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Classification of hand grasp kinetics and types using movement-related cortical potentials and EEG rhythms

Research output: Contribution to journalArticlepeer-review

Mads Jochumsen, Cecilie Rovsing, Helene Rovsing, Imran Khan Niazi, Kim Dremstrup, Ernest Nlandu Kamavuako

Original languageEnglish
Number of pages8
JournalComputational Intelligence and Neuroscience
Early online date29 Aug 2017
DOIs
Accepted/In press26 Jul 2017
E-pub ahead of print29 Aug 2017

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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 provide 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.

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