Gruenwald, J., Znobishchev, A., Kapeller, C., Kamada, K., Scharinger, J. and Guger, C. (2019) Time-Variant Linear Discriminant Analysis Improves Hand Gesture and Finger Movement Decoding for Invasive Brain-Computer Interfaces. Frontiers in Neuroscience, 13, 901.
https://doi.org/10.3389/fnins.2019.00901

has been cited by the following article:

• TITLE: Investigating Neural Representation of Finger-Movement Directions Using Electroencephalography Independent Components

  AUTHORS: Mohamed Mounir Tellache, Hiroyuki Kambara, Yasuharu Koike, Makoto Miyakoshi, Natsue Yoshimura

  KEYWORDS: Electroencephalography, Independent Component Analysis, Finger Movement Decoding Brain-Computer Interface, Occipital Lobe

  JOURNAL NAME: Journal of Biomedical Science and Engineering, Vol.14 No.6, June 15, 2021

  ABSTRACT: 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.