Brain–computer interfaces (BCI) have potential to provide a new channel of communication and control for people with severe motor disabilities. Although many empirical studies exist, few have specifically evaluated the impact of contributing factors on user performance and perception in BCI applications, especially for users with motor disabilities. This article reports the effects of luminosity contrast and stimulus duration on user performance and usage preference in a P300-based BCI application, P300 Speller. Ten participants with neuromuscular disabilities (amyotrophic lateral sclerosis and cerebral palsy) and 10 able-bodied participants were asked to spell six 10-character phrases in the P300 Speller. The overall accuracy was 76.5% for the able-bodied participants and 26.8% for participants with motor disabilities. The results showed that luminosity contrast and stimulus duration have significant effects on user performance. In addition, participants preferred high luminosity contrast with middle or short stimulus duration. However, these effects on user performance and preference varied for participants with and without motor disabilities. The results also indicated that although most participants with motor disabilities can establish BCI control, BCI illiteracy does exist. These results of the study should provide insights into the future research of the BCI systems, especially the real-world applicability of the BCI applications as a nonmuscular communication and control system for people with severe motor disabilities.

In this study, we analyze the effect of a recliner chair with rocking motions on sleep quality of naps using automated sleep scoring and spindle detection models. The quality of sleep corresponding to the two rocking motions was measured quantitatively and qualitatively. For the quantitative evaluation, we conducted a sleep parameter analysis based on the results of the estimated sleep stages obtained on the brainwave and spindle estimation, and a sleep survey assessment from the participants was analyzed for the qualitative evaluation. The analysis showed that sleep in the recliner chair with rocking motions positively increased the duration of the spindles and deep sleep stage, resulting in improved sleep quality.

Feature of event-related potential (ERP) has not been completely understood and illiteracy problem remains unsolved. To this end, P300 peak has been used as the feature of ERP in most brain–computer interface applications, but subjects who do not show such peak are common. Recent development of convolutional neural network provides a way to analyze spatial and temporal features of ERP. Here, we train the convolutional neural network with 2 convolutional layers whose feature maps represented spatial and temporal features of event-related potential. We have found that nonilliterate subjects’ ERP show high correlation between occipital lobe and parietal lobe, whereas illiterate subjects only show correlation between neural activities from frontal lobe and central lobe. The nonilliterates showed peaks in P300, P500, and P700, whereas illiterates mostly showed peaks in around P700. P700 was strong in both subjects. We found that P700 peak may be the key feature of ERP as it appears in both illiterate and nonilliterate subjects.

Respiratory sinus arrhythmia (RSA) is a phenomenon in which the heart rate (HR) changes with respiration, increasing during inspiration and decreasing during expiration. RSA biofeedback training has an effect in relieving negative mental conditions, such as anxiety and stress. Respiration is an important indicator affecting the parasympathetic activation within the body during RSA biofeedback training. Although there are existing studies that consider individual differences when selecting optimized respiration using heart rate variability, the studies that use the high frequency components of HRV, which is an indicator of parasympathetic activation, are insufficient. For this reason, this paper proposes a process to identify optimized respiration for efficient RSA feedback, consisting of three steps: (1) application, (2) optimization, and (3) validation. In the application phase, we measured PPG data against various respiratory cycles based on the HF components of HRV and calculated the proposed heart stabilization indicator (HSI) from the data. Then, we determined the optimized respiration cycle based on the HSI in the optimization step. Finally, we analyzed seven stress-related indices against the optimized respiration cycle. The experimental results show that HSI is associated with the parasympathetic nervous system activation, and the proposed method could help to determine the optimal respiratory cycle for each individual. Lastly, we expect that the proposed design could be used as an alternative to improving the efficiency of RSA biofeedback training.

Brain-computer interfaces have been adopting visual evoked potential (VEP) signals to evaluate real-time cognitive responses, which have been shown to differ depending on the system’s panel design and inter-stimulus interval (ISI). The objective of this study was to determine the optimal ISI and display contents for improved VEP signals. The panel contents consisted of six Korean texts or symbols from a television remote controller, and three levels of ISI were used. Subjects concentrated on a target stimulus while the panel randomly flashed different texts or symbols. Six local areas were measured by electroencephalogram based on the Extended International 10–20 system. P3 and N1 amplitudes and latencies were analyzed to determine the optimal combination of panel contents and ISI. Text-based panels were found to produce superior P3 and N1 responses compared with symbol-based panels. The ISI of 300 ms induced the best ERP responses for both text and symbol panels.