Brain-Machine-Interface in Chronic Stroke Rehabilitation: A Controlled Study

By Ander Ramos-Murguialday, D. Broetz, Massimiliano Rea, L. Läer, Özge Yilmaz, F. Brasil, G. Liberati, M. Curado, E. Garcia-Cossio, A. Vyziotis and 7 more. Annals of Neurology, 2013

937 citations recorded by the source corpus.

Chronic stroke patients with severe hand weakness respond poorly to rehabilitation efforts. Here, we evaluated efficacy of daily brain–machine interface (BMI) training to increase the hypothesized beneficial effects of physiotherapy alone in patients with severe paresis in a double‐blind sham‐controlled design proof of concept study.

Subfields: Cognitive Neuroscience, Neurology, Rehabilitation

View Paper: https://www.semanticscholar.org/paper/4d1242d8db3cd7a995cfe7d52addfa62017c85f8

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In Cognitive Neuroscience

In Neurology

In Rehabilitation

Leonardo G. Cohen

Citations in the corpus

Signal Processing Approaches to Minimize or Suppress Calibration Time in Oscillatory Activity-Based Brain–Computer Interfaces (2015, methodology): “…for a number of applications for which ERP-based BCI cannot be used, including (but not limited to) self-paced BCI control [23], [24], stroke rehabilitation [25], [26] or passive BCI that monitor and adapt an application based on users’ mental states such as attention or workload [7], [27], [28].”
Brain–computer interface boosts motor imagery practice during stroke recovery (2015, background): “A similar physiologically driven, rather than data-driven, approach in BCI control feature extraction has recent garnered attention in BCI applications that promote motor rehabilitation after stroke.(26)”
Brain-machine interfaces in neurorehabilitation of stroke. (2015, positive): “Encouraged by these findings, a larger clinical trial with 32 chronic stroke survivors without residual finger movements (12.22 ± 1.51 out of 54 points according to a combined hand and modified arm FMA, cFMA, indicating severe upper-limb motor impairment) was conducted and showed that motor improvem”
Progress in Brain Computer Interface: Challenges and Opportunities (2019, background): “In stroke patients with paretic muscles without residual finger movement, increased electromyogram activity post rehabilitation by BCI-driven orthoses exhibits increased neuromuscular coherence that is essential for restoring movement control [97].”
Brain-Machine Interfaces: From Basic Science to Neuroprostheses and Neurorehabilitation. (2017, background): “However, long-term BMI training combined with physical therapy resulted in clear motor recovery (93, 659).”
Deep learning techniques for classification of electroencephalogram (EEG) motor imagery (MI) signals: a review (2021, background): “Ramos-Mur-guialday et al. from the University of Tu¨bingen in 2013 were the ﬁrst to scientiﬁcally prove that BCI has a reha-bilitative impact on stroke patients [160].”
EEG-Based Brain-Computer Interfaces (2017, background): “To date there have been a number of studies providing proof of principle, but only a few that provide clear evidence of efﬁcacy (e.g., [52]).”