computer interface using high-density multi-distance NIRS.

This study investigated the effectiveness of using a high-density multi-distance source-detector (SD) separations in near-infrared spectroscopy (NIRS), for enhancing the performance of a functional NIRS (fNIRS)-based brain-computer interface (BCI). The NIRS system that was used for the experiment was capable of measuring signals from four SD separations: 15, 21.2, 30, and 33.

Integrative Medicine for Postoperative Patients: A Survey of Korean Medicine Doctors.

The purpose of this survey was to document the experience of Korean Medicine doctors (KMD) who provided postoperative care to patients through integrative medicine and to understand their opinions about integrative medicine utilization. Three researchers (two with a KMD license) of the Korea Institute of Oriental Medicine conducted the survey. The questionnaire was distributed via e-mail to the 17,041 members of the Association of Korean Medicine in 2015.

Ubiquitin-dependent regulation of Cdc42 by XIAP.

Rho GTPases control fundamental cellular processes and Cdc42 is a well-studied member of the family that controls filopodia formation and cell migration. Although the regulation of Cdc42 activity by nucleotide binding is well documented, the mechanisms driving its proteostasis are not clear. Here, we demonstrate that the highly conserved, RING domain containing E3 ubiquitin ligase XIAP controls the protein stability of Cdc42.

Hemodynamic changes caused by acupuncture in healthy volunteers: a prospective, single-arm exploratory clinical study.

Background: Radial pressure pulse wave (RPPW) examination has been a key diagnostic component of traditional Chinese medicine. The objective of this study was to investigate the changes in RPPW along with various hemodynamic variables after acupuncture stimulation and to examine the validity of pulse diagnosis as a modern diagnostic tool.

Methods: We conducted acupuncture stimulation at both ST36 acupuncture points in 25 healthy volunteers.