@article {4111,
	title = {Controlling pre-movement sensorimotor rhythm can improve finger extension after stroke},
	journal = {Journal of Neural Engineering},
	volume = {15},
	year = {2018},
	month = {08/2018},
	abstract = {Objective. Brain{\textendash}computer interface (BCI) technology is attracting increasing interest as a tool for enhancing recovery of motor function after stroke, yet the optimal way to apply this technology is unknown. Here, we studied the immediate and therapeutic effects of BCI-based training to control pre-movement sensorimotor rhythm (SMR) amplitude on robot-assisted finger extension in people with stroke. Approach. Eight people with moderate to severe hand impairment due to chronic stroke completed a four-week three-phase protocol during which they practiced finger extension with assistance from the FINGER robotic exoskeleton. In Phase 1, we identified spatiospectral SMR features for each person that correlated with the intent to extend the index and/or middle finger(s). In Phase 2, the participants learned to increase or decrease SMR features given visual feedback, without movement. In Phase 3, the participants were cued to increase or decrease their SMR features, and when successful, were then cued to immediately attempt to extend the finger(s) with robot assistance. Main results. Of the four participants that achieved SMR control in Phase 2, three initiated finger extensions with a reduced reaction time after decreasing (versus increasing) pre-movement SMR amplitude during Phase 3. Two also extended at least one of their fingers more forcefully after decreasing pre-movement SMR amplitude. Hand function, measured by the box and block test (BBT), improved by 7.3  {\textpm}  7.5 blocks versus 3.5  {\textpm}  3.1 blocks in those with and without SMR control, respectively. Higher BBT scores at baseline correlated with a larger change in BBT score. Significance. These results suggest that learning to control person-specific pre-movement SMR features associated with finger extension can improve finger extension ability after stroke for some individuals. These results merit further investigation in a rehabilitation context.},
	keywords = {BCI, Motor control, Rehabilitation, robot, sensorimotor rhythm, Stroke},
	doi = {10.1088/1741-2552/aad724},
	url = {http://stacks.iop.org/1741-2552/15/i=5/a=056026},
	author = {Norman, SL and McFarland, DJ and Miner, A and Cramer, SC and Wolbrecht, ET and Jonathan Wolpaw and Reinkensmeyer, DJ}
}
@article {3113,
	title = {A scanning protocol for a sensorimotor rhythm-based brain-computer interface.},
	journal = {Biological psychology},
	volume = {80},
	year = {2009},
	month = {02/2009},
	pages = {169{\textendash}175},
	abstract = {The scanning protocol is a novel brain-computer interface (BCI) implementation that can be controlled with sensorimotor rhythms (SMRs) of the electroencephalogram (EEG). The user views a screen that shows four choices in a linear array with one marked as target. The four choices are successively highlighted for 2.5s each. When a target is highlighted, the user can select it by modulating the SMR. An advantage of this method is the capacity to choose among multiple choices with just one learned SMR modulation. Each of 10 naive users trained for ten 30 min sessions over 5 weeks. User performance improved significantly (p<0.001) over the sessions and ranged from 30 to 80\% mean accuracy of the last three sessions (chance accuracy=25\%). The incidence of correct selections depended on the target position. These results suggest that, with further improvements, a scanning protocol can be effective. The ultimate goal is to expand it to a large matrix of selections.},
	keywords = {BCI, brain-computer interface, scanning protocol, sensorimotor rhythm},
	issn = {1873-6246},
	doi = {10.1016/j.biopsycho.2008.08.004},
	url = {http://www.ncbi.nlm.nih.gov/pubmed/18786603},
	author = {Friedrich, Elisabeth V. C. and Dennis J. McFarland and Neuper, Christa and Theresa M Vaughan and Peter Brunner and Jonathan Wolpaw}
}
@article {3207,
	title = {Brain-computer interface systems: progress and prospects.},
	journal = {Expert review of medical devices},
	volume = {4},
	year = {2007},
	month = {07/2007},
	pages = {463{\textendash}474},
	abstract = {Brain-computer interface (BCI) systems support communication through direct measures of neural activity without muscle activity. BCIs may provide the best and sometimes the only communication option for users disabled by the most severe neuromuscular disorders and may eventually become useful to less severely disabled and/or healthy individuals across a wide range of applications. This review discusses the structure and functions of BCI systems, clarifies terminology and addresses practical applications. Progress and opportunities in the field are also identified and explicated.},
	keywords = {ALS, assistive communication, BCI, BMI, brain-acuated control, brain-computer interface, brain-machine interface, EEG, ERP, locked-in syndrome, slow cortical potential, SSVEP, Stroke},
	issn = {1743-4440},
	doi = {10.1586/17434440.4.4.463},
	url = {http://www.ncbi.nlm.nih.gov/pubmed/17605682},
	author = {Brendan Z. Allison and Wolpaw, Elizabeth Winter and Jonathan Wolpaw}
}
@article {3133,
	title = {Brain-computer interface signal processing at the Wadsworth Center: mu and sensorimotor beta rhythms.},
	journal = {Progress in brain research},
	volume = {159},
	year = {2006},
	month = {02/2006},
	pages = {411{\textendash}419},
	abstract = {The Wadsworth brain-computer interface (BCI), based on mu and beta sensorimotor rhythms, uses one- and two-dimensional cursor movement tasks and relies on user training. This is a real-time closed-loop system. Signal processing consists of channel selection, spatial filtering, and spectral analysis. Feature translation uses a regression approach and normalization. Adaptation occurs at several points in this process on the basis of different criteria and methods. It can use either feedforward (e.g., estimating the signal mean for normalization) or feedback control (e.g., estimating feature weights for the prediction equation). We view this process as the interaction between a dynamic user and a dynamic system that coadapt over time. Understanding the dynamics of this interaction and optimizing its performance represent a major challenge for BCI research.},
	keywords = {adaptation, BCI, Signal Processing},
	issn = {0079-6123},
	doi = {10.1016/S0079-6123(06)59026-0},
	url = {http://www.ncbi.nlm.nih.gov/pubmed/17071245},
	author = {Dennis J. McFarland and Krusienski, Dean J. and Jonathan Wolpaw}
}
@article {3222,
	title = {The BCI Competition 2003: progress and perspectives in detection and discrimination of EEG single trials.},
	journal = {IEEE transactions on bio-medical engineering},
	volume = {51},
	year = {2004},
	month = {06/2004},
	pages = {1044{\textendash}1051},
	abstract = {Interest in developing a new method of man-to-machine communication{\textendash}a brain-computer interface (BCI){\textendash}has grown steadily over the past few decades. BCIs create a new communication channel between the brain and an output device by bypassing conventional motor output pathways of nerves and muscles. These systems use signals recorded from the scalp, the surface of the cortex, or from inside the brain to enable users to control a variety of applications including simple word-processing software and orthotics. BCI technology could therefore provide a new communication and control option for individuals who cannot otherwise express their wishes to the outside world. Signal processing and classification methods are essential tools in the development of improved BCI technology. We organized the BCI Competition 2003 to evaluate the current state of the art of these tools. Four laboratories well versed in EEG-based BCI research provided six data sets in a documented format. We made these data sets (i.e., labeled training sets and unlabeled test sets) and their descriptions available on the Internet. The goal in the competition was to maximize the performance measure for the test labels. Researchers worldwide tested their algorithms and competed for the best classification results. This paper describes the six data sets and the results and function of the most successful algorithms.},
	keywords = {augmentative communication, BCI, beta-rhythm, brain-computer interface, EEG, ERP, imagined hand movements, lateralized readiness potential, mu-rhythm, P300, Rehabilitation, single-trial classification, slow cortical potentials},
	issn = {0018-9294},
	doi = {10.1109/TBME.2004.826692},
	url = {http://www.ncbi.nlm.nih.gov/pubmed/15188876},
	author = {Benjamin Blankertz and M{\"u}ller, Klaus-Robert and Curio, Gabriel and Theresa M Vaughan and Gerwin Schalk and Jonathan Wolpaw and Schl{\"o}gl, Alois and Neuper, Christa and Pfurtscheller, Gert and Hinterberger, Thilo and Schr{\"o}der, Michael and Niels Birbaumer}
}
