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.

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.

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.

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.