Young man sitting looking at his phone

Neurorehabilitation Tool for Post-Stroke Patients


Stroke causes long-term disability to the survivors. Each year, about 15 million people suffer from stroke world-wide. Stroke survivors face both motor impairments as well as cognitive deficits and an early intervention to restore these deficits is crucial in order to perform their daily activities [1-2]. In Denmark, a total of 12000 incidents are reported each year, making it the leading cause of long-term disability [3]. Further, the disability causes a huge burden on care givers due to limited and expensive rehabilitation protocols. The extensive care needed for post-stroke patients mount up to 4% of the total expenditures in the Danish healthcare system [4]. With limited availability of resources, these figures show the pressing need for novel and advanced research methods to provide practical neuro-rehabilitation tools for such patients. Presently, the main approach underlying motor recovery involves enhanced activity of the primary motor cortex induced by active motor training (AMT), Functional Electrical Stimulation (FES) and pharmacological interventions [5]. Alternative strategies are sought for as physical movements by stroke survivors are limited or often not possible. The mental rehearsal of physical movement tasks or in other words, the movement imagery (MI) can be seen as an approach to access the motor system and rehabilitation at all stages of stroke recovery [6-9]. This opens up the opportunity to explore the use of brain computer interface (BCI) systems with its neuro-feedback ability as an innovative practical approach to neuro-rehabilitation. Due to the bidirectional interaction between the brain and the computer, BCI appears appropriate for neuro-rehabilitation applications, as it can be used to alter the brain functions through neural plasticity [10-12]. 

Project objectives

In this project, a BCI controlled system is proposed as a complete neuro-rehabilitation tool for post-stroke patients to regain fine motor skills so that the hand gripping function can be triggered. An inexpensive portable neuro-rehabilitating training system is envisioned which can potentially cause neural plasticity and improvement in the motor skills. The method will be based on controlling a Funcional Electrical Stimulation (FES) device attached on the affected arm, using the EEG of the patients as the control mechanism.  


1) Prigatano, GP, Principles of neurophysiological rehabilitation, New York, Oxford University Press, 1999.

2) Maaijwee, N. A. M. M., Rutten-Jacobs, L. C. A., Arntz, R. M., Schaapsmeerders, P., Schoonderwaldt, H. C., vanDijk, E. J., and de Leeuw, F.-E., “Long-term increased risk of unemployment after young stroke: A long-term follow-up study”, Neurology 83, 1132–1138, 2014.

3) Truelsen, T, B. Piechowski-Jozwiak, R. Bonita, C. Mathers, J. Bogousslavsky, and G. Boysen, “Stroke incidence and prevalence in Europe: a review of available data”, European journal of neurology, vol. 13, no.6, pp: 581-598, 2006.

4) Hjerteforeningen, ”Fakta om apopleksi”, [Last accessed: 28 November 2013], 2011.

5) Calautti, C and J. C. Baron, “Functional neuroimaging studies of motor recovery after stroke in adults: a review,” Stroke, vol. 34, no.6, pp. 1553-1566, 2003.

6) Johnson, S.H, “Imagining the impossible: intact motor representations in hemiplegics,” NeuroReport, vol. 11, no. 4, pp.729-732, 2000.

7) Johnson, S.H, G. Sprehn, and A. J. Saykin, “Intact motor imagery in chronic upper limb hemiplegics: evidence for activity-independent action representations,” Journal of Cognitive Neuroscience, vol. 14, no. 6, pp. 841-852, 2002.

8) Sharma, N, V. M. Pomeroy, and J. C. Baron, “Motor imagery: a backdoor to the motor system after stroke?,” Stroke, vol. 37, no. 7, pp. 1941-1952, 2006.

9) Pfurtscheller et al., “Mu rhythm (de)synchronization and EEG single-trial classification of different motor imagery tasks”, NeuroImage, 31:153-159, 2006. doi: 10.1016/j.neuroimage.2005.12.003.

10) Dobkin, B.H., “BCI technology as a tool to augment plasticity and outcomes for neurological rehabilitation”, The Jl. Of Physiology, 579, 637-642, 2007.

11) Allison BZ, Brunner C, Kaiser V, Müller-Putz GR, Neuper C, Pfurtscheller G, “Toward a hybrid brain-computer interface based on imagined movement and visual attention”, J Neural Eng. 7(2):26007. Epub 2010.

12) Mak, J. N. and J. R. Wolpaw, “Clinical Applications of Brain-Computer Interfaces: Current State and Future Prospects”, IEEE Rev Biomed Eng 2: 187-199, 2009.



Sadasivan Puthusserypady
Associate Professor
DTU Electrical Engineering
+45 45 25 36 52


Helle Klingenberg Iversen
Rigshospitalet - Neurocentret - Glostrup
17 DECEMBER 2018