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Sensors 2014, 14(10), 18172-18186; doi:10.3390/s141018172

Evaluating Classifiers to Detect Arm Movement Intention from EEG Signals

Brain-Machine Interface Systems Lab, Miguel Hernández University of Elche, Avda. de la Universidad S/N, 03202, Elche (Alicante), Spain
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Received: 18 July 2014 / Revised: 16 September 2014 / Accepted: 17 September 2014 / Published: 29 September 2014
(This article belongs to the Collection Sensors for Globalized Healthy Living and Wellbeing)
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Abstract

This paper presents a methodology to detect the intention to make a reaching movement with the arm in healthy subjects before the movement actually starts. This is done by measuring brain activity through electroencephalographic (EEG) signals that are registered by electrodes placed over the scalp. The preparation and performance of an arm movement generate a phenomenon called event-related desynchronization (ERD) in the mu and beta frequency bands. A novel methodology to characterize this cognitive process based on three sums of power spectral frequencies involved in ERD is presented. The main objective of this paper is to set the benchmark for classifiers and to choose the most convenient. The best results are obtained using an SVM classifier with around 72% accuracy. This classifier will be used in further research to generate the control commands to move a robotic exoskeleton that helps people suffering from motor disabilities to perform the movement. The final aim is that this brain-controlled robotic exoskeleton improves the current rehabilitation processes of disabled people. View Full-Text
Keywords: brain-computer interface; ERD; movement intention; classifier brain-computer interface; ERD; movement intention; classifier
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. (CC BY 4.0).

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MDPI and ACS Style

Planelles, D.; Hortal, E.; Costa, Á.; Úbeda, A.; Iáez, E.; Azorín, J.M. Evaluating Classifiers to Detect Arm Movement Intention from EEG Signals. Sensors 2014, 14, 18172-18186.

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