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Abstract Brain-computer interface (BCI) has helped people by allowing them to control a computer or machine through brain activity without actual body movement. Despite this advantage, BCI cannot be used widely because some people cannot achieve controllable performance. To solve this problem, researchers have proposed stimulation methods to modulate relevant brain activity to improve BCI performance. However, multiple studies have reported mixed results following stimulation, and the comparative study of different stimulation modalities has been overlooked. Accordingly, this study was designed to compare vibrotactile stimulation and transcranial direct current stimulation’s (tDCS) effects on brain activity modulation and motor imagery BCI performance among inefficient BCI users. We recruited 44 subjects and divided them into sham, vibrotactile stimulation, and tDCS groups, and low performers were selected from each stimulation group. We found that the latter’s BCI performance in the vibrotactile stimulation group increased significantly by 9.13% (p

Measurement(s) Electroencephalography Technology Type(s) electroencephalography (EEG) Sample Characteristic - Organism Homo sapiens

The demand for public datasets has increased as data-driven methodologies have been introduced in the field of brain-computer interfaces (BCIs). Indeed, many BCI datasets are available in various platforms or repositories on the web, and the studies that have employed these datasets appear to be increasing. Motor imagery is one of the significant control paradigms in the BCI field, and many datasets related to motor tasks are open to the public already. However, to the best of our knowledge, these studies have yet to investigate and evaluate the datasets, although data quality is essential for reliable results and the design of subject− or system-independent BCIs. In this study, we conducted a thorough investigation of motor imagery/execution EEG datasets recorded from healthy participants published over the past 13 years. The 25 datasets were collected from six repositories and subjected to a meta-analysis. In particular, we reviewed the specifications of the recording settings and experimental design, and evaluated the data quality measured by classification accuracy from standard algorithms such as Common Spatial Pattern (CSP) and Linear Discriminant Analysis (LDA) for comparison and compatibility across the datasets. As a result, we found that various stimulation types, such as text, figure, or arrow, were used to instruct subjects what to imagine and the length of each trial also differed, ranging from 2.5 to 29 s with a mean of 9.8 s. Typically, each trial consisted of multiple sections: pre-rest (2.38 s), imagination ready (1.64 s), imagination (4.26 s, ranging from 1 to 10 s), the post-rest (3.38 s). In a meta-analysis of the total of 861 sessions from all datasets, the mean classification accuracy of the two-class (left-hand vs. right-hand motor imagery) problem was 66.53%, and the population of the BCI poor performers, those who are unable to reach proficiency in using a BCI system, was 36.27% according to the estimated accuracy distribution. Further, we analyzed the CSP features and found that each dataset forms a cluster, and some datasets overlap in the feature space, indicating a greater similarity among them. Finally, we checked the minimal essential information (continuous signals, event type/latency, and channel information) that should be included in the datasets for convenient use, and found that only 71% of the datasets met those criteria. Our attempts to evaluate and compare the public datasets are timely, and these results will contribute to understanding the dataset’s quality and recording settings as well as the use of using public datasets for future work on BCIs.

Each person has his or her own distinct event-related potential (ERP) signals. Thus, traditional brain-computer interface (BCI) systems require a calibration process in which the subject's data are extracted in order to train machine-learning classifiers. Despite past efforts to eliminate this process, often referred to as “zero-training,” BCI systems' best performance is achievable only with some level of calibration. This tedious process is one of the factors that have limited the use of BCI systems in the real world. Meanwhile, convolutional neural networks (CNN) have been proven to be useful in distinguishing neurophysiological features. In this study, we investigated whether an existing convolutional neural network (CNN) combined with large ERP samples (n = 99,000) can achieve zero-training in a P300 BCI speller system. As a result, the zero-trained CNN achieved comparable performance (89%, p 0.05) with calibration. Additionally, we illustrate a hybrid approach in order to further enhance performance, which adaptively updates a linear classifier using label information generated from a zero-trained CNN. With this technique, the hybrid approach achieved reasonable performance (92%), showing no statistical difference (p > 0.05) when compared to the traditional approach in the same offline data.

This study presents an automatic sleep-stage classification system based on utilizing compressive sensing (CS) for data reduction. The amount of electroencephalogram (EEG) signal data required for sleep-stage classification can be significantly reduced by applying CS at the cost of distortion in the reconstructed EEG signal. A neural network trained on features extracted from the reconstructed EEG signal was implemented as a classifier to avoid compromising the accuracy of classification in the presence of distortion in the reconstructed EEG signal. A radial basis function (RBF) neural network that uses a simple Manhattan distance function instead of complicated computation, such as vector multiplication matrix (VMM), was selected to reduce the hardware complexity. A classification method that utilizes information from the previous sleep stage based on the unique nature of human sleep was also presented and implemented on the proposed RBF classifier for further improvement of the classification accuracy. The classification system has been evaluated using EEG data from eight subjects in the Cyclic Alternating Pattern (CAP) sleep dataset. The measurement results showed that the classification system achieved high classification accuracy comparable to the previously reported sleep-stage classifiers that do not utilize signal compression.

Transcranial direct current stimulation (tDCS) is an emerging non-invasive neuromodulation method that is convenient and popular for clinical use. However, there is a practical issue when tDCS is applied to actual subjects, in which the geometrical variation in each model influences the effect of the predicted electric field (EF) distribution tDCS induces, and thus, may evoke unexpected EF distributions. In this paper, we investigated the effect of geometrical variations in the conventional two-pad tDCS and multi-array tDCS. For comparison, we constructed five spherical models of various thicknesses with cerebrospinal fluid and skull, as well as three anatomical head models. Thereafter, tDCS’ stimulation effects in the primary motor cortex (Brodmann area 4) were compared with respect to the EFs induced. We observed that geometrical variation’s effect is obvious for both forms of tDCS; but regardless of inter-subject variability, the multi-array tDCS montage may yield induced EFs of comparable or higher intensity and far greater focality. Thus, the multi-array tDCS is expected to have great potential to overcome inter-subject variability.

Acoustic and electrical brain stimulations are techniques well known to enhance memory consolidation by driving slow oscillations (SO,

Brain-computer interface (BCI) systems were developed so that people can control computers or machines through their brain activity without moving their limbs. The P300 speller is one of the BCI applications used most commonly, as is very simple and reliable and can achieve satisfactory performance. However, like other BCIs, the P300 speller still has room for improvements in terms of its practical use, for example, selecting the best compromise between spelling accuracy and information transfer rate (ITR; speed) so that the P300 speller can maintain high accuracy while increasing spelling speed. Therefore, seeking correlates of, and predicting, the P300 speller’s performance is necessary to understand and improve the technique. In this work, we investigated the correlations between rapid serial visual presentation (RSVP) task features and the P300 speller’s performance. Fifty-five subjects participated in the RSVP and conventional matrix P300 speller tasks and RSVP behavioral and electroencephalography (EEG) features were compared in the P300’s speller performance. We found that several of the RSVP’s event-related potential (ERP) and behavioral features were correlated with the P300 speller’s offline binary classification accuracy. Using these features, we propose a simple multi-feature performance predictor (r = 0.53, p = 0.0001) that outperforms any single feature performance predictor, including that of the conventional RSVP T1% predictor (r = 0.28, p = 0.06). This result demonstrates that selective multi-features can predict BCI performance better than a single feature alone.

Brain–computer interfaces (BCIs) facilitate communication for people who cannot move their own body. A BCI system requires a lengthy calibration phase to produce a reasonable classifier. To reduce the duration of the calibration phase, it is natural to attempt to create a subject-independent classifier with all subject datasets that are available; however, electroencephalogram (EEG) data have notable inter-subject variability. Thus, it is very challenging to achieve subject-independent BCI performance comparable to subject-specific BCI performance. In this study, we investigate the potential for achieving better subject-independent motor imagery BCI performance by conducting comparative performance tests with several selective subject pooling strategies (i.e., choosing subjects who yield reasonable performance selectively and using them for training) rather than using all subjects available. We observed that the selective subject pooling strategy worked reasonably well with public MI BCI datasets. Finally, based upon the findings, criteria to select subjects for subject-independent BCIs are proposed here.

Background/Aims: In exploring human factors, stereoscopic 3D images have been used to investigate the neural responses associated with excessive depth, texture complexity, and other factors. However, the cortical oscillation associated with the complexity of stereoscopic images has been studied rarely. Here, we demonstrated that the oscillatory responses to three differently shaped 3D images (circle, star, and bat) increase as the complexity of the image increases. Methods: We recorded simultaneous EEG/MEG for three different stimuli. Spatio-temporal and spatio-spectro-temporal features were investigated by non-parametric permutation test. Results: The results showed that N300 and alpha inhibition increased in the ventral area as the shape complexity of the stereoscopic image increased. Conclusion: It seems that the relative disparity in complex stereoscopic images may increase cognitive processing (N300) and cortical load (alpha inhibition) in the ventral area.

Advances in computer processing technology have enabled researchers to analyze real-time brain activity and build real-time closed-loop paradigms. In many fields, the effectiveness of these closed-loop protocols has proven to be better than that of the simple open-loop paradigms. Recently, sleep studies have attracted much attention as one possible application of closed-loop paradigms. To date, several studies that used closed-loop paradigms have been reported in the sleep-related literature and recommend a closed-loop feedback system to enhance specific brain activity during sleep, which leads to improvements in sleep’s effects, such as memory consolidation. However, to the best of our knowledge, no report has reviewed and discussed the detailed technical issues that arise in designing sleep closed-loop paradigms. In this paper, we reviewed the most recent reports on sleep closed-loop paradigms and offered an in-depth discussion of some of their technical issues. We found 148 journal articles strongly related with ‘sleep and stimulation’ and reviewed 20 articles on closed-loop feedback sleep studies. We focused on human sleep studies conducting any modality of feedback stimulation. Then we introduced the main component of the closed-loop system and summarized several open-source libraries, which are widely used in closed-loop systems, with step-by-step guidelines for closed-loop system implementation for sleep. Further, we proposed future directions for sleep research with closed-loop feedback systems, which provide some insight into closed-loop feedback systems.

The detailed biophysical mechanisms through which transcranial magnetic stimulation (TMS) activates cortical circuits are still not fully understood. Here we present a multi-scale computational model to describe and explain the activation of different pyramidal cell types in motor cortex due to TMS. Our model determines precise electric fields based on an individual head model derived from magnetic resonance imaging and calculates how these electric fields activate morphologically detailed models of different neuron types. We predict neural activation patterns for different coil orientations consistent with experimental findings. Beyond this, our model allows us to calculate activation thresholds for individual neurons and precise initiation sites of individual action potentials on the neurons’ complex morphologies. Specifically, our model predicts that cortical layer 3 pyramidal neurons are generally easier to stimulate than layer 5 pyramidal neurons, thereby explaining the lower stimulation thresholds observed for I-waves compared to D-waves. It also shows differences in the regions of activated cortical layer 5 and layer 3 pyramidal cells depending on coil orientation. Finally, it predicts that under standard stimulation conditions, action potentials are mostly generated at the axon initial segment of cortical pyramidal cells, with a much less important activation site being the part of a layer 5 pyramidal cell axon where it crosses the boundary between grey matter and white matter. In conclusion, our computational model offers a detailed account of the mechanisms through which TMS activates different cortical pyramidal cell types, paving the way for more targeted application of TMS based on individual brain morphology in clinical and basic research settings.

In recent years, research on Brain-Computer Interface (BCI) technology for healthy users has attracted considerable interest, and BCI games are especially popular. This study reviews the current status of, and describes future directions, in the field of BCI games. To this end, we conducted a literature search and found that BCI control paradigms using electroencephalographic signals (motor imagery, P300, steady state visual evoked potential and passive approach reading mental state) have been the primary focus of research. We also conducted a survey of nearly three hundred participants that included researchers, game developers and users around the world. From this survey, we found that all three groups (researchers, developers and users) agreed on the significant influence and applicability of BCI and BCI games, and they all selected prostheses, rehabilitation and games as the most promising BCI applications. User and developer groups tended to give low priority to passive BCI and the whole head sensor array. Developers gave higher priorities to “the easiness of playing” and the “development platform” as important elements for BCI games and the market. Based on our assessment, we discuss the critical point at which BCI games will be able to progress from their current stage to widespread marketing to consumers. In conclusion, we propose three critical elements important for expansion of the BCI game market: standards, gameplay and appropriate integration.

Social interaction is one of humans’ most important activities and many efforts have been made to understand the phenomenon. Recently, some investigators have attempted to apply advanced brain signal acquisition systems that allow dynamic brain activities to be measured simultaneously during social interactions. Most studies to date have investigated dyadic interactions, although multilateral interactions are more common in reality. However, it is believed that most studies have focused on such interactions because of methodological limitations, in that it is very difficult to design a well-controlled experiment for multiple users at a reasonable cost. Accordingly, there are few simultaneous acquisition systems for multiple users. In this study, we propose a design framework for an acquisition system that measures EEG data simultaneously in an environment with 10 or more people. Our proposed framework allowed us to acquire EEG data at up to 1 kHz frequency from up to 20 people simultaneously. Details of our acquisition system are described from hardware and software perspectives. In addition, various related issues that arose in the system’s development—such as synchronization techniques, system loads, electrodes, and applications—are discussed. In addition, simultaneous visual ERP experiments were conducted with a group of nine people to validate the EEG acquisition framework proposed. We found that our framework worked reasonably well with respect to less than 4 ms delay and average loss rates of 1%. It is expected that this system can be used in various hyperscanning studies, such as those on crowd psychology, large-scale human interactions, and collaborative brain−computer interface, among others.

The detailed biophysical mechanisms through which transcranial magnetic stimulation (TMS) activates cortical circuits are still not fully understood. Here we present a multi-scale computational model to describe and explain the activation of different pyramidal cell types in motor cortex due to TMS. Our model determines precise electric fields based on an individual head model derived from magnetic resonance imaging and calculates how these electric fields activate morphologically detailed models of different neuron types. We predict neural activation patterns for different coil orientations consistent with experimental findings. Beyond this, our model allows us to calculate activation thresholds for individual neurons and precise initiation sites of individual action potentials on the neurons’ complex morphologies. Specifically, our model predicts that cortical layer 3 pyramidal neurons are generally easier to stimulate than layer 5 pyramidal neurons, thereby explaining the lower stimulation thresholds observed for I-waves compared to D-waves. It also shows differences in the regions of activated cortical layer 5 and layer 3 pyramidal cells depending on coil orientation. Finally, it predicts that under standard stimulation conditions, action potentials are mostly generated at the axon initial segment of cortical pyramidal cells, with a much less important activation site being the part of a layer 5 pyramidal cell axon where it crosses the boundary between grey matter and white matter. In conclusion, our computational model offers a detailed account of the mechanisms through which TMS activates different cortical pyramidal cell types, paving the way for more targeted application of TMS based on individual brain morphology in clinical and basic research settings.

Brain-computer interface (BCI) performance has achieved continued improvement over recent decades, and sensorimotor rhythm-based BCIs that use motor function have been popular subjects of investigation. However, it remains problematic to introduce them to the public market because of their low reliability. As an alternative resolution to this issue, visual-based BCIs that use P300 or steady-state visually evoked potentials seem promising; however, the inherent visual fatigue that occurs with these BCIs may be unavoidable. For these reasons, steady-state somatosensory evoked potential (SSSEP) BCIs, which are based on tactile selective attention, have gained increasing attention recently. These may reduce the fatigue induced by visual attention and overcome the low reliability of motor activity. In this literature survey, recent findings on SSSEP and its methodological uses in BCI are reviewed. Further, existing limitations of SSSEP BCI and potential future directions for the technique are discussed.

Subdural cortical stimulation (SuCS) is an appealing method in the treatment of neurological disorders, and computational modeling studies of SuCS have been applied to determine the optimal design for electrotherapy. To achieve a better understanding of computational modeling on the stimulation effects of SuCS, the influence of anisotropic white matter conductivity on the activation of cortical neurons was investigated in a realistic head model. In this paper, we constructed pyramidal neuronal models (layers 3 and 5) that showed primary excitation of the corticospinal tract, and an anatomically realistic head model reflecting complex brain geometry. The anisotropic information was acquired from diffusion tensor magnetic resonance imaging (DT-MRI) and then applied to the white matter at various ratios of anisotropic conductivity. First, we compared the isotropic and anisotropic models; compared to the isotropic model, the anisotropic model showed that neurons were activated in the deeper bank during cathodal stimulation and in the wider crown during anodal stimulation. Second, several popular anisotropic principles were adapted to investigate the effects of variations in anisotropic information. We observed that excitation thresholds varied with anisotropic principles, especially with anodal stimulation. Overall, incorporating anisotropic conductivity into the anatomically realistic head model is critical for accurate estimation of neuronal responses; however, caution should be used in the selection of anisotropic information.

Subdural cortical stimulation (SuCS) is a method used to inject electrical current through electrodes beneath the dura mater, and is known to be useful in treating brain disorders. However, precisely how SuCS must be applied to yield the most effective results has rarely been investigated. For this purpose, we developed a three-dimensional computational model that represents an anatomically realistic brain model including an upper chest. With this computational model, we investigated the influence of stimulation amplitudes, electrode configurations (single or paddle-array), and white matter conductivities (isotropy or anisotropy). Further, the effects of stimulation were compared with two other computational models, including an anatomically realistic brain-only model and the simplified extruded slab model representing the precentral gyrus area. The results of voltage stimulation suggested that there was a synergistic effect with the paddle-array due to the use of multiple electrodes; however, a single electrode was more efficient with current stimulation. The conventional model (simplified extruded slab) far overestimated the effects of stimulation with both voltage and current by comparison to our proposed realistic upper body model. However, the realistic upper body and full brain-only models demonstrated similar stimulation effects. In our investigation of the influence of anisotropic conductivity, model with a fixed ratio (1∶10) anisotropic conductivity yielded deeper penetration depths and larger extents of stimulation than others. However, isotropic and anisotropic models with fixed ratios (1∶2, 1∶5) yielded similar stimulation effects. Lastly, whether the reference electrode was located on the right or left chest had no substantial effects on stimulation.

A continuous Nondestructive monitoring method is required to apply proper feedback controls during tissue regeneration. Conductivity is one of valuable information to assess the physiological function and structural formation of regenerated tissues or cultured cells. However, conductivity imaging methods suffered from inherited ill-posed characteristics in image reconstruction, unknown boundary geometry, uncertainty in electrode position, and systematic artifacts. In order to overcome the limitation of microscopic electrical impedance tomography (micro-EIT), we applied a 3D-specific container with a fixed boundary geometry and electrode configuration to maximize the performance of Graz consensus reconstruction algorithm for EIT (GREIT). The separation of driving and sensing electrodes allows us to simplify the hardware complexity and obtain higher measurement accuracy from a large number of small sensing electrodes. We investigated the applicability of the GREIT to 3D micro-EIT images via numerical simulations and large-scale phantom experiments. We could reconstruct multiple objects regardless of the location. The resolution was 5 mm3 with 30 dB SNR and the position error was less than 2.54 mm. This shows that the new micro-EIT system integrated with GREIT is robust with the intended resolution. With further refinement and scaling down to a microscale container, it may be a continuous nondestructive monitoring tool for tissue engineering applications.

In most brain computer interface (BCI) systems, some target users have significant difficulty in using BCI systems. Such target users are called 'BCI-illiterate'. This phenomenon has been poorly investigated, and a clear understanding of the BCI-illiteracy mechanism or a solution to this problem has not been reported to date. In this study, we sought to demonstrate the neurophysiological differences between two groups (literate, illiterate) with a total of 52 subjects. We investigated recordings under non-task related state (NTS) which is collected during subject is relaxed with eyes open. We found that high theta and low alpha waves were noticeable in the BCI-illiterate relative to the BCI-literate people. Furthermore, these high theta and low alpha wave patterns were preserved across different mental states, such as NTS, resting before motor imagery (MI), and MI states, even though the spatial distribution of both BCI-illiterate and BCI-literate groups did not differ. From these findings, an effective strategy for pre-screening subjects for BCI illiteracy has been determined, and a performance factor that reflects potential user performance has been proposed using a simple combination of band powers. Our proposed performance factor gave an r = 0.59 (r(2) = 0.34) in a correlation analysis with BCI performance and yielded as much as r = 0.70 (r(2) = 0.50) when seven outliers were rejected during the evaluation of whole data (N = 61), including BCI competition datasets (N = 9). These findings may be directly applicable to online BCI systems.

MEG/EEG beamformer source imaging is a promising approach which can easily address spatiotemporal multi-dipole problems without a priori information on the number of sources and is robust to noise. Despite such promise, beamformer generally has weakness which is degrading localization performance for correlated sources and is requiring of dense scanning for covering all possible interesting (entire) source areas. Wide source space scanning yields all interesting area images, and it results in lengthy computation time. Therefore, an efficient source space scanning strategy would be beneficial in achieving accelerated beamformer source imaging. We propose a new strategy in computing beamformer to reduce scanning points and still maintain effective accuracy (good spatial resolution). This new strategy uses the distribution of correlation values between measurements and lead-field vectors. Scanning source points are chosen yielding higher RMS correlations than the predetermined correlation thresholds. We discuss how correlation thresholds depend on SNR and verify the feasibility and efficacy of our proposed strategy to improve the beamformer through numerical and empirical experiments. Our proposed strategy could in time accelerate the conventional beamformer up to over 40% without sacrificing spatial accuracy.