Classifying Imaginary Hand Movement through Electroencephalograph Signal for Neuro-rehabilitation
Keywords:Brain-Computer-Interface (BCI), neuro-rehabilitation, electroencephalogram (EEG)
Brain-Computer-Interface (BCI) has been widely used in the field of neuro-rehabilitation such as automatic controls based on brain commands to upper and lower extremity prosthesis devices in patients with paralysis. In a post-stroke period, approximately 50 % of stroke survivors have unilateral motor deficits leading to a sustained decline in chronic upper extremity function. Stroke affects patients in their productive and elderly age which is potentially creating new problems in national health development. BCI could be used to aid post-stroke patient recovery, so the motion detection and classification are essential for optimizing the BCI device control. Therefore, this study aims to distinguish several hand functions such as grasping, pinching, and hand lifting from releasing movement by the standard actions performed during post-stroke rehabilitation based on brain signals of a healthy subject obtained from an electroencephalogram (EEG) with a 5 channels electrode. In this study, the EEG signals were decomposed using a Discrete Wavelet Transform (DWT) then filtered by a bandpass filter to generate Mu and Beta waves, which were correlated with imaginary movement. Then, the Mu and Beta waves were calculated using a Common Spatial Pattern (CSP) algorithm as the inputs for Extreme Learning Machine (ELM) to distinguish 2 types of imaginary hand movements (grasping v. releasing, pinching v. releasing, hand lifting v. releasing). The results of these classifications shown that ELM and CSP were useful features in distinguishing 2 types of motion with software/system accuracy average above 95 %. Therefore, this could be useful for optimizing BCI devices in neuro-rehabilitation, moreover by combining it with a Functional Electrical Stimulator (FES) as a self-therapy for post-stroke patients.
Badan Penelitian dan Pengembangan Kesehatan Kementrian Kesehatan RI. Riset Kesehatan Dasar. Ministry of Health of the Republic of Indonesia, 2013, p. 129-32.
JA Franck, J Halfens, R Smeets and H Seelen. Concise arm and hand rehabilitation approach in stroke (CARAS): A practical and evidence-based framework for clinical rehabilitation management. Open J. Occup. Ther. 2015; 3, 10.
N Birbaumer, AR Murguialday and L Cohen. Brain-computer interface in paralysis. Curr. Opin. Neurol. 2008; 21, 634-8.
JJ Daly, R Cheng, J Rogers, K Litinas, K Hrovat and M Dohring. Feasibility of a new application of noninvasive brain computer interface (BCI): A case study of training for recovery of volitional motor control after stroke. J. Neurol. Phys. Ther. 2009; 33. 203-11.
KK Ang, C Guan, KS Phua, C Wang, L Zhou, KY Tang, GJ Ephraim Joseph, CWK Kuah and KSG Chua. Brain-computer interface-based robotic end effector system for wrist and hand rehabilitation: Results of a three-armed randomized controlled trial for chronic stroke. Front. Neuroeng. 2014; 7, 30.
E Buch, C Weber, LG Cohen, C Braun, MA Dimyan, T Ard, J Mellinger, A Caria, S Soekadar, A Fourkas and N Birbaumer. Think to move: A neuromagnetic brain-computer interface (BCI) system for chronic stroke. Stroke 2008; 39, 910-7.
J Cantillo-Negrete, J Gutierrez-Martinez, RI Carino-Escobar, P Carrillo-Mora and D Elias-Vinas. An approach to improve the performance of subject-independent BCIs-based on motor imagery allocating subjects by gender. Biomed. Eng. Online 2014; 13, 1-15.
R Tomioka, K Aihara and KR Müller. Logistic regression for single trial EEG classification. In: Proceeings of the Neural Information Processing Systems Conference. Canada, 2007, p. 1377-84.
GB Huang, Q Zhu, C Siew, GHÃQ Zhu, C Siew, GB Huang, Q Zhu and C Siew. Extreme learning machine: Theory and applications. Neurocomputing 2006; 70, 489-501.
S Hatamikia and AM Nasrabadi. Subject independent BCI based on LTCCSP method and GA wrapper optimization. In: Proceeings of the 22nd Iranian Conference on Biomedical Engineering. Tehran, Iran, 2015, p. 405-9.
X Yong and C Menon. EEG classification of different imaginary movements within the same limb. PLoS One 2015; 10, 1-24.
M Jochumsen, IK Niazi, K Dremstrup and EN Kamavuako. Detecting and classifying three different hand movement types through electroencephalography recordings for neurorehabilitation. Med. Biol. Eng. Comput. 2016; 54, 1491-501.
P Szachewicz. 2013, Classification of Motor Imagery for Brain-Computer Interfaces, Master Thesis. Poznan University of Technology, Poznan, Poland.
B Shoelson. edfRead. MatlabCentral, 2011.
J Ethridge and W Weaver. Common Spatial Patterns Alogarithm. MatlabCentral, 2009.
Q Yuan, W Zhou, S Li and D Cai. Epileptic EEG classification based on extreme learning machine and nonlinear features. Epilepsy Res. 2011; 96, 29-38.
GB Huang. Introduction to extreme learning machines. In: Hands-on Workshop on Machine Learning for BioMedical Informatics 2006. National University of Singapore, 2006.
MHA Samaha and K AlKamha. Automated classification of L/R hand movement EEG signals using advanced feature extraction and machine learning. Int. J. Adv. Comput. Sci. Appl. 2013; 4, 6.
G Lange, CY Low, K Johar, FA Hanapiah and F Kamaruzaman. Classification of electroencephalogram data from hand grasp and release movements for BCI controlled prosthesis. Proc. Tech. 2016; 26, 374-81.
Emotiv Inc. Emotiv Insight User Manual, Available at: https://www.emotiv.com, accessed November 2017.