ObjectiveTo investigate the feasibility and effectiveness of motor imagery based brain computer interface with wrist passive movement in chronic stroke patients with wrist extension impairment.MethodsFifteen chronic stroke patients with a mean age of (47.60±14.66) years were recruited from March 2017 to June 2018. At baseline, motor imagery ability was assessed first. Then motor imagery based brain computer interface with wrist passive movement was given as an intervention. Both range of motion of paretic wrist and Barthel index was assessed before and after the intervention.ResultsAmong the 15 chronic stroke patients admitted in the study, 12 finished the whole therapy, and 3 failed to pass the initial assessment. After the therapy, the 12 participants who completed the whole sessions of the treatment and follow up had improved ability of control electroencephalogram, in whom 9 regained the ability to actively extend the affected wrist, and the other 3 failed to actively extend their wrist (the rate of active extending wrist was 75%). The activity of daily life of all the participants did not change significantly before and after intervention, and no discomfort was found after daily treatment.ConclusionIn chronic stroke patients with wrist extension impairment, motor imagery based brain computer interface with wrist passive movement training is feasible and effective.
Brain-computer interface (BCI) is a revolutionary human-computer interaction technology, which includes both BCI that can output instructions directly from the brain to external devices or machines without relying on the peripheral nerve and muscle system, and BCI that bypasses the peripheral nerve and muscle system and inputs electrical, magnetic, acoustic and optical stimuli or neural feedback directly to the brain from external devices or machines. With the development of BCI technology, it has potential application not only in medical field, but also in non-medical fields, such as education, military, finance, entertainment, smart home and so on. At present, there is little literature on the relevant application of BCI technology, the current situation of BCI industrialization at home and abroad and its commercial value. Therefore, this paper expounds and discusses the above contents, which are expected to provide valuable information for the public and organizations, BCI researchers, BCI industry translators and salespeople, and improve the cognitive level of BCI technology, further promote the application and industrial transformation of BCI technology and enhance the commercial value of BCI, so as to serve mankind better.
Clinical grading diagnosis of disorder of consciousness (DOC) patients relies on behavioral assessment, which has certain limitations. Combining multi-modal technologies and brain-computer interface (BCI) paradigms can assist in identifying patients with minimally conscious state (MCS) and vegetative state (VS). This study collected electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) signals under motor BCI paradigms from 14 DOC patients, who were divided into two groups based on clinical scores: 7 in the MCS group and 7 in the VS group. We calculated event-related desynchronization (ERD) and motor decoding accuracy to analyze the effectiveness of motor BCI paradigms in detecting consciousness states. The results showed that the classification accuracies for left-hand and right-hand movement tasks using EEG were 93.28% and 76.19% for the MCS and VS groups, respectively; the classification precisions using fNIRS were 53.72% and 49.11% for these groups. When combining EEG and fNIRS features, the classification accuracies for left-hand and right-hand movement tasks in the MCS and VS groups were 95.56% and 87.38%, respectively. Although there was no statistically significant difference in motor decoding accuracy between the two groups, significant differences in ERD were observed between different consciousness states during left-hand movement tasks (P < 0.001). This study demonstrates that motor BCI paradigms can assist in assessing the level of consciousness, with EEG being more sensitive for evaluating residual motor intention intensity. Moreover, the ERD feature of motor intention intensity is more sensitive than BCI classification accuracy.