Background:Most investigators of brain–computer interface (BCI) research believe that BCI can be achieved through induced neuronal activity from the cortex, but not by evoked neuronal activity. Motor imagery (MI)–based BCI is one of the standard concepts of BCI, in that the user can generate induced activity by imagining motor movements. However, variations in performance over sessions and subjects are too severe to overcome easily; therefore, a basic understanding and investigation of BCI performance variation is necessary to find critical evidence of performance variation. Here we present not only EEG datasets for MI BCI from 52 subjects, but also the results of a psychological and physiological questionnaire, EMG datasets, the locations of 3D EEG electrodes, and EEGs for non-task-related states.Findings:We validated our EEG datasets by using the percentage of bad trials, event-related desynchronization/synchronization (ERD/ERS) analysis, and classification analysis. After conventional rejection of bad trials, we showed contralateral ERD and ipsilateral ERS in the somatosensory area, which are well-known patterns of MI. Finally, we showed that 73.08% of datasets (38 subjects) included reasonably discriminative information.Conclusions:Our EEG datasets included the information necessary to determine statistical significance; they consisted of well-discriminated datasets (38 subjects) and less-discriminative datasets. These may provide researchers with opportunities to investigate human factors related to MI BCI performance variation, and may also achieve subject-to-subject transfer by using metadata, including a questionnaire, EEG coordinates, and EEGs for non-task-related states.

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 (r2 = 0.34) in a correlation analysis with BCI performance and yielded as much as r = 0.70 (r2 = 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.

While brain computer interface (BCI) can be employed with patients and healthy subjects, there are problems that must be resolved before BCI can be useful to the public. In the most popular motor imagery (MI) BCI system, a significant number of target users (called “BCI-Illiterates”) cannot modulate their neuronal signals sufficiently to use the BCI system. This causes performance variability among subjects and even among sessions within a subject. The mechanism of such BCI-Illiteracy and possible solutions still remain to be determined. Gamma oscillation is known to be involved in various fundamental brain functions, and may play a role in MI. In this study, we investigated the association of gamma activity with MI performance among subjects. Ten simultaneous MEG/EEG experiments were conducted; MI performance for each was estimated by EEG data, and the gamma activity associated with BCI performance was investigated with MEG data. Our results showed that gamma activity had a high positive correlation with MI performance in the prefrontal area. This trend was also found across sessions within one subject. In conclusion, gamma rhythms generated in the prefrontal area appear to play a critical role in BCI performance.

Overall, our proposed consecutive hybrid approach is very promising for the development of advanced BCI systems.

Recently, neurophysiological findings about social interaction have been investigated widely, and hardware has been developed that can measure multiple subjects' brain activities simultaneously. These hyperscanning studies have enabled us to discover new and important evidences of interbrain interactions. Yet, very little is known about verbal interaction without any visual input. Therefore, we conducted a new hyperscanning study based on verbal, interbrain turn-taking interaction using simultaneous EEG/MEG, which measures rapidly changing brain activities. To establish turn-taking verbal interactions between a pair of subjects, we set up two EEG/MEG systems (19 and 146 channels of EEG and MEG, respectively) located 100 miles apart. Subjects engaged in verbal communication via condenser microphones and magnetic-compatible earphones, and a network time protocol synchronized the two systems. Ten subjects participated in this experiment and performed verbal interaction and noninteraction tasks separately. We found significant oscillations in EEG alpha and MEG alpha/ gamma bands in several brain regions for all subjects. Furthermore, we estimated phase synchronization between two brains using the weighted phase lag index and found statistically significant

A. 1,lO-Phenanthroline Complexes [ (phen),Mn'" ,0/Mn'"(phen)z]4+ /o\ 1 Mn'"(phen)2]*+ (2) e-3 . p 3 . X B. 2,2'-Bipyridyl Complexes 0 0 CibPYXMn\ '" \Mn".(bpy)J'+ , 1 k ( = o.OZs-' Y n 2 . e-& r(bPYhMn\ 'I' , 'Mn"'(bpy),]2+ ( 4 ) 0 Ep,c = +0.36 V 1 fast Z pared to 2.53 PB (uncorrected) for the solid by the Guoy method a t room temperaturet2 and for 3, Feff = 2.26 f 0.08 p c~g .~~,~~ The reduced magnetic moments that are observed relative to those for the spin-only condition support the conclusion that the di-v-oxo bridged species are stable in solution.Of the two complexes, the mixed-oxidation state bipyridyl complex (2) is slightly easier to oxidize and more difficult to reduce than the corresponding 1,lO-phenanthroline derivative. Also, the coupled chemical reactions occur a t a faster rate for the bipyridyl complexes. This change in chemical and electrochemical reactivity can be accounted for on the basis of the "floppy" bipyridyl ligand compared to the rigid structure of 1 ,lo-phenanthroline.

The goal of this in vitro study was to investigate the effect of lipid emulsion on apoptosis induced by a toxic dose of bupivacaine (BPV) in H9c2 rat cardiomyoblast cell lines. The effect of lipid emulsion on the decreased cell viability and count induced by BPV or mepivacaine (MPV) in the H9c2 cells was assessed using an 3-(4,5-dimethylthiazole-2-yl)-2,5-diphenyl tetrazolium bromide assay or a cell count assay. The effect of BPV or lipid emulsion combined with BPV on cleaved caspase 3, caspase 8, and Bax in H9c2 cells was investigated using Western blotting. A terminal deoxynucleotidyl transferase dUTP2′-deoxyuridine 5′-triphosphate nick end-labeling (TUNEL) assay was performed to detect apoptosis of H9c2 cells treated with BPV alone or lipid emulsion combined with BPV. The magnitude of lipid emulsion-mediated attenuation of decreased cell viability induced by BPV was higher than that of lipid emulsion-mediated attenuation of decreased cell viability induced by MPV. Lipid emulsion attenuated the increases in cleaved caspase 3, caspase 8 and Bax induced by BPV. Lipid emulsion attenuated the increases in TUNEL-positive cells induced by BPV. These results suggest that lipid emulsion attenuates a toxic dose of BPV-induced apoptosis via inhibition of the extrinsic and intrinsic apoptotic pathways. The protective effect of lipid emulsion may be partially associated with the relatively high lipid solubility of BPV.

Performance variation is a critical issue in motor imagery brain–computer interface (MI-BCI), and various neurophysiological, psychological, and anatomical correlates have been reported in the literature. Although the main aim of such studies is to predict MI-BCI performance for the prescreening of poor performers, studies which focus on the user’s sense of the motor imagery process and directly estimate MI-BCI performance through the user’s self-prediction are lacking. In this study, we first test each user’s self-prediction idea regarding motor imagery experimental datasets. Fifty-two subjects participated in a classical, two-class motor imagery experiment and were asked to evaluate their easiness with motor imagery and to predict their own MI-BCI performance. During the motor imagery experiment, an electroencephalogram (EEG) was recorded; however, no feedback on motor imagery was given to subjects. From EEG recordings, the offline classification accuracy was estimated and compared with several questionnaire scores of subjects, as well as with each subject’s self-prediction of MI-BCI performance. The subjects’ performance predictions during motor imagery task showed a high positive correlation (r = 0.64, p < 0.01). Interestingly, it was observed that the self-prediction became more accurate as the subjects conducted more motor imagery tasks in the Correlation coefficient (pre-task to 2nd run: r = 0.02 to r = 0.54, p < 0.01) and root mean square error (pre-task to 3rd run: 17.7% to 10%, p < 0.01). We demonstrated that subjects may accurately predict their MI-BCI performance even without feedback information. This implies that the human brain is an active learning system and, by self-experiencing the endogenous motor imagery process, it can sense and adopt the quality of the process. Thus, it is believed that users may be able to predict MI-BCI performance and results may contribute to a better understanding of low performance and advancing BCI.