Brain-computer interfaces (BCIs) have been used by users to convey their intentions directly with brain signals. For example, a spelling system that uses EEGs allows letters on a display to be selected. In comparison, previous studies have investigated decoding speech information such as syllables, words from single-trial brain signals during speech comprehension, or articulatory imagination. Such decoding realizes speech recognition with a relatively short time-lag and without relying on a display. Previous magnetoencephalogram (MEG) research showed that a template matching method could be used to classify three English sentences by using phase patterns in theta oscillations. This method is based on the synchronization between speech rhythms and neural oscillations during speech processing, that is, theta oscillations synchronized with syllabic rhythms and low-gamma oscillations with phonemic rhythms. The present study aimed to approximate this classification method to a BCI application. To this end, (1) we investigated the performance of the EEG-based classification of three Japanese sentences and (2) evaluated the generalizability of our models to other different users. For the purpose of improving accuracy, (3) we investigated the performances of four classifiers: template matching (baseline), logistic regression, support vector machine, and random forest. In addition, (4) we propose using novel features including phase patterns in a higher frequency range. Our proposed features were constructed in order to capture synchronization in a low-gamma band, that is, (i) phases in EEG oscillations in the range of 2-50 Hz from all electrodes used for measuring EEG data (all) and (ii) phases selected on the basis of feature importance (selected). The classification results showed that, except for random forest, most classifiers perform similarly. Our proposed features improved the classification accuracy with statistical significance compared with a baseline feature, which is a phase pattern in neural oscillations in the range of 4-8 Hz from the right hemisphere. The best mean accuracy across folds was 55.9% using template matching trained by all features. We concluded that the use of phase information in a higher frequency band improves the performance of EEG-based sentence classification and that this model is applicable to other different users.

A new hybrid brain-computer interface (BCI), which is based on sequential controls by eye tracking and steady-state visual evoked potentials (SSVEPs), has been proposed for high-speed spelling in virtual reality (VR) with a 40-target virtual keyboard. During target selection, gaze point was first detected by an eye-tracking accessory. A 4-target block was then selected for further target selection by a 4-class SSVEP BCI. The system can type at a speed of 1.25 character/sec in a cue-guided target selection task. Online experiments on three subjects achieved an averaged information transfer rate (ITR) of 360.7 bits/min.

A brain-computer interface (BCI) translates the brain activity into commands to control external devices. P300 speller based character recognition is an important kind of application system in BCI. In this paper, we propose a framework to integrate channel correlation analysis into P300 detection. This work is distinguished by two key contributions. First, a coefficient matrix is introduced and constructed for multiple channels with the elements indicating channel correlations. Agglomerative clustering is applied to group correlated channels. Second, the statistics of central tendency are used to fuse the information of correlated channels and generate virtual channels. The generated virtual channels can extend the EEG signals and lift up the signal-to-noise ratio. The correlated features from virtual channels are combined with original signals for classification and the outputs of discriminative classifier are used to determine the characters for spelling. Experimental results prove the effectiveness and efficiency of the channel correlation analysis based framework. Compared with the state-of-the-art, the recognition rate was increased by both 6% with 5 and 10 epochs by the proposed framework.

Classification tasks in computer vision and brain-computer interface research have presented several applications such as biometrics and cognitive training. However, like in any other discipline, determining suitable representation of data has been challenging, and recent approaches have deviated from the familiar form of one vector for each data sample. This paper considers a kernel between vector sets, the mean polynomial kernel, motivated by recent studies where data are approximated by linear subspaces, in particular, methods that were formulated on Grassmann manifolds. This kernel takes a more general approach given that it can also support input data that can be modeled as a vector sequence, and not necessarily requiring it to be a linear subspace. We discuss how the kernel can be associated with the Projection kernel, a Grassmann kernel. Experimental results using face image sequences and physiological signal data show that the mean polynomial kernel surpasses existing subspace-based methods on Grassmann manifolds in terms of predictive performance and efficiency.

We have developed a portable NIRS-based optical BCI system that features a non-invasive, facile probe attachment and does not require muscle movement to control the target devices. The system consists of a 2-channel probe, a signal-processing unit, and an infrared-emission device, which measures the blood volume change in the participant's prefrontal cortex in a real time. We use the threshold logic as a switching technology, which transmits a control signal to a target device when the electrical waveforms exceed the pre-defined threshold. Eight healthy volunteers participated in the experiments and they could change the television channel or control the movement of a toy robot with average switching times of 11.5 ± 5.3 s and the hit rate was 83.3%. These trials suggest that this system provides a novel communication aid for people with motor disabilities.

Brain-Computer Interfaces (BCIs) are systems that translate one's thoughts into commands to restore control and communication to severely paralyzed people, and they are also appealing to healthy people. One of the challenges is to improve the performance of BCIs, often measured by the accuracy and the trial duration, or the information transfer rate (ITR), i.e., the mutual information per unit time. Since BCIs are communications between a user and a system, error control schemes such as forward error correction and automatic repeat request (ARQ) can be applied to BCIs to improve the accuracy. This paper presents reliability-based ARQ (RB-ARQ), a variation of ARQ designed for BCIs, which employs the maximum posterior probability for the repeat decision. The current results show that RB-ARQ is more effective than the conventional methods, i.e., better accuracy when trial duration was the same, and shorter trial duration when the accuracy was the same. This resulted in a greater information transfer rate and a greater utility, which is a more practical performance measure in the P300 speller task. The results also show that such users who achieve a poor accuracy for some reason can benefit the most from RB-ARQ, which could make BCIs more universal.

We have developed a prototype optical brain-computer interface (BCI) system that can be used by an operator to manipulate external, electrically controlled equipment. Our optical BCI uses near-infrared spectroscopy and functions as a compact, practical, unrestrictive, non-invasive brain-switch. The optical BCI system measured spatiotemporal changes in the hemoglobin concentrations in the blood flow of a subject's prefrontal cortex at 22 measurement points. An exponential moving average (EMA) filter was applied to the data, and then their weighted sum with a task-related parameter derived from a pretest is utilized for time-indicated control (GO-STOP) of an external object. In experiments using untrained subjects, the system achieved control patterns within an accuracy of 6 sec for more than 80% control.

In this study, we propose a method of classifying a spontaneous electroencephalogram (EEG) approach to a brain-computer interface. Ten subjects, aged 21-32 years, volunteered to imagine left- and right-hand movements. An independent component analysis based on a fixed-point algorithm is used to eliminate the activities found in the EEG signals. We use a fractal dimension value to reveal the embedded potential responses in the human brain. The different fractal dimension values between the relaxing and imaging periods are computed. Featured data is classified by a three-layer feed-forward neural network based on a simple backpropagation algorithm. Two conventional methods, namely, the use of the autoregressive (AR) model and the band power estimation (BPE) as features, and the linear discriminant analysis (LDA) as a classifier, are selected for comparison in this study. Experimental results show that the proposed method is more effective than the conventional methods.

A communication means is presented for patients with amyotrophic lateral sclerosis in totally locked-in state who are completely unable to move any part of the body and have no usual communication means. The method utilizes changes in cerebral blood volume accompanied with changes in brain activity. When a patient is asked a question and the answer to it is 'yes', the patient makes his or her brain active. The change in blood volume at the frontal lobe is detected with near-infrared light. The instantaneous amplitude and phase of the change are calculated, and the maximum amplitude and phase change are obtained. The answer 'yes' or 'no' of the patient is detected using a discriminant analysis with these two quantities as variables. The rate of correct detection is 80% on average.

A brain-computer interface using an electroencephalogram as input into an artificial neural network is investigated as a potentially general control system applicable to all subjects and time frames. Using the intent and imagination of bending the left or right elbow, the left and right desired movements are successfully distinguished using event-related desynchronization resolved by fast Fourier transformation of the electroencephalogram and analysis of the power spectrum using the artificial neural network. The influence of age was identified and eliminated through the use of a frequency distribution in the α band, and the recognition rate was further improved by confirmation based on forced excitement of the β band in the case of an error. The proposed system was effectively trained for general use by using the combined data of a cross-section of subjects.