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Author Jochumsen, Mads ♦ Rovsing, Cecilie ♦ Rovsing, Helene ♦ Niazi, Imran Khan ♦ Dremstrup, Kim ♦ Kamavuako, Ernest Nlandu
Editor Sanei, Saeid
Source Hindawi
Content type Text
Publisher Hindawi
File Format PDF
Copyright Year ©2017
Language English
Abstract Detection of single-trial movement intentions from EEG is paramount for brain-computer interfacing in neurorehabilitation. These movement intentions contain task-related information and if this is decoded, the neurorehabilitation could potentially be optimized. The aim of this study was to classify single-trial movement intentions associated with two levels of force and speed and three different grasp types using EEG rhythms and components of the movement-related cortical potential (MRCP) as features. The feature importance was used to estimate encoding of discriminative information. Two data sets were used. 29 healthy subjects executed and imagined different hand movements, while EEG was recorded over the contralateral sensorimotor cortex. The following features were extracted: delta, theta, mu/alpha, beta, and gamma rhythms, readiness potential, negative slope, and motor potential of the MRCP. Sequential forward selection was performed, and classification was performed using linear discriminant analysis and support vector machines. Limited classification accuracies were obtained from the EEG rhythms and MRCP-components: 0.48±0.05 (grasp types), 0.41±0.07 (kinetic profiles, motor execution), and 0.39±0.08 (kinetic profiles, motor imagination). Delta activity contributed the most but all features provided discriminative information. These findings suggest that information from the entire EEG spectrum is needed to discriminate between task-related parameters from single-trial movement intentions.
ISSN 16875265
Learning Resource Type Article
Publisher Date 2017-08-29
Rights License This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
e-ISSN 16875273
Journal Computational Intelligence and Neuroscience
Volume Number 2017
Page Count 8


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