Brain-computer interfaces in neurological rehabilitation.

By Janis J. Daly, Jonathan R. Wolpaw. Lancet Neurology, 2008

1,145 citations recorded by the source corpus.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience, Human-Computer Interaction

View Paper: https://www.semanticscholar.org/paper/32f297c1f61a6ac305d841c98b4a07b9db90a27d

How researchers see this paper

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

In Cellular and Molecular Neuroscience

In Cognitive Neuroscience

In Human-Computer Interaction

Citations in the corpus

Brain-Computer Interfaces Using Sensorimotor Rhythms: Current State and Future Perspectives (2014, positive): “initial evidence strongly encourages the usage of SMR-BCI in clinical rehabilitation applications [115].”
Clinical Applications of Brain-Computer Interfaces: Current State and Future Prospects (2009, background): “In a recent review, Daly and Wolpaw [29] classified the possible BCI-based motor learning strategies into two categories.”
Brain Computer Interfaces, a Review (2012, methodology): “In other words, offline simulation and cross-validation can be valuable methods to develop and test new algorithms, but only online analysis can yield solid evidence of BCI system performance [137,212,213].”
The Berlin Brain–Computer Interface: Non-Medical Uses of BCI Technology (2010, background): “…the overall aim to help paralyzed patients (Kübler et al., 1999, 2000, 2001, 2005; Birbaumer et al., 2000, 2003; Neuper et al., 2003; Pfurtscheller et al., 2003; Birbaumer and Cohen, 2007; Dobkin, 2007; Müller-Putz et al., 2007; Cincotti et al., 2008; Daly and Wolpaw, 2008; Conradi et al., 2009).”
Brain-machine interfaces in neurorehabilitation of stroke. (2015, neutral): “The mechanism by which such BMIs might improve motor function is less clear, possibly involving attempts to return brain activity " closer to normal " (Daly and Wolpaw, 2008).”
Brain-computer interfaces in medicine. (2012, background): “In addition, some researchers are exploring the se of BCIs in neurorehabilitation.(68) The hypothesis s that BCIs can augment current rehabilitation therpies by reinforcing and thereby increasing more ffective use of impaired brain areas and connecions.”
Noninvasive neuroimaging enhances continuous neural tracking for robotic device control (2019, background): “Although levels of internal motivation vary across populations and time (17), engaging users and maintaining attention via stimulating task paradigms may diminish these differences.”
A comprehensive review of EEG-based brain–computer interface paradigms (2019, positive): “EEG signals could provide a pathway from the brain to various external devices resulting in brain-controlled assistive devices for disabled people and brain-controlled rehabilitation devices for patients with strokes and other neurological deficits [21 – 25].”
Closing the sensorimotor loop: haptic feedback facilitates decoding of motor imagery (2011, background): “Instead, BCIs may also help to directly facilitate rehabilitation of body parts impaired by neurological conditions such as stroke [11].”
Electrocortical activity is coupled to gait cycle phase during treadmill walking (2011, positive): “In addition to providing insight into how the human central nervous system coordinates locomotion, we believe that in the long run these dynamics may be of use for brain-machine interface (BMI) based neurorehabilitation (Daly and Wolpaw, 2008; Yang and Gorassini, 2006) and that future applications o”