This paper proposed a novel tactile-stimuli P300 paradigm for Brain-Computer Interface (BCI), which potentially targeted at people with less learning ability or difficulty in maintaining attention. The new paradigm using only two types of stimuli was designed, and different targets were distinguished by frequency and spatial information. The classification algorithm was developed by introducing filters for frequency bands selection and conducting optimization with common spatial pattern (CSP) on the tactile evoked EEG signals. It features a combination of spatial and frequency information, with the spatial information distinguishing the sites of stimuli and frequency information identifying target stimuli and disturbances. We investigated both electrical stimuli and vibration stimuli, in which only one target site was stimulated in each block. The results demonstrated an average accuracy of 94.88% for electrical stimuli and 95.21% for vibration stimuli, respectively.
Background Brain-computer interface (BCI) is a promising neurorehabilitation strategy for ameliorating post-stroke function disorders. Physiological changes in the brain, such as functional near-infrared spectroscopy (fNIRS) dedicated to exploring cerebral circulatory responses during neurological rehabilitation tasks, are essential for gaining insights into neurorehabilitation mechanisms. However, the relationship between the neurovascular responses in different brain regions under rehabilitation tasks remains unknown.Objective The present study explores the fNIRS interactions between brain regions under different motor imagery (MI) tasks, emphasizing functional characteristics of brain network patterns and BCI motor task classification.Methods Granger causality analysis (GCA) is carried out for oxyhemoglobin data from 29 study participants in left-and right-hand MI tasks. ResultsAccording to research findings, homozygous and heterozygous states in the two brain connectivity modes reveal one and nine channel pairs, respectively, with significantly different (P < 0.05) GC values under the left-and right-hand MI tasks in the population. With reference to the total 10 channel pairs of causality differences between the two brain working states, a support vector machine is used to classify the two tasks with an overall accuracy of 83% for five-fold cross-validation. ConclusionAs demonstrated in the present study, fNIRS offers causality patterns in different brain states of MIBCI motor tasks. The research findings show that fNIRS causality can be used to assess different states of the brain, providing theoretical support for its application to neurorehabilitation assessment protocols to ultimately improve patients' quality of life.
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