@article {3091,
	title = {Should the parameters of a BCI translation algorithm be continually adapted?.},
	journal = {Journal of neuroscience methods},
	volume = {199},
	year = {2011},
	month = {07/2011},
	pages = {103{\textendash}107},
	abstract = {People with or without motor disabilities can learn to control sensorimotor rhythms (SMRs) recorded from the scalp to move a computer cursor in one or more dimensions or can use the P300 event-related potential as a control signal to make discrete selections. Data collected from individuals using an SMR-based or P300-based BCI were evaluated offline to estimate the impact on performance of continually adapting the parameters of the translation algorithm during BCI operation. The performance of the SMR-based BCI was enhanced by adaptive updating of the feature weights or adaptive normalization of the features. In contrast, P300 performance did not benefit from either of these procedures.},
	keywords = {adaptation, brain-computer interface, EEG},
	issn = {1872-678X},
	doi = {10.1016/j.jneumeth.2011.04.037},
	url = {http://www.ncbi.nlm.nih.gov/pubmed/21571004},
	author = {Dennis J. McFarland and Sarnacki, William A. and Jonathan Wolpaw}
}
@article {3093,
	title = {Trained modulation of sensorimotor rhythms can affect reaction time.},
	journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
	volume = {122},
	year = {2011},
	month = {09/2011},
	pages = {1820{\textendash}1826},
	abstract = {OBJECTIVE:
Brain-computer interface (BCI) technology might be useful for rehabilitation of motor function. This speculation is based on the premise that modifying the EEG will modify behavior, a proposition for which there is limited empirical data. The present study examined the possibility that voluntary modulation of sensorimotor rhythm (SMR) can affect motor behavior in normal human subjects.
METHODS:
Six individuals performed a cued-reaction task with variable warning periods. A typical variable foreperiod effect was associated with SMR desynchronization. SMR features that correlated with reaction times were then used to control a two-target cursor movement BCI task. Following successful BCI training, an uncued reaction time task was embedded within the cursor movement task.
RESULTS:
Voluntarily increasing SMR beta rhythms was associated with longer reaction times than decreasing SMR beta rhythms.
CONCLUSIONS:
Voluntary modulation of EEG SMR can affect motor behavior.
SIGNIFICANCE:
These results encourage studies that integrate BCI training into rehabilitation protocols and examine its capacity to augment restoration of useful motor function.},
	keywords = {brain-computer interface, EEG, Reaction Time},
	issn = {1872-8952},
	doi = {10.1016/j.clinph.2011.02.016},
	url = {http://www.ncbi.nlm.nih.gov/pubmed/21411366},
	author = {Chadwick B. Boulay and Sarnacki, W. A. and Jonathan Wolpaw and Dennis J. McFarland}
}
@article {3094,
	title = {Brain-computer interface research comes of age: traditional assumptions meet emerging realities.},
	journal = {Journal of motor behavior},
	volume = {42},
	year = {2010},
	month = {11/2010},
	pages = {351{\textendash}353},
	abstract = {Brain-computer interfaces (BCIs) could provide important new communication and control options for people with severe motor disabilities. Most BCI research to date has been based on 4 assumptions that: (a) intended actions are fully represented in the cerebral cortex; (b) neuronal action potentials can provide the best picture of an intended action; (c) the best BCI is one that records action potentials and decodes them; and (d) ongoing mutual adaptation by the BCI user and the BCI system is not very important. In reality, none of these assumptions is presently defensible. Intended actions are the products of many areas, from the cortex to the spinal cord, and the contributions of each area change continually as the CNS adapts to optimize performance. BCIs must track and guide these adaptations if they are to achieve and maintain good performance. Furthermore, it is not yet clear which category of brain signals will prove most effective for BCI applications. In human studies to date, low-resolution electroencephalography-based BCIs perform as well as high-resolution cortical neuron-based BCIs. In sum, BCIs allow their users to develop new skills in which the users control brain signals rather than muscles. Thus, the central task of BCI research is to determine which brain signals users can best control, to maximize that control, and to translate it accurately and reliably into actions that accomplish the users{\textquoteright} intentions.},
	keywords = {brain-computer interface, brain-machine interface, EEG, human, neuroprosthesis},
	issn = {1940-1027},
	doi = {10.1080/00222895.2010.526471},
	url = {http://www.ncbi.nlm.nih.gov/pubmed/21184352},
	author = {Jonathan Wolpaw}
}
@article {3104,
	title = {A novel P300-based brain-computer interface stimulus presentation paradigm: moving beyond rows and columns.},
	journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
	volume = {121},
	year = {2010},
	month = {07/2010},
	pages = {1109{\textendash}1120},
	abstract = {OBJECTIVE:
An electroencephalographic brain-computer interface (BCI) can provide a non-muscular means of communication for people with amyotrophic lateral sclerosis (ALS) or other neuromuscular disorders. We present a novel P300-based BCI stimulus presentation - the checkerboard paradigm (CBP). CBP performance is compared to that of the standard row/column paradigm (RCP) introduced by Farwell and Donchin (1988).
METHODS:
Using an 8x9 matrix of alphanumeric characters and keyboard commands, 18 participants used the CBP and RCP in counter-balanced fashion. With approximately 9-12 min of calibration data, we used a stepwise linear discriminant analysis for online classification of subsequent data.
RESULTS:
Mean online accuracy was significantly higher for the CBP, 92\%, than for the RCP, 77\%. Correcting for extra selections due to errors, mean bit rate was also significantly higher for the CBP, 23 bits/min, than for the RCP, 17 bits/min. Moreover, the two paradigms produced significantly different waveforms. Initial tests with three advanced ALS participants produced similar results. Furthermore, these individuals preferred the CBP to the RCP.
CONCLUSIONS:
These results suggest that the CBP is markedly superior to the RCP in performance and user acceptability.
SIGNIFICANCE:
The CBP has the potential to provide a substantially more effective BCI than the RCP. This is especially important for people with severe neuromuscular disabilities.},
	keywords = {brain-computer interface, brain-machine interface, EEG, event-related potential, P300, Rehabilitation},
	issn = {1872-8952},
	doi = {10.1016/j.clinph.2010.01.030},
	url = {http://www.ncbi.nlm.nih.gov/pubmed/20347387},
	author = {Townsend, G. and LaPallo, B. K. and Chadwick B. Boulay and Krusienski, D. J. and Frye, G. E. and Hauser, C. K. and Schwartz, N. E. and Theresa M Vaughan and Jonathan Wolpaw and Sellers, E. W.}
}
@article {3108,
	title = {Toward a high-throughput auditory P300-based brain-computer interface.},
	journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
	volume = {120},
	year = {2009},
	month = {07/2009},
	pages = {1252{\textendash}1261},
	abstract = {OBJECTIVE:
Brain-computer interface (BCI) technology can provide severely disabled people with non-muscular communication. For those most severely disabled, limitations in eye mobility or visual acuity may necessitate auditory BCI systems. The present study investigates the efficacy of the use of six environmental sounds to operate a 6x6 P300 Speller.
METHODS:
A two-group design was used to ascertain whether participants benefited from visual cues early in training. Group A (N=5) received only auditory stimuli during all 11 sessions, whereas Group AV (N=5) received simultaneous auditory and visual stimuli in initial sessions after which the visual stimuli were systematically removed. Stepwise linear discriminant analysis determined the matrix item that elicited the largest P300 response and thereby identified the desired choice.
RESULTS:
Online results and offline analyses showed that the two groups achieved equivalent accuracy. In the last session, eight of 10 participants achieved 50\% or more, and four of these achieved 75\% or more, online accuracy (2.8\% accuracy expected by chance). Mean bit rates averaged about 2 bits/min, and maximum bit rates reached 5.6 bits/min.
CONCLUSIONS:
This study indicates that an auditory P300 BCI is feasible, that reasonable classification accuracy and rate of communication are achievable, and that the paradigm should be further evaluated with a group of severely disabled participants who have limited visual mobility.
SIGNIFICANCE:
With further development, this auditory P300 BCI could be of substantial value to severely disabled people who cannot use a visual BCI.},
	keywords = {brain-computer interface, brain-machine interface, EEG, event-related potential, P300, Rehabilitation},
	issn = {1872-8952},
	doi = {10.1016/j.clinph.2009.04.019},
	url = {http://www.ncbi.nlm.nih.gov/pubmed/19574091},
	author = {Klobassa, D. S. and Theresa M Vaughan and Peter Brunner and Schwartz, N. E. and Jonathan Wolpaw and Neuper, C. and Sellers, E. W.}
}
@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 {3138,
	title = {Brain-computer interface (BCI) operation: signal and noise during early training sessions.},
	journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
	volume = {116},
	year = {2005},
	month = {01/2005},
	pages = {56{\textendash}62},
	abstract = {OBJECTIVE:
People can learn to control mu (8-12 Hz) or beta (18-25 Hz) rhythm amplitude in the electroencephalogram (EEG) recorded over sensorimotor cortex and use it to move a cursor to a target on a video screen. The recorded signal may also contain electromyogram (EMG) and other non-EEG artifacts. This study examines the presence and characteristics of EMG contamination during new users{\textquoteright} initial brain-computer interface (BCI) training sessions, as they first attempt to acquire control over mu or beta rhythm amplitude and to use that control to move a cursor to a target.
METHODS:
In the standard one-dimensional format, a target appears along the right edge of the screen and 1s later the cursor appears in the middle of the left edge and moves across the screen at a fixed rate with its vertical movement controlled by a linear function of mu or beta rhythm amplitude. In the basic two-choice version, the target occupies the upper or lower half of the right edge. The user{\textquoteright}s task is to move the cursor vertically so that it hits the target when it reaches the right edge. The present data comprise the first 10 sessions of BCI training from each of 7 users. Their data were selected to illustrate the variations seen in EMG contamination across users.
RESULTS:
Five of the 7 users learned to change rhythm amplitude appropriately, so that the cursor hit the target. Three of these 5 showed no evidence of EMG contamination. In the other two of these 5, EMG was prominent in early sessions, and tended to be associated with errors rather than with hits. As EEG control improved over the 10 sessions, this EMG contamination disappeared. In the remaining two users, who never acquired actual EEG control, EMG was prominent in initial sessions and tended to move the cursor to the target. This EMG contamination was still detectable by Session 10.
CONCLUSIONS:
EMG contamination arising from cranial muscles is often present early in BCI training and gradually wanes. In those users who eventually acquire EEG control, early target-related EMG contamination may be most prominent for unsuccessful trials, and may reflect user frustration. In those users who never acquire EEG control, EMG may initially serve to move the cursor toward the target. Careful and comprehensive topographical and spectral analyses throughout user training are essential for detecting EMG contamination and differentiating between cursor control provided by EEG control and cursor control provided by EMG contamination.
SIGNIFICANCE:
Artifacts such as EMG are common in EEG recordings. Comprehensive spectral and topographical analyses are necessary to detect them and ensure that they do not masquerade as, or interfere with acquisition of, actual EEG-based cursor control.},
	keywords = {brain-computer interface, EEG, Electroencephalography, Learning, mu rhythm, sensorimotor cortex},
	issn = {1388-2457},
	doi = {10.1016/j.clinph.2004.07.004},
	url = {http://www.ncbi.nlm.nih.gov/pubmed/15589184},
	author = {Dennis J. McFarland and Sarnacki, William A. and Theresa M Vaughan and Jonathan Wolpaw}
}
@conference {3436,
	title = {Tracking of the mu rhythm using an empirically derived matched filter.},
	booktitle = {Proc. IEEE International Conference of Neural Engineering},
	year = {2005},
	month = {03/2005},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Arlington, VA},
	keywords = {bioelectric potentials, Brain Computer Interfaces, brain modeling, brain-computer interface, communication device, communication system control, cortical mu rhythm modulation, EEG, Electroencephalography, empirically derived matched filter, handicapped aids, laboratories, matched filters, medical signal detection, medical signal processing, monitoring, motor imagery, mu rhythm tracking, noninvasive treatment, rhythm, synchronous motors, two-dimensional cursor control data},
	isbn = {0-7803-8710-4},
	doi = {10.1109/CNE.2005.1419559},
	url = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=1419559},
	author = {Krusienski, Dean J and Gerwin Schalk and Dennis J. McFarland 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}
}
@article {3238,
	title = {Mu and beta rhythm topographies during motor imagery and actual movements.},
	journal = {Brain topography},
	volume = {12},
	year = {2000},
	month = {03/2000},
	pages = {177{\textendash}186},
	abstract = {People can learn to control the 8-12 Hz mu rhythm and/or the 18-25 Hz beta rhythm in the EEG recorded over sensorimotor cortex and use it to control a cursor on a video screen. Subjects often report using motor imagery to control cursor movement, particularly early in training. We compared in untrained subjects the EEG topographies associated with actual hand movement to those associated with imagined hand movement. Sixty-four EEG channels were recorded while each of 33 adults moved left- or right-hand or imagined doing so. Frequency-specific differences between movement or imagery and rest, and between right- and left-hand movement or imagery, were evaluated by scalp topographies of voltage and r spectra, and principal component analysis. Both movement and imagery were associated with mu and beta rhythm desynchronization. The mu topographies showed bilateral foci of desynchronization over sensorimotor cortices, while the beta topographies showed peak desynchronization over the vertex. Both mu and beta rhythm left/right differences showed bilateral central foci that were stronger on the right side. The independence of mu and beta rhythms was demonstrated by differences for movement and imagery for the subjects as a group and by principal components analysis. The results indicated that the effects of imagery were not simply an attenuated version of the effects of movement. They supply evidence that motor imagery could play an important role in EEG-based communication, and suggest that mu and beta rhythms might provide independent control signals.},
	keywords = {beta rhythm, EEG, imagery, mu rhythm, sensorimotor cortex},
	issn = {0896-0267},
	doi = {10.1023/A:1023437823106},
	url = {http://www.ncbi.nlm.nih.gov/pubmed/10791681},
	author = {Dennis J. McFarland and Miner, L. A. and Theresa M Vaughan and Jonathan Wolpaw}
}
@article {3267,
	title = {An EEG-based brain-computer interface for cursor control.},
	journal = {Electroencephalography and clinical neurophysiology},
	volume = {78},
	year = {1991},
	month = {03/1991},
	pages = {252{\textendash}259},
	abstract = {This study began development of a new communication and control modality for individuals with severe motor deficits. We trained normal subjects to use the 8-12 Hz mu rhythm recorded from the scalp over the central sulcus of one hemisphere to move a cursor from the center of a video screen to a target located at the top or bottom edge. Mu rhythm amplitude was assessed by on-line frequency analysis and translated into cursor movement: larger amplitudes moved the cursor up and smaller amplitudes moved it down. Over several weeks, subjects learned to change mu rhythm amplitude quickly and accurately, so that the cursor typically reached the target in 3 sec. The parameters that translated mu rhythm amplitudes into cursor movements were derived from evaluation of the distributions of amplitudes in response to top and bottom targets. The use of these distributions was a distinctive feature of this study and the key factor in its success. Refinements in training procedures and in the distribution-based method used to translate mu rhythm amplitudes into cursor movements should further improve this 1-dimensional control. Achievement of 2-dimensional control is under study. The mu rhythm may provide a significant new communication and control option for disabled individuals.},
	keywords = {Communication, computer control, EEG, mu rhythm, operant conditioning, prosthesis, sensorimotor rhythm},
	issn = {0013-4694},
	doi = {10.1016/0013-4694(91)90040-B},
	url = {http://www.ncbi.nlm.nih.gov/pubmed/1707798},
	author = {Jonathan Wolpaw and Dennis J. McFarland and Neat, G. W. and Forneris, C. A.}
}
