Improving the Performance of Electrotactile Brain–Computer Interface Using Machine Learning Methods on Multi-Channel Features of Somatosensory Event-Related Potentials

Improving the Performance of Electrotactile Brain–Computer Interface Using Machine Learning Methods on Multi-Channel Features of Somatosensory Event-Related Potentials

Traditional tactile brain–computer interfaces (BCIs), particularly those based on steady-state somatosensory–evoked potentials, face challenges such as lower accuracy, reduced bit rates, and the need for spatially distant stimulation points. In contrast, using transient electrical stimuli offers a p...

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Bibliographic Details
Main Authors: Marija Novičić, Olivera Djordjević, Vera Miler-Jerković, Ljubica Konstantinović, Andrej M. Savić
Format: Article
Language:English
Published: MDPI AG 2024-12-01
Series:Sensors
Subjects:
brain–computer interface (BCI)
somatosensory event-related potentials (sERPs)
electrical stimulation
tactile attention
tactile BCI
feature selection
Online Access:https://www.mdpi.com/1424-8220/24/24/8048
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