Neural Modulation By Repetitive Transcranial Magnetic Stimulation (rTMS) for BCI Enhancement in Stroke Patients

Brain-computer interface (BCI) is a novel method for stroke rehabilitation. However, lacking of sufficient motor-related cortical activity greatly decreases the BCI performance in stroke patients. Interestingly, high-frequency repetitive transcranial magnetic stimulation (rTMS) has been shown to increase the cortical excitability of lesioned hemisphere in stroke patients. This stimulation effect may have benefits on the enhancement of BCI decoding. This study recruited 16 stroke patients to evaluate the stimulation effect on BCI accuracy, with 8 patients were assigned to the TMS-group and the other 8 patients were assigned to the Control-group. Patients in the TMS-group underwent 12 sessions of 10-Hz TMS interventions in four consecutive weeks, whereas no stimulation was applied during this period in the Control-group. Meanwhile, three BCI evaluation sessions were carried out in one day before, one day after, and three days after the TMS intervention, separately. The results showed that the TMS intervention significantly improved the BCI accuracy from 63.5% to 74.3% in motor imagery (MI) tasks, and from 81.9% to 91.1% in motor execution (ME) tasks. This finding provides a novel method for the cure of BCI-inefficiency problem, and may facilitate the clinical application of BCI-based stroke rehabilitation.

[1]  L. Cohen,et al.  Brain–machine interface in chronic stroke rehabilitation: A controlled study , 2013, Annals of neurology.

[2]  Jianjun Meng,et al.  Simultaneously Optimizing Spatial Spectral Features Based on Mutual Information for EEG Classification , 2015, IEEE Transactions on Biomedical Engineering.

[3]  W. Kakuda,et al.  Bilateral high- and low-frequency rTMS in acute stroke patients with hemiparesis: A comparative study with unilateral high-frequency rTMS , 2014, Brain injury.

[4]  Á. Pascual-Leone,et al.  Modulation of corticospinal excitability by repetitive transcranial magnetic stimulation , 2000, Clinical Neurophysiology.

[5]  Klaus-Robert Müller,et al.  Enhanced Performance by a Hybrid Nirs–eeg Brain Computer Interface , 2022 .

[6]  Cuntai Guan,et al.  Brain–Computer Interface for Neurorehabilitation of Upper Limb After Stroke , 2015, Proceedings of the IEEE.

[7]  A. Doud,et al.  Continuous Three-Dimensional Control of a Virtual Helicopter Using a Motor Imagery Based Brain-Computer Interface , 2011, PloS one.

[8]  Liqing Zhang,et al.  A Boosting-Based Spatial-Spectral Model for Stroke Patients’ EEG Analysis in Rehabilitation Training , 2016, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[9]  Jonathan R. Wolpaw,et al.  Brain-computer interfaces (BCIs) for communication and control , 2007, Assets '07.

[10]  Wei He,et al.  Performance of Motor Imagery Brain-Computer Interface Based on Anodal Transcranial Direct Current Stimulation Modulation , 2013, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[11]  Cuntai Guan,et al.  Filter Bank Common Spatial Pattern Algorithm on BCI Competition IV Datasets 2a and 2b , 2012, Front. Neurosci..

[12]  M. Jeannerod.  Mental imagery in the motor context , 1995, Neuropsychologia.

[13]  J. Wolpaw,et al.  Clinical Applications of Brain-Computer Interfaces: Current State and Future Prospects , 2009, IEEE Reviews in Biomedical Engineering.

[14]  L. Cohen,et al.  Non-invasive brain stimulation: a new strategy to improve neurorehabilitation after stroke? , 2006, The Lancet Neurology.

[15]  Benjamin Blankertz,et al.  Towards a Cure for BCI Illiteracy , 2009, Brain Topography.

[16]  Gerwin Schalk,et al.  Contralesional Brain–Computer Interface Control of a Powered Exoskeleton for Motor Recovery in Chronic Stroke Survivors , 2017, Stroke.

[17]  Xinjun Sheng,et al.  Enhanced Motor Imagery-Based BCI Performance via Tactile Stimulation on Unilateral Hand , 2017, Front. Hum. Neurosci..