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. Therefore, to make hBCIs more appealing, it is necessary to implement a lightweight and compact hBCI system with minimal performance degradation. In this study, we investigated the feasibility of implementing a compact hBCI system with significantly less EEG channels and fNIRS source-detector (SD) pairs, but that can achieve a classification accuracy high enough to be used in practical BCI applications. EEG and fNIRS data were acquired while participants performed three different mental tasks consisting of mental arithmetic, right-hand motor imagery, and an idle state. Our analysis results showed that the three mental states could be classified with a fairly high classification accuracy of 77.6 ± 12.1% using an hBCI system with only two EEG channels and two fNIRS SD pairs.

Cortical volume and 40-Hz auditory-steady-state responses in patients with schizophrenia and healthy controls.

BACKGROUND: Abnormalities in the 40-Hz auditory steady-state response (ASSR) of the gamma range have been reported in schizophrenia (SZ) and are regarded as important pathophysiological features. Many of the previous studies reported diminished gamma oscillations in SZ, although some studies reported increased spontaneous gamma oscillations. Furthermore, brain morphological correlates of the gamma band ASSR deficits have rarely examined. We investigated different measures of the 40-Hz ASSR and their association with brain volumes and psychological measures of SZ. METHODS: The 40-Hz ASSR was measured for 80 dB click sounds (1 ms, 500-ms trains at 40-Hz, with 3050 to 3500 inter-train interval) using electroencephalography with 64 electrodes in 33 patients with SZ (male: 16, female: 17 (age range: 21-60)) and 30 healthy controls (HCs) (male: 13, female: 17 (age range: 23-64)). Four gamma oscillation measures (evoked power, spontaneous oscillations (baseline and total power), and inter-trial phase coherence (ITC)) were assessed. The source activities of the ASSR were also analyzed. Brain volumes were assessed using high-resolution magnetic resonance imaging and voxel-based morphometry and superior temporal gyrus (STG) volume measures were obtained. RESULTS: Patients with SZ had larger total and evoked powers and higher ITC than HCs. Both groups showed significantly different association between mean evoked power and right STG volume. In HCs but not SZ, mean evoked power showed significant positive correlation with right STG volume. In addition, the two groups showed significantly different association between verbal fluency and mean evoked power. High evoked power was significantly correlated with poor verbal fluency in SZ. CONCLUSIONS: The current study found increased gamma oscillation in SZ and suggests significant involvement of the STG in gamma oscillations.

Electroencephalography-based endogenous brain-computer interface for online communication with a completely locked-in patient.

BACKGROUND: Brain-computer interfaces (BCIs) have demonstrated the potential to provide paralyzed individuals with new means of communication, but an electroencephalography (EEG)-based endogenous BCI has never been successfully used for communication with a patient in a completely locked-in state (CLIS). METHODS: In this study, we investigated the possibility of using an EEG-based endogenous BCI paradigm for online binary communication by a patient in CLIS. A female patient in CLIS participated in this study. She had not communicated even with her family for more than one year with complete loss of motor function. Offline and online experiments were conducted to validate the feasibility of the proposed BCI system. In the offline experiment, we determined the best combination of mental tasks and the optimal classification strategy leading to the best performance. In the online experiment, we investigated whether our BCI system could be potentially used for real-time communication with the patient. RESULTS: An online classification accuracy of 87.5% was achieved when Riemannian geometry-based classification was applied to real-time EEG data recorded while the patient was performing one of two mental-imagery tasks for 5 s. CONCLUSIONS: Our results suggest that an EEG-based endogenous BCI has the potential to be used for online communication with a patient in CLIS.

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. Here we propose the use of an hBCI for the classification of three brain activation patterns elicited by mental arithmetic, motor imagery, and idle state, with the aim to elevate the information transfer rate (ITR) of hBCI by increasing the number of classes while minimizing the loss of accuracy. EEG electrodes were placed over the prefrontal cortex and the central cortex, and NIRS optodes were placed only on the forehead. The ternary classification problem was decomposed into three binary classification problems using the "one-versus-one" (OVO) classification strategy to apply the filter-bank common spatial patterns filter to EEG data. A 10 × 10-fold cross validation was performed using shrinkage linear discriminant analysis (sLDA) to evaluate the average classification accuracies for EEG-BCI, NIRS-BCI, and hBCI when the meta-classification method was adopted to enhance classification accuracy. The ternary classification accuracies for EEG-BCI, NIRS-BCI, and hBCI were 76.1 ± 12.8, 64.1 ± 9.7, and 82.2 ± 10.2%, respectively. The classification accuracy of the proposed hBCI was thus significantly higher than those of the other BCIs (p < 0.005). The average ITR for the proposed hBCI was calculated to be 4.70 ± 1.92 bits/minute, which was 34.3% higher than that reported for a previous binary hBCI study.

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.

Integrative Evaluation of Automated Massage Combined with Thermotherapy: Physical, Physiological, and Psychological Viewpoints.

Various types of massages are reported to relieve stress, pain, and anxiety which are beneficial for rehabilitation; however, more comprehensive studies are needed to understand the mechanism of massage therapy. In this study, we investigated the effect of massage therapy, alone or in combination with infrared heating, on 3 different aspects: physical, physiological, and psychological. Twenty-eight healthy university students were subjected to 3 different treatment conditions on separate days, one condition per day: control, massage only, or massage with infrared heating. Physical (trunk extension [TE]; maximum power of erector spinae), physiological (heart-rate variability [HRV]; electroencephalogram [EEG]), and psychological (state-trait anxiety inventory [STAI]; visual analogue scale [VAS]) measurements were evaluated and recorded before and after each treatment condition. The results showed that massage therapy, especially when combined with infrared heating, significantly improved physical functioning, increased parasympathetic response, and decreased psychological stress and anxiety. In the current study, we observed that massage therapy contributes to various physical, physiological, and psychological changes, where the effect increases with thermotherapy.

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.

Disruptions in small-world cortical functional connectivity network during an auditory oddball paradigm task in patients with schizophrenia.

P300 deficits in patients with schizophrenia have previously been investigated using EEGs recorded during auditory oddball tasks. However, small-world cortical functional networks during auditory oddball tasks and their relationships with symptom severity scores in schizophrenia have not yet been investigated. In this study, the small-world characteristics of source-level functional connectivity networks of EEG responses elicited by an auditory oddball paradigm were evaluated using two representative graph-theoretical measures, clustering coefficient and path length. EEG signals from 34 patients with schizophrenia and 34 healthy controls were recorded while each subject was asked to attend to oddball tones. The results showed reduced clustering coefficients and increased path lengths in patients with schizophrenia, suggesting that the small-world functional network is disrupted in patients with schizophrenia. In addition, the negative and cognitive symptom components of positive and negative symptom scales were negatively correlated with the clustering coefficient and positively correlated with path length, demonstrating that both indices are indicators of symptom severity in patients with schizophrenia. Our study results suggest that disrupted small-world characteristics are potential biomarkers for patients with schizophrenia.

Causal influence of epileptic network during spike-and-wave discharge in juvenile myoclonic epilepsy.

Electroencephalographic (EEG) characteristic of juvenile myoclonic epilepsy (JME) is spike-and-wave discharge (SWD), which is dominant in the frontal region. However, activity in the parietal area, including the precuneus, has also been documented for several seconds before and during SWD. The aim of this study was to identify the role of the parietal region, especially the precuneus, and to clarify the causal dynamics among cortical regions during SWD. EEGs were obtained from seven patients with JME. Each SWD was divided into six distinct temporal phases: spike onset, spike peak, slow-wave onset, slow-wave ascending, slow-wave peak, and slow-wave descending phases. Based on the cortical current source distribution and the results of a previous study, we selected the medial frontal, orbitofrontal, anterior cingulate, and mesial temporal cortices and the precuneus as regions of interest (ROIs). To assess epileptic networks and the causal relationships among ROIs during SWD, the directed transfer function (DTF), a measure of multivariate causality, was calculated for each phase of SWD. During spike onset, the maximal outdegree region in all patients was the precuneus. The spike-peak and slow-wave onset phases did not show a consistently dominant outflow region. Outflow from the anterior cingulate cortex was dominant in four patients during the slow-wave ascending phase, and the precuneus showed the maximal outdegree in six patients during the slow-wave peak. In the slow-wave descending phase, four patients showed maximal outflow from the temporal cortex. Our findings suggest that the precuneus is likely a key region for SWD despite the small amount of neural activity observed. The precuneus was the region with the maximal outdegree during both the spike onset and slow-wave peak phases, indicating that SWD in JME is initiated and sustained by a network involving the frontal cortex, precuneus, and thalamus.

Inconsistent outcomes of transcranial direct current stimulation (tDCS) may be originated from the anatomical differences among individuals: a simulation study using individual MRI data.

Transcranial direct current stimulation (tDCS) is a kind of neuromodulation protocol, which transmits small amount of DC currents through scalp electrodes to facilitate or inhibit particular areas of the brain. Although many studies have demonstrated that tDCS can effectively modulate excitability of various brain sites, the outcomes of the tDCS treatment are not consistent among subjects to whom identical electrode montages were applied. So far, no studies have clearly elucidated the main cause of this individual variability. The hypothesis of our study was that the individual variability in the tDCS effect might be originated due to the anatomical differences among subjects. To verify our hypothesis, we investigated the relationship between the current density value at dorsolateral prefrontal cortex (DLPFC) simulated using finite element method (FEM) and the behavioral outcomes of a simple working memory (WM) task. A 3-back WM task experiment was conducted with twenty-five healthy subjects before and after the DC stimulation, when the cathode and anode electrodes were attached to right supraorbital area and F3 location, respectively, for all subjects. The results showed that participants who showed enhanced WM task performance after tDCS had a significantly larger current density on DLPFC, suggesting that the inconsistent behavioral outcomes of tDCS might be partially due to the anatomical differences among subjects.

An image-guided transcranial direct current stimulation system: a pilot phantom study.

In this study, an image-guided transcranial direct current stimulation (IG-tDCS) system that can deliver an increased stimulation current to a target brain area without the need to adjust the location of an active electrode was implemented. This IG-tDCS system was based on the array-type tDCS concept, which was validated through computer simulations in a previous study. Unlike a previous study, the present IG-tDCS system adopts a single reference electrode and an active electrode array consisting of 16 (4 × 4) sub-electrodes. The proposed IG-tDCS system is capable of shaping current flow inside the human head by controlling the input currents of the arrayed electrodes. Once a target brain area has been selected, the optimal injection current of each arrayed sub-electrode is evaluated automatically using a genetic algorithm in order to deliver the maximum available current to the target area. The operation of our pilot system was confirmed through a simple phantom experiment.

An EEG-based real-time cortical functional connectivity imaging system.

In the present study, we introduce an EEG-based, real-time, cortical functional connectivity imaging system capable of monitoring and tracing dynamic changes in cortical functional connectivity between different regions of interest (ROIs) on the brain cortical surface. The proposed system is based on an EEG-based dynamic neuroimaging system, which is capable of monitoring spatiotemporal changes of cortical rhythmic activity at a specific frequency band by conducting real-time cortical source imaging. To verify the implemented system, we performed three test experiments in which we monitored temporal changes in cortical functional connectivity patterns in various frequency bands during structural face processing, finger movements, and working memory task. We also traced the changes in the number of connections between all possible pairs of ROIs whose correlations exceeded a predetermined threshold. The quantitative analysis results were consistent with those of previous off-line studies, thereby demonstrating the possibility of imaging cortical functional connectivity in real-time. We expect our system to be applicable to various potential applications, including real-time diagnosis of psychiatric diseases and EEG neurofeedback.

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.