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Haptic Brain Computer Interface in Paralyzed Chronic Stroke Patients

Ander Ramos-Murguialday1, 2*, Andrea Caria1, Doris Broetz1, Leonhard Läer1, Surjo R. Soekadar3, 4 and Niels Birbaumer1, 5
  • 1 University of Tübingen, Medical Psychology and Behavioral Neurobiology, Germany
  • 2 Tecnalia Germany, Germany
  • 3 University Hospital Tübingen, Psychiatry and Psychotherapy, Germany
  • 4 National institute of Health, Human Cortical Physiology and Stroke Section, United States
  • 5 Ospedale San Camillo, Italy

Incidence of a first stroke in Europe is about 1.1 million and prevalence about 6 million per year. Currently, about 75% of people affected by a stroke survive one year or more and this proportion will increase in the coming years due to enhanced quality in hyper-acute, follow-up acute and sub-acute care, and life-long treatment of these conditions. From all the stroke survivors showing no active upper limb motion at hospital admission, 14% showed complete recovery, while 30% showed partial recovery and 56% showed no recovery (Hendrick et al. 2002). Stroke survivors with chronic hand plegia and very low score in the Fugl-Meyer scale show limited residual muscle activity in the upper arm extensor muscles and normally no residual finger extension. Currently, there is no accepted and efficient rehabilitation strategy available that aims at reducing focal impairments in patients with chronic stroke and complete hand paralysis. In tight collaboration, the University of Tubingen (Germany) and the National Institutes of Health (NIH) demonstrated for the first time that stroke patients with complete hand paralysis can learn to control a magnetoencephalography (MEG) based Brain-Computer Interface (BCI) to drive a hand robotic orthosis (Buch et al. 2008). The BCI was used to move a cursor on a screen and depending on correct or incorrect response the hand orthotic device would or would not move the hand respectively. The results could not be translated out of the lab and patients needed the orthosis to move their hands. In a later study, we demonstrated that the combination of BCI and daily life-oriented physiotherapy can elicit functional recovery improving hand and arm movements as well as gait (Broetz et al. 2009). Furthermore, using a multimodal neuroimaging approach based on fMRI and diffusion tensor imaging (DTI) we investigated brain plasticity in the motor system along with longitudinal clinical assessments. We found a convergent association between functional and structural data in the ipsilesional premotor areas (Caria et al. 2010). Parallel to these findings we studied the effect of haptic feedback during the use of a sensorimotor rhythm (SMR) BCI. Here, an online EEG-based proprioceptive BCI was used for stroke rehabilitation (Ramos-Murguialday et al. 2009 & 20010) controlling a robotic exoskeleton online (250msec delay) using brain signals. In our study 36 chronic stroke patients with minimal residual hand extension underwent a 6-week daily online haptic-BCI rehabilitation therapy combined with goal-oriented physiotherapy. Several multimodal pre- and post-measurements were used to assess physiological and functional rehabilitation. The pre-measurements were conducted twice, two months before and immediately before the 6-week daily training. These two measurements allowed us to have a baseline of neurophysiological and psychophysiological data to check for stability and reliability of our measurements. The post-measurements were divided in two phases as well having one on the day after the last day of training and the second one six months later as a follow-up measurement. Magnetoencephalography (MEG) was used to measure sensory inputs using pneumatic vibrotactile actuators fixed to the index and pinky fingers and the lip. The ability to imagine and perform movement was assessed through a three-class protocol using MEG and functional magnetic resonance (fMRI). The MRI-scanner was also used to acquire important information related to anatomy and anatomical connectivity. To explore the corticospinal tract integrity, neuronavigated TMS was applied to the patients acquiring MEPs from lower and upper arm muscles and thus allowing us to have a more precise cortical map of flexors and extensors. TMS was applied following several protocols to elicit more stable and greater MEPs using pre contraction or imagination of movement. Several movements included in the Fugl-Meyer scale were used to generate a protocol to register muscle activity from the healthy and paretic side for further comparisons. EEG screenings were performed in order to identify most relevant oscillatory brain frequencies and electrode positions during hand opening and closing. This information was used to set up the online proprioceptive BCI classifier. Other psychological and physiotherapeutical tests (e.g. Wolf Motor Function Test, Fugl-Meyer Score, Ashworth Scale, SEIQL) were performed to correlate functional scales with neurophysiological data. Patients were assigned into one of three different feedback contingency groups (positive, negative and non-contingent feedback). Pilot results of this clinical study will be presented and discussed.

Acknowledgements

This study was supported by the German Ministry of Education and Research (Bernstein Fokus: Neurotechnologie, 01GQ0761, BMBF 16SV3783), ERC grant 227632-BCCI, the Deutsche Forschungsgemeinschaft (DFG) and the Intramural Research Program (IRP) of the National Institute of Neurological Disorders and Stroke (NINDS), National Institues of Health (NIH).

References

Broetz, D., Braun, C., Weber, C., Soekadar, SR., Caria, A., Birbaumer, N. (2010). Combination of brain-computer interface training and goal-directed physical therapy in chronic stroke: a case report. Neurorehabil Neural Repair. 24(7): 674-679.

Buch, E., Weber, C., Cohen, LG., Braun, C., Dimyan, MA., Ard, T., Mellinger, J., Caria, A., Soekadar, S., Fourkas, A., Birbaumer, N. (2008). Think to move: a neuromagnetic brain-computer interface (BCI) system for chronic stroke. Stroke. 39(3): 910-917.

Hendricks, HT., Limbeck, VJ. et al. (2002) Motor recovery after stroke: a systematic review of the literature. Arch Phys Med Rehabil 83:1629–1637

Ramos-Murguialday, A., Halder, S. and Birbaumer, N. (2009) Propioceptive Feedback in BCI. In proceedings of NER'09, 4th International IEEE EMBS Conference on Neural Engineering, Antalya, Turkey, 2009.

Ramos-Murguialday, A., Soares, E. and Birbaumer N. (2010) Upper Limb EMG Artefact Rejection in Motor Sensitive BCIs; Engineering in Medicine and Biology Society (EMBC) Proceedings, 2010 Annual International Conference of the IEEE 2010 (1:6).

Keywords: brain machine interface, Haptic Feedback, stroke rehabilitation

Conference: BC11 : Computational Neuroscience & Neurotechnology Bernstein Conference & Neurex Annual Meeting 2011, Freiburg, Germany, 4 Oct - 6 Oct, 2011.

Presentation Type: Abstract

Topic: neurotechnology and brain-machine interface (please use "neurotechnology and brain-machine interface" as keyword)

Citation: Ramos-Murguialday A, Caria A, Broetz D, Läer L, Soekadar S and Birbaumer N (2011). Haptic Brain Computer Interface in Paralyzed Chronic Stroke Patients. Front. Comput. Neurosci. Conference Abstract: BC11 : Computational Neuroscience & Neurotechnology Bernstein Conference & Neurex Annual Meeting 2011. doi: 10.3389/conf.fncom.2011.53.00119

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Received: 23 Aug 2011; Published Online: 04 Oct 2011.

* Correspondence: Dr. Ander Ramos-Murguialday, University of Tübingen, Medical Psychology and Behavioral Neurobiology, Tübingen, Germany, ander.ramos@gmail.com