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) when compared to traditional approaches (94%) in an offline study. We also demonstrate that the constructed CNN works successfully in an online experiment in which twelve BCI subjects achieved 85% mean accuracy without calibration, compared to 82% (but the difference was not significant, 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.

Acoustic and electrical brain stimulations are techniques well known to enhance memory consolidation by driving slow oscillations (SO, < 1Hz) and sleep spindle activity. Recent studies have suggested that the temporal relationship between SO and sleep spindle activity may be an important key to understanding memory consolidation mechanisms. We hypothesized that evoking SO after sleep spindle activity may enhance memory consolidation. To derive these spindle-SO pairs, we delivered acoustic stimulation after sleep spindle detection and investigated its effects on memory consolidation with behavioral tests and analyses of neurophysiological features. Thirteen healthy male subjects (mean ± SD age: 26.3 ± 2.4 years) participated in this study. Subjects took a nap with acoustic stimulation after spindle activity detection and a sham nap without acoustic stimulation. All subjects performed word-pair memorization and finger tapping tasks before and after their nap. We found phase-locked SO and delta (1-4 Hz) activity during the stimulation nap in response to acoustic stimuli, and the subjects had a greater improvement in finger tapping tasks after the stimulation nap than after the sham nap (p = 0.014). We found strong motorlearning enhancement after the stimulation nap, but this effect was limited to the subjects who did not demonstrate evoked spindle activity after their acoustic stimulation. Evoked spindle activity occurred in the up-state following the negative peak in auditory evoked potential (AEP), and this activity was observed only in subjects who had a greater AEP amplitude than normal SO. Based on these results, we suggest that subject-specific stimulation parameters, such as acoustic amplitude and timing, improve motor learning, and are appropriate to drive SO without causing a spindle response.

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.

Motor-imagery brain-computer interface (MI-BCI) is a technique that manipulates external machines using brain activities, and is highly useful to amyotrophic lateral sclerosis patients who cannot move their limbs. However, it is reported that approximately 15–30% of users cannot modulate their brain signals, which results in the inability to operate motor imagery BCI systems. Thus, advance prediction of BCI performance has drawn researchers’ attention, and some predictors have been proposed using the alpha band’s power, as well as other spectral bands’ powers, or spectral entropy from resting state electroencephalography (EEG). However, these predictors rely on a single state alone, such as the eyes-closed or eyes-open state; thus, they may often be less stable or unable to explain inter-/intra-subject variability. In this work, a modified predictor of MI-BCI performance that considered both brain states (eyes-open and eyes-closed resting states) was investigated with 41 online MI-BCI session datasets acquired from 15 subjects. The results showed that our proposed predictor and online MI-BCI classification accuracy were positively and highly significantly correlated (r = 0.71, p < 0.1 × 10 − 7 ), which indicates that the use of multiple brain states may yield a more robust predictor than the use of a single state alone.

As attention to deep learning techniques has grown, many researchers have attempted to develop ready-to-go brain-computer interfaces (BCIs) that include automatic processing pipelines. However, to do so, a large and clear dataset is essential to increase the model’s reliability and performance. Accordingly, our electroencephalogram (EEG) dataset for rapid serial visual representation (RSVP) and P300 speller may contribute to increasing such BCI research. We validated our dataset with respect to features and accuracy. For the RSVP, the participants (N = 50) achieved about 92% mean target detection accuracy. At the feature level, we observed notable ERPs (at 315 ms in the RSVP; at 262 ms in the P300 speller) during target events compared to non-target events. Regarding P300 speller performance, the participants (N = 55) achieved about 92% mean accuracy. In addition, P300 speller performance over trial repetitions up to 15 was explored. The presented dataset could potentially improve P300 speller applications. Further, it may be used to evaluate feature extraction and classification algorithm effectively, such as for cross-subjects/cross-datasets, and even for the cross-paradigm BCI model.