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Prediction of gait intention from pre-movement EEG signals: a feasibility study by Shafiul Hasan, S. M.; Siddiquee, Masudur R.; Atri, Roozbeh; Ramon, Rodrigo; Marquez, J. Sebastian; Bai, Ou is a Medicine article available to read on EtoBox.

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## Background Prediction of Gait intention from pre-movement Electroencephalography (EEG) signals is a vital step in developing a real-time Brain-computer Interface (BCI) for a proper neuro-rehabilitation system. In that respect, this paper investigates the feasibility of a fully predictive methodology to detect the intention to start and stop a gait cycle by utilizing EEG signals obtained before the event occurrence. ## Methods An eight-channel, custom-made, EEG system with electrodes placed around the sensorimotor cortex was used to acquire EEG data from six healthy subjects and two amputees. A discrete wavelet transform-based method was employed to capture event related information in alpha and beta bands in the time-frequency domain. The Hjorth parameters, namely activity, mobility, and complexity, were extracted as features while a two-sample unpaired Wilcoxon test was used to get rid of redundant features for better classification accuracy. The feature set thus obtained was then used to classify between ’walk vs. stop’ and ’rest vs. start’ classes using support vector machine (SVM) classifier with RBF kernel in a ten-fold cross-validation scheme. ## Results Using a fully pred

Who reads Prediction of gait intention from pre-movement EEG signals: a feasibility study?

Author

Shafiul Hasan, S. M.; Siddiquee, Masudur R.; Atri, Roozbeh; Ramon, Rodrigo; Marquez, J. Sebastian; Bai, Ou

Publisher

BioMed Central; Springer (Biomed Central Ltd.); [London]: BioMed Central, 2004-; Springer Science and Business Media LLC; Society for Mining, Metallurgy and Exploration Inc. (ISSN 1743-0003)

Published

2020

Language

EN

Field

Medicine (Health Sciences)