Scoring upper-extremity motor function from EEG with artificial neural networks: a preliminary study

Journal of Neural Engineering
Xin ZhangCarlo Menon

Abstract

Motor function of chronic stroke survivors is generally accessed using clinical motor assessments. These motor assessments are partially subjective and require prior training for the examiners. Additionally, those motor function assessments require the health professionals to be present in person. The method proposed in this paper has the potential to radically change the way motor function is assessed. This work investigates the feasibility of automatically scoring upper-extremity motor function from EEG using artificial neural networks. Twelve healthy participants and fourteen participants with chronic stroke participated in this study. EEG data were recorded while the participants were clicking a button. Convolutional neural network models were trained based on the participants' Fugl Meyer motor assessment score. The result showed that the proposed method achieved high prediction accuracy both within (n  =  14, r  =  0.9921, p   =  3.3907  ×  10-12) and cross (n  =  14, r  =  0.9867, p   =  7.9342  ×  10-11) participant testing. This evidence suggests the proposed method is feasible to be used as a stable and objective measurement for motor function assessment.

References

Nov 27, 1999·Clinical Neurophysiology : Official Journal of the International Federation of Clinical Neurophysiology·G Pfurtscheller, F H Lopes da Silva
Dec 18, 2001·International Journal of Psychophysiology : Official Journal of the International Organization of Psychophysiology·C Neuper, G Pfurtscheller
Sep 18, 2002·Neurorehabilitation and Neural Repair·David J GladstoneSandra E Black
Oct 9, 2004·Stroke; a Journal of Cerebral Circulation·Michel J A M van Putten, Dénes L J Tavy
Aug 12, 2005·Neurorehabilitation and Neural Repair·Steven L WolfSonya L Pearson
Apr 29, 2009·Clinical Neurophysiology : Official Journal of the International Federation of Clinical Neurophysiology·Jose Leon-CarrionMaria Rosario Dominguez-Morales
Jul 21, 2011·Medical & Biological Engineering & Computing·Rui C V LoureiroMichelle Johnson
Dec 23, 2011·Brain : a Journal of Neurology·Els Karla VanhoutteUNKNOWN PeriNomS Study Group
Aug 4, 2012·Clinical Neurophysiology : Official Journal of the International Federation of Clinical Neurophysiology·Simon Finnigan, Michel J A M van Putten
Aug 17, 2012·Stroke; a Journal of Cerebral Circulation·Vera KaiserChrista Neuper
Mar 27, 2013·Archives of Physical Medicine and Rehabilitation·Michelle L WoodburyPamela W Duncan
Dec 3, 2014·Neural Networks : the Official Journal of the International Neural Network Society·Jürgen Schmidhuber
May 29, 2015·Nature·Yann LeCunGeoffrey Hinton
Jul 15, 2015·Brain : a Journal of Neurology·Pierre NicoloAdrian G Guggisberg
Oct 8, 2015·Clinical Science·Dorinne DespositoLudovic Waeckel
Apr 14, 2016·IEEE Reviews in Biomedical Engineering·Tommaso ProiettiNathanael Jarrasse
May 17, 2017·Neurorehabilitation and Neural Repair·Teiji KawanoIchiro Miyai
Jul 22, 2017·International Journal of Geriatric Psychiatry·Nicole CarsonKelly J Murphy
Aug 8, 2017·Human Brain Mapping·Robin Tibor SchirrmeisterTonio Ball
Sep 19, 2017·Lancet Neurology·Cathy M Stinear

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