The effects on mental fatigue and the cognitive function of mechanical massage and binaural beats (brain massage) provided by massage chairs.

OBJECTIVE: To verify whether the mechanical massage using massage chairs and binaural beats (brain massage) affect the mental fatigue recovery and cognitive enhancements. METHODS: 25 healthy adults used massage chairs that could provide mechanical massage and binaural beats (brain massage) for 20 min. Mental fatigue and cognitive function were assessed before and after receiving brain massage using electroencephalogram (EEG) and 5 prolonged cognitive tests. RESULTS: When a person received a brain massage on the massage chair, the decrease in mental fatigue was statistically significant compared to taking a rest or receiving a mechanical massage only on the massage chair. In addition, sustained attention, verbal short-term and long-term memory and non-verbal long-term memory were statistically significantly increased after using brain massage. CONCLUSION: Brain massage (mechanical massage and binaural beats) are effective in reducing mental fatigue and improving the cognitive function.

Assessment of user voluntary engagement during neurorehabilitation using functional near-infrared spectroscopy: a preliminary study.

BACKGROUND: Functional near infrared spectroscopy (fNIRS) finds extended applications in a variety of neuroscience fields. We investigated the potential of fNIRS to monitor voluntary engagement of users during neurorehabilitation, especially during combinatory exercise (CE) that simultaneously uses both, passive and active exercises. Although the CE approach can enhance neurorehabilitation outcome, compared to the conventional passive or active exercise strategies, the active engagement of patients in active motor movements during CE is not known. METHODS: We determined hemodynamic responses induced by passive exercise and CE to evaluate the active involvement of users during CEs using fNIRS. In this preliminary study, hemodynamic responses of eight healthy subjects during three different tasks (passive exercise alone, passive exercise with motor imagery, and passive exercise with active motor execution) were recorded. On obtaining statistically significant differences, we classified the hemodynamic responses induced by passive exercise and CEs to determine the identification accuracy of the voluntary engagement of users using fNIRS. RESULTS: Stronger and broader activation around the sensorimotor cortex was observed during CEs, compared to that during passive exercise. Moreover, pattern classification results revealed more than 80% accuracy. CONCLUSIONS: Our preliminary study demonstrated that fNIRS can be potentially used to assess the engagement of users of the combinatory neurorehabilitation strategy.

Global Electroencephalography Synchronization as a New Indicator for Tracking Emotional Changes of a Group of Individuals during Video Watching.

In the present study, we investigated whether global electroencephalography (EEG) synchronization can be a new promising index for tracking emotional arousal changes of a group of individuals during video watching. Global field synchronization (GFS), an index known to correlate with human cognitive processes, was evaluated; this index quantified the global temporal synchronization among multichannel EEG data recorded from a group of participants (n = 25) during the plays of two short video clips. The two video clips were each about 5 min long and were designed to evoke negative (fearful) or positive (happy) emotion, respectively. Another group of participants (n = 37) was asked to select the two most emotionally arousing (most touching or most fearful) scenes in each clip. The results of these questionnaire surveys were used as the ground-truth to evaluate whether the GFS could detect emotional highlights of both video clips. The emotional highlights estimated using the grand-averaged GFS waveforms of the first group were also compared with those evaluated from galvanic skin response, photoplethysmography, and multimedia content analysis, which are conventional methods used to estimate temporal changes in emotional arousal during video plays. From our results, we found that beta-band GFS values decreased during high emotional arousal, regardless of the type of emotional stimulus. Moreover, the emotional highlights estimated using the GFS waveforms coincided best with those found by the questionnaire surveys. These findings suggest that GFS might be applicable as a new index for tracking emotional arousal changes of a group of individuals during video watching, and is likely to be used to evaluate or edit movies, TV commercials, and other broadcast products.

An emergency call system for patients in locked-in state using an SSVEP-based brain switch.

Patients in a locked-in state (LIS) due to severe neurological disorders such as amyotrophic lateral sclerosis (ALS) require seamless emergency care by their caregivers or guardians. However, it is a difficult job for the guardians to continuously monitor the patients' state, especially when direct communication is not possible. In the present study, we developed an emergency call system for such patients using a steady-state visual evoked potential (SSVEP)-based brain switch. Although there have been previous studies to implement SSVEP-based brain switch system, they have not been applied to patients in LIS, and thus their clinical value has not been validated. In this study, we verified whether the SSVEP-based brain switch system can be practically used as an emergency call system for patients in LIS. The brain switch used for our system adopted a chromatic visual stimulus, which proved to be visually less stimulating than conventional checkerboard-type stimuli but could generate SSVEP responses strong enough to be used for brain-computer interface (BCI) applications. To verify the feasibility of our emergency call system, 14 healthy participants and 3 patients with severe ALS took part in online experiments. All three ALS patients successfully called their guardians to their bedsides in about 6.56 seconds. Furthermore, additional experiments with one of these patients demonstrated that our emergency call system maintains fairly good performance even up to 4 weeks after the first experiment without renewing initial calibration data. Our results suggest that our SSVEP-based emergency call system might be successfully used in practical scenarios.

Clinical feasibility of brain-computer interface based on steady-state visual evoked potential in patients with locked-in syndrome: Case studies.

Although the feasibility of brain-computer interface (BCI) systems based on steady-state visual evoked potential (SSVEP) has been extensively investigated, only a few studies have evaluated its clinical feasibility in patients with locked-in syndrome (LIS), who are the main targets of BCI technology. The main objective of this case report was to share our experiences of SSVEP-based BCI experiments involving five patients with LIS, thereby providing researchers with useful information that can potentially help them to design BCI experiments for patients with LIS. In our experiments, a four-class online SSVEP-based BCI system was implemented and applied to four of five patients repeatedly on multiple days to investigate its test-retest reliability. In the last experiments with two of the four patients, the practical usability of our BCI system was tested using a questionnaire survey. All five patients showed clear and distinct SSVEP responses at all four fundamental stimulation frequencies (6, 6.66, 7.5, 10 Hz), and responses at harmonic frequencies were also observed in three patients. Mean classification accuracy was 76.99% (chance level = 25%). The test-retest reliability experiments demonstrated stable performance of our BCI system over different days even when the initial experimental settings (e.g., electrode configuration, fixation time, visual angle) used in the first experiment were used without significant modifications. Our results suggest that SSVEP-based BCI paradigms might be successfully used to implement clinically feasible BCI systems for severely paralyzed patients.

Data-Driven User Feedback: An Improved Neurofeedback Strategy considering the Interindividual Variability of EEG Features.

It has frequently been reported that some users of conventional neurofeedback systems can experience only a small portion of the total feedback range due to the large interindividual variability of EEG features. In this study, we proposed a data-driven neurofeedback strategy considering the individual variability of electroencephalography (EEG) features to permit users of the neurofeedback system to experience a wider range of auditory or visual feedback without a customization process. The main idea of the proposed strategy is to adjust the ranges of each feedback level using the density in the offline EEG database acquired from a group of individuals. Twenty-two healthy subjects participated in offline experiments to construct an EEG database, and five subjects participated in online experiments to validate the performance of the proposed data-driven user feedback strategy. Using the optimized bin sizes, the number of feedback levels that each individual experienced was significantly increased to 139% and 144% of the original results with uniform bin sizes in the offline and online experiments, respectively. Our results demonstrated that the use of our data-driven neurofeedback strategy could effectively increase the overall range of feedback levels that each individual experienced during neurofeedback training.

An unsupervised eye blink artifact detection method for real-time electroencephalogram processing.

Electroencephalogram (EEG) is easily contaminated by unwanted physiological artifacts, among which electrooculogram (EOG) artifacts due to eye blinking are known to be most dominant. The eye blink artifacts are reported to affect theta and alpha rhythms of frontal EEG signals, and hard to be accurately detected in an unsupervised way due to large individual variability. In this study, we propose a new method for detecting eye blink artifacts automatically in real time without using any labeled training data. The proposed method combined our previous method for detecting eye blink artifacts based on digital filters with an automatic thresholding algorithm. The proposed method was evaluated using EEG data acquired from 24 participants. Two conventional algorithms were implemented and their performances were compared with that of the proposed method. The main contributions of this study are (1) confirming that individual thresholding is necessary for artifact detection, (2) proposing a novel algorithm structure to detect blink artifacts in a real-time environment without any a priori knowledge, and (3) demonstrating that the length of training data can be minimized through the use of a real-time adaption procedure.

Evaluation of various mental task combinations for near-infrared spectroscopy-based brain-computer interfaces.

A number of recent studies have demonstrated that near-infrared spectroscopy (NIRS) is a promisingneuroimaging modality for brain-computer interfaces (BCIs). So far, most NIRS-based BCI studies have focusedon enhancing the accuracy of the classification of different mental tasks. In the present study, we evaluated theperformances of a variety of mental task combinations in order to determine the mental task pairs that are bestsuited for customized NIRS-based BCIs. To this end, we recorded event-related hemodynamic responses whileseven participants performed eight different mental tasks. Classification accuracies were then estimated for allpossible pairs of the eight mental tasks (8C2 = 28). Based on this analysis, mental task combinations with relatively high classification accuracies frequently included the following three mental tasks: "mental multiplication," "mental rotation," and "right-hand motor imagery." Specifically, mental task combinations consisting of two of these three mental tasks showed the highest mean classification accuracies. It is expected that our results will be a useful reference to reduce the time needed for preliminary tests when discovering individual-specific mental task combinations.

Development of an "eyes-closed" brain-computer interface system for communication of patients with oculomotor impairment.

The goal of this study was to develop a new steady-state visual evoked potential (SSVEP)-based BCI system, which can be applied to disabled individuals with impaired oculomotor function. The developed BCI system allows users to express their binary intentions without needing to open their eyes. To present visual stimuli, we used a pair of glasses with two LEDs flickering at different frequencies. EEG spectral patterns were classified in real time while participants were attending to one of the presented visual stimuli with their eyes closed. Through offline experiments performed with 11 healthy participants, we confirmed that SSVEP responses could be modulated by visual selective attention to a specific light stimulus penetrating through the eyelids, and could be classified with accuracy high enough for use in a practical BCI system. After customizing the parameters of the proposed SSVEP-based BCI paradigm based on the offline analysis results, binary intentions of five healthy participants and one locked-in state patient were classified online. The average ITR of the online experiments reached to 10.83 bits/min with an average accuracy of 95.3 %. An online experiment applied to a patient with ALS showed a classification accuracy of 80 % and an ITR of 2.78 bits/min, demonstrating the practical feasibility of our BCI paradigm.

Development of a hybrid mental speller combining EEG-based brain-computer interface and webcam-based eye-tracking.

The main goal of this study was to develop a hybrid mental spelling system combining a steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) technology and a webcam-based eye-tracker, which utilizes information from the brain electrical activity and eye gaze direction at the same time. In the hybrid mental spelling system, a character decoded using SSVEP was not typed if the position of the selected character was not matched with the eye direction information ('left' or 'right') obtained from the eye-tracker. Thus, the users did not need to correct a misspelled character using a 'BACKSPACE' key. To verify the feasibility of the developed hybrid mental spelling system, we conducted online experiments with ten healthy participants. Each participant was asked to type 15 English words consisting of 68 characters. As a result, 16.6 typing errors could be prevented on average, demonstrating that the implemented hybrid mental spelling system could enhance the practicality of our mental spelling system.

Classification of binary intentions for individuals with impaired oculomotor function: 'eyes-closed' SSVEP-based brain-computer interface (BCI).

OBJECTIVE: Some patients suffering from severe neuromuscular diseases have difficulty controlling not only their bodies but also their eyes. Since these patients have difficulty gazing at specific visual stimuli or keeping their eyes open for a long time, they are unable to use the typical steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) systems. In this study, we introduce a new paradigm for SSVEP-based BCI, which can be potentially suitable for disabled individuals with impaired oculomotor function. APPROACH: The proposed electroencephalography (EEG)-based BCI system allows users to express their binary intentions without needing to open their eyes. A pair of glasses with two light emitting diodes flickering at different frequencies was used to present visual stimuli to participants with their eyes closed, and we classified the recorded EEG patterns in the online experiments conducted with five healthy participants and one patient with severe amyotrophic lateral sclerosis (ALS). MAIN RESULTS: Through offline experiments performed with 11 participants, we confirmed that human SSVEP could be modulated by visual selective attention to a specific light stimulus penetrating through the eyelids. Furthermore, the recorded EEG patterns could be classified with accuracy high enough for use in a practical BCI system. After customizing the parameters of the proposed SSVEP-based BCI paradigm based on the offline analysis results, binary intentions of five healthy participants were classified in real time. The average information transfer rate of our online experiments reached 10.83 bits min(-1). A preliminary online experiment conducted with an ALS patient showed a classification accuracy of 80%. SIGNIFICANCE: The results of our offline and online experiments demonstrated the feasibility of our proposed SSVEP-based BCI paradigm. It is expected that our 'eyes-closed' SSVEP-based BCI system can be potentially used for communication of disabled individuals with impaired oculomotor function.

Evaluation of feature extraction methods for EEG-based brain-computer interfaces in terms of robustness to slight changes in electrode locations.

To date, most EEG-based brain-computer interface (BCI) studies have focused only on enhancing BCI performance in such areas as classification accuracy and information transfer rate. In practice, however, test-retest reliability of the developed BCI systems must also be considered for use in long-term, daily life applications. One factor that can affect the reliability of BCI systems is the slight displacement of EEG electrode locations that often occurs due to the removal and reattachment of recording electrodes. The aim of this study was to evaluate and compare various feature extraction methods for motor-imagery-based BCI in terms of robustness to slight changes in electrode locations. To this end, EEG signals were recorded from three reference electrodes (Fz, C3, and C4) and from six additional electrodes located close to the reference electrodes with a 1-cm inter-electrode distance. Eight healthy participants underwent 180 trials of left- and right-hand motor imagery tasks. The performance of four different feature extraction methods [power spectral density (PSD), phase locking value (PLV), a combination of PSD and PLV, and cross-correlation (CC)] were evaluated using five-fold cross-validation and linear discriminant analysis, in terms of robustness to electrode location changes as well as regarding absolute classification accuracy. The quantitative evaluation results demonstrated that the use of either PSD- or CC-based features led to higher classification accuracy than the use of PLV-based features, while PSD-based features showed much higher sensitivity to changes in EEG electrode location than CC- or PLV-based features. Our results suggest that CC can be used as a promising feature extraction method in motor-imagery-based BCI studies, since it provides high classification accuracy along with being little affected by slight changes in the EEG electrode locations.

Development of an SSVEP-based BCI spelling system adopting a QWERTY-style LED keyboard.

In this study, we introduce a new mental spelling system based on steady-state visual evoked potential (SSVEP), adopting a QWERTY style layout keyboard with 30 LEDs flickering with different frequencies. The proposed electroencephalography (EEG)-based mental spelling system allows the users to spell one target character per each target selection, without the need for multiple step selections adopted by conventional SSVEP-based mental spelling systems. Through preliminary offline experiments and online experiments, we confirmed that human SSVEPs elicited by visual flickering stimuli with a frequency resolution of 0.1 Hz could be classified with classification accuracy high enough to be used for a practical brain-computer interface (BCI) system. During the preliminary offline experiments performed with five participants, we optimized various factors influencing the performance of the mental spelling system, such as distances between adjacent keys, light source arrangements, stimulating frequencies, recording electrodes, and visual angles. Additional online experiments were conducted with six participants to verify the feasibility of the optimized mental spelling system. The results of the online experiments were an average typing speed of 9.39 letters per minute (LPM) with an average success rate of 87.58%, corresponding to an average information transfer rate of 40.72 bits per minute, demonstrating the high performance of the developed mental spelling system. Indeed, the average typing speed of 9.39 LPM attained in this study was one of the best LPM results among those reported in previous BCI literatures.

Classification of selective attention to auditory stimuli: toward vision-free brain-computer interfacing.

Brain-computer interface (BCI) is a developing, novel mode of communication for individuals with severe motor impairments or those who have no other options for communication aside from their brain signals. However, the majority of current BCI systems are based on visual stimuli or visual feedback, which may not be applicable for severe locked-in patients that have lost their eyesight or the ability to control their eye movements. In the present study, we investigated the feasibility of using auditory steady-state responses (ASSRs), elicited by selective attention to a specific sound source, as an electroencephalography (EEG)-based BCI paradigm. In our experiment, two pure tone burst trains with different beat frequencies (37 and 43 Hz) were generated simultaneously from two speakers located at different positions (left and right). Six participants were instructed to close their eyes and concentrate their attention on either auditory stimulus according to the instructions provided randomly through the speakers during the inter-stimulus interval. EEG signals were recorded at multiple electrodes mounted over the temporal, occipital, and parietal cortices. We then extracted feature vectors by combining spectral power densities evaluated at the two beat frequencies. Our experimental results showed high classification accuracies (64.67%, 30 commands/min, information transfer rate (ITR) = 1.89 bits/min; 74.00%, 12 commands/min, ITR = 2.08 bits/min; 82.00%, 6 commands/min, ITR = 1.92 bits/min; 84.33%, 3 commands/min, ITR = 1.12 bits/min; without any artifact rejection, inter-trial interval = 6s), enough to be used for a binary decision. Based on the suggested paradigm, we implemented a first online ASSR-based BCI system that demonstrated the possibility of materializing a totally vision-free BCI system.

A vision-free brain-computer interface (BCI) paradigm based on auditory selective attention.

Majority of the recently developed brain computer interface (BCI) systems have been using visual stimuli or visual feedbacks. However, the BCI paradigms based on visual perception might not be applicable to severe locked-in patients who have lost their ability to control their eye movement or even their vision. In the present study, we investigated the feasibility of a vision-free BCI paradigm based on auditory selective attention. We used the power difference of auditory steady-state responses (ASSRs) when the participant modulates his/her attention to the target auditory stimulus. The auditory stimuli were constructed as two pure-tone burst trains with different beat frequencies (37 and 43 Hz) which were generated simultaneously from two speakers located at different positions (left and right). Our experimental results showed high classification accuracies (64.67%, 30 commands/min, information transfer rate (ITR) = 1.89 bits/min; 74.00%, 12 commands/min, ITR = 2.08 bits/min; 82.00%, 6 commands/min, ITR = 1.92 bits/min; 84.33%, 3 commands/min, ITR = 1.12 bits/min; without any artifact rejection, inter-trial interval = 6 sec), enough to be used for a binary decision. Based on the suggested paradigm, we implemented a first online ASSR-based BCI system that demonstrated the possibility of materializing a totally vision-free BCI system.

Effects of anti-asthma therapy on dyspnea perception in acute asthma patients.

Blunted perception of dyspnea may predispose patients to fatal asthma attacks. To examine whether this impaired perception of dyspnea in patients with acute asthma could be corrected by anti-asthma therapy, the medical records of 104 consecutive asthma patients who had been hospitalized as a result of asthma attacks were analyzed retrospectively. During the course of treatment with conventional asthma medications, the forced expiratory volume in 1s (FEV1) and the Borg scale-based dyspnea perception scores during breathing through an inspiratory muscle trainer were measured at least twice. The baseline Borg score measured just before discharge was significantly lower than from that measured initially, regardless of improvement in FEV1. In contrast, the Borg score at the highest resistance (HR; 3.12+/-0.26 vs. 5.03+/-0.53; P<0.01) and the HR-induced DeltaBorg score (1.68+/-0.20 vs. 4.47+/-0.54, P<0.001) were increased significantly in the Poor Perceivers (Borg score 5 at HR and HR-induced DeltaBorg score 3). Patient age (r=0.363, P<0.001), blood eosinophil counts (r=-0.285, P<0.01), and serum total IgE levels (r=-0.213, P<0.05), but not FEV1, were significantly related to the effect of the treatment on the HR-induced DeltaBorg scores. These findings suggest that anti-asthma treatments decrease dyspnea even without a concomitant improvement in lung function and correct the impaired perception of inspiratory resistive load in acute asthma, and that age and allergy influence the effect of treatment on impaired perception.