Think to Move: a Neuromagnetic Brain-Computer Interface (BCI) System for Chronic Stroke

By Ethan R. Buch, Cornelia Weber, Leonardo G. Cohen, Christoph Braun, M. Dimyan, T. Ard, Jürgen Mellinger, A. Caria, Surjo Raphael Soekadar, Alissa D. Fourkas and 1 more. Stroke, 2008

621 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Neurology, Rehabilitation

View Paper: https://www.semanticscholar.org/paper/0eee8c8c8aef66dc5b05d22c2aae68b013e6f874

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cognitive Neuroscience

In Neurology

In Rehabilitation

Citations in the corpus

Brain-Computer Interfaces Using Sensorimotor Rhythms: Current State and Future Perspectives (2014, positive): “cessfully learned to use motor imagery to control their SMRs, and they were able to use a BCI to control an orthotic device that opened and closed their paralyzed hands [29].”
Closing the sensorimotor loop: haptic feedback facilitates decoding of motor imagery (2011, background): “Furthermore, successful MEG-based decoding of motor imagery in chronic stroke patients has been demonstrated in [14].”
Combining Brain–Computer Interfaces and Assistive Technologies: State-of-the-Art and Challenges (2010, background): “[23] have shown that six out of eight chronic stroke patients suffering from a handplegia learned to control a magnetoencephalography-based BCI by motor imagery.”
fNIRS-based brain-computer interfaces: a review (2015, background): “…Kubler, 2013; Ko and Sim, 2013; Hammer et al., 2014; Kim et al., 2014; Soekadar et al., 2014), magnetoencephalography (MEG) (Mellinger et al., 2007; Buch et al., 2008; Sardouie and Shamsollahi, 2012), functional magnetic resonance imaging (fMRI) (Weiskopf et al., 2004; LaConte, 2011; van der…”
Brain-Machine Interfaces: From Basic Science to Neuroprostheses and Neurorehabilitation. (2017, background): “For example, stroke patients can learn to operate an MEG-based BMI by modulating their rhythm recorded in the hemisphere ipsilateral to the lesion (104).”
Brain-machine interfaces in neurorehabilitation of stroke. (2015, positive): “While the majority of stroke patients, particularly those with subcortical lesions, were able to learn SMR-based BMI control (Buch et al., 2008, 2012), such BMI learning is often slower after stroke compared to healthy controls (Soekadar et al., 2011b).”
Review of the BCI Competition IV (2012, positive): “Despite of its practical restrictions (an MEG system is neither practical nor affordable for patients), this MEG-BCI could still be applied in conjunction with online feedback, e.g., for stroke rehabilitation attempts (Buch et al., 2008) or for prosthesis training.”
Clinical Applications of Brain-Computer Interfaces: Current State and Future Prospects (2009, methodology): “Since preliminary results in stroke patients demonstrate that they can gain control of specific brain activity patterns [50,73], a BCI might be used to enhance this control by measuring and extracting EEG features that can be translated into feedback to the user.”
Brain Computer Interfaces, a Review (2012, methodology): “[283] developed a BCI system that used MEG activity evoked by patient intent to move a completely paralyzed hand, in order to control grasping motions of a mechanical orthosis attached to the affected hand.”