Enhancing Classification Performance of fNIRS-BCI by Identifying Cortically Active Channels Using the z-Score Method.

ABSTRACT: A state-of-the-art brain-computer interface (BCI) system includes brain signal acquisition, noise removal, channel selection, feature extraction, classification, and an application interface. In functional near-infrared spectroscopy-based BCI (fNIRS-BCI) channel selection may enhance classification performance by identifying suitable brain regions that contain brain activity. In this study, the z-score method for channel selection is proposed to improve fNIRS-BCI performance. The proposed method uses cross-correlation to match the similarity between desired and recorded brain activity signals, followed by forming a vector of each channel's correlation coefficients' maximum values. After that, the z-score is calculated for each value of that vector. A channel is selected based on a positive

SUBMITTER: Nazeer H 

PROVIDER: S-EPMC7730208 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature