<span style="font-family:Verdana;">There are few EEG studies on finger movement directions because ocular artifacts also convey directional information, which makes it hard to separate the contribution...<span style="font-family:Verdana;">There are few EEG studies on finger movement directions because ocular artifacts also convey directional information, which makes it hard to separate the contribution of EEG from that of the ocular artifacts. To overcome this issue, we designed an experiment in which EEG’s temporal dynamics and spatial information are evaluated together to improve the performance of brain-computer interface (BCI) for classifying finger movement directions. Six volunteers participated in the study. We examined their EEG using decoding analyses. Independent components (ICs) that represented brain-source signals successfully classified the directions of the finger movements with higher rates than chance level. The weight analyses of the classifiers revealed that maximal performance of the classification was recorded at the latencies prior to the onset of finger movements. The weight analyses also revealed the relevant cortical areas including the right lingual, left posterior cingulate, left inferior temporal gyrus, and right precuneus, which indicated the involvement of the visuospatial processing. We concluded that combining spatial distribution and temporal dynamics of the scalp EEG may improve BCI performance.</span>展开更多
文摘<span style="font-family:Verdana;">There are few EEG studies on finger movement directions because ocular artifacts also convey directional information, which makes it hard to separate the contribution of EEG from that of the ocular artifacts. To overcome this issue, we designed an experiment in which EEG’s temporal dynamics and spatial information are evaluated together to improve the performance of brain-computer interface (BCI) for classifying finger movement directions. Six volunteers participated in the study. We examined their EEG using decoding analyses. Independent components (ICs) that represented brain-source signals successfully classified the directions of the finger movements with higher rates than chance level. The weight analyses of the classifiers revealed that maximal performance of the classification was recorded at the latencies prior to the onset of finger movements. The weight analyses also revealed the relevant cortical areas including the right lingual, left posterior cingulate, left inferior temporal gyrus, and right precuneus, which indicated the involvement of the visuospatial processing. We concluded that combining spatial distribution and temporal dynamics of the scalp EEG may improve BCI performance.</span>
