Node strength variability of brain network applied to single-hand movement directions decoding

Yanjiao Wang; Qin Wei

doi:10.1117/12.2682331

20 June 2023 Node strength variability of brain network applied to single-hand movement directions decoding

Yanjiao Wang, Qin Wei

Author Affiliations +

Proceedings Volume 12715, Eighth International Conference on Electronic Technology and Information Science (ICETIS 2023); 1271514 (2023) https://doi.org/10.1117/12.2682331
Event: Eighth International Conference on Electronic Technology and Information Science (ICETIS 2023), 2023, Dalian, China

Abstract

Electroencephalograms (EEG) signals can be used to decode hand movement parameters. Most of the researches on decoding hand movement parameters concentrated efforts on low-frequency feature of EEG extracted from timefrequency domain. However, complex brain structure and performance of various frequency bands have been less taken into account for hand movement direction decoding. In this paper, the node strength variability (NSV), a novel feature, was proposed to decode hand movement between horizontal and vertical direction. It is generated from discrepancy of connectivity among electrode nodes in two adjacent periods of brain networks constructed based on the phase locking value (PLV) from EEG signals. Five volunteers participated in our experiments, and totally 600 sets of EEG data were collected. NSV of five distinct frequency bands obtained between preparation and movement execution were applied to classify hand movement direction for each subject through a ten-fold cross-validation support vector machine (SVM). The results indicated that NSV of alpha band has the best effect on distinguishing horizontal and vertical hand movement directions, which provides new ideas for hand movement direction decoding.

Citation Download Citation

Yanjiao Wang and Qin Wei "Node strength variability of brain network applied to single-hand movement directions decoding", Proc. SPIE 12715, Eighth International Conference on Electronic Technology and Information Science (ICETIS 2023), 1271514 (20 June 2023); https://doi.org/10.1117/12.2682331

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