@article {3561,
	title = {Spatio-Temporal Progression of Cortical Activity Related to Continuous Overt and Covert Speech Production in a Reading Task.},
	journal = {PloS one},
	volume = {11},
	year = {2016},
	month = {Nov},
	pages = {e0166872},
	abstract = {How the human brain plans, executes, and monitors continuous and fluent speech has remained largely elusive. For example, previous research has defined the cortical locations most important for different aspects of speech function, but has not yet yielded a definition of the temporal progression of involvement of those locations as speech progresses either overtly or covertly. In this paper, we uncovered the spatio-temporal evolution of neuronal population-level activity related to continuous overt speech, and identified those locations that shared activity characteristics across overt and covert speech. Specifically, we asked subjects to repeat continuous sentences aloud or silently while we recorded electrical signals directly from the surface of the brain (electrocorticography (ECoG)). We then determined the relationship between cortical activity and speech output across different areas of cortex and at sub-second timescales. The results highlight a spatio-temporal progression of cortical involvement in the continuous speech process that initiates utterances in frontal-motor areas and ends with the monitoring of auditory feedback in superior temporal gyrus. Direct comparison of cortical activity related to overt versus covert conditions revealed a common network of brain regions involved in speech that may implement orthographic and phonological processing. Our results provide one of the first characterizations of the spatiotemporal electrophysiological representations of the continuous speech process, and also highlight the common neural substrate of overt and covert speech. These results thereby contribute to a refined understanding of speech functions in the human brain.},
	issn = {1932-6203},
	doi = {10.1371/journal.pone.0166872},
	url = {http://www.ncbi.nlm.nih.gov/pubmed/27875590},
	author = {Brumberg, Jonathan S. and Krusienski, Dean J. and Chakrabarti, Shreya and Gunduz, Aysegul and Peter Brunner and A L Ritaccio and Gerwin Schalk}
}
@article {3087,
	title = {Brain-computer interfaces in medicine.},
	journal = {Mayo Clinic proceedings. Mayo Clinic},
	volume = {87},
	year = {2012},
	month = {03/2012},
	pages = {268{\textendash}279},
	abstract = {Brain-computer interfaces (BCIs) acquire brain signals, analyze them, and translate them into commands that are relayed to output devices that carry out desired actions. BCIs do not use normal neuromuscular output pathways. The main goal of BCI is to replace or restore useful function to people disabled by neuromuscular disorders such as amyotrophic lateral sclerosis, cerebral palsy, stroke, or spinal cord injury. From initial demonstrations of electroencephalography-based spelling and single-neuron-based device control, researchers have gone on to use electroencephalographic, intracortical, electrocorticographic, and other brain signals for increasingly complex control of cursors, robotic arms, prostheses, wheelchairs, and other devices. Brain-computer interfaces may also prove useful for rehabilitation after stroke and for other disorders. In the future, they might augment the performance of surgeons or other medical professionals. Brain-computer interface technology is the focus of a rapidly growing research and development enterprise that is greatly exciting scientists, engineers, clinicians, and the public in general. Its future achievements will depend on advances in 3 crucial areas. Brain-computer interfaces need signal-acquisition hardware that is convenient, portable, safe, and able to function in all environments. Brain-computer interface systems need to be validated in long-term studies of real-world use by people with severe disabilities, and effective and viable models for their widespread dissemination must be implemented. Finally, the day-to-day and moment-to-moment reliability of BCI performance must be improved so that it approaches the reliability of natural muscle-based function.},
	keywords = {User-Computer Interface},
	issn = {1942-5546},
	doi = {10.1016/j.mayocp.2011.12.008},
	url = {http://www.ncbi.nlm.nih.gov/pubmed/22325364},
	author = {Shih, Jerry J. and Krusienski, Dean J. and Jonathan Wolpaw}
}
@article {3107,
	title = {Brain-computer interface research at the wadsworth center developments in noninvasive communication and control.},
	journal = {International review of neurobiology},
	volume = {86},
	year = {2009},
	month = {02/2009},
	pages = {147{\textendash}157},
	abstract = {Brain-computer interface (BCI) research at the Wadsworth Center focuses on noninvasive, electroencephalography (EEG)-based BCI methods for helping severely disabled individuals communicate and interact with their environment. We have demonstrated that these individuals, as well as able-bodied individuals, can learn to use sensorimotor rhythms (SMRs) to move a cursor rapidly and accurately in one and two dimensions. We have also developed a practical P300-based BCI that enables users to access and control the full functionality of their personal computer. We are currently translating this laboratory-proved BCI technology into a system that can be used by severely disabled individuals in their homes with minimal ongoing technical oversight. Our comprehensive approach to BCI design has led to several innovations that are applicable in other BCI contexts, such as space missions.},
	keywords = {User-Computer Interface},
	issn = {0074-7742},
	doi = {10.1016/S0074-7742(09)86011-X},
	url = {http://www.ncbi.nlm.nih.gov/pubmed/19607997},
	author = {Krusienski, Dean J. and Jonathan Wolpaw}
}
@article {3131,
	title = {Emulation of computer mouse control with a noninvasive brain-computer interface.},
	journal = {Journal of neural engineering},
	volume = {5},
	year = {2008},
	month = {06/2008},
	pages = {101{\textendash}110},
	abstract = {Brain-computer interface (BCI) technology can provide nonmuscular communication and control to people who are severely paralyzed. BCIs can use noninvasive or invasive techniques for recording the brain signals that convey the user{\textquoteright}s commands. Although noninvasive BCIs are used for simple applications, it has frequently been assumed that only invasive BCIs, which use electrodes implanted in the brain, will be able to provide multidimensional sequential control of a robotic arm or a neuroprosthesis. The present study shows that a noninvasive BCI using scalp-recorded electroencephalographic (EEG) activity and an adaptive algorithm can provide people, including people with spinal cord injuries, with two-dimensional cursor movement and target selection. Multiple targets were presented around the periphery of a computer screen, with one designated as the correct target. The user{\textquoteright}s task was to use EEG to move a cursor from the center of the screen to the correct target and then to use an additional EEG feature to select the target. If the cursor reached an incorrect target, the user was instructed not to select it. Thus, this task emulated the key features of mouse operation. The results indicate that people with severe motor disabilities could use brain signals for sequential multidimensional movement and selection.},
	keywords = {User-Computer Interface},
	issn = {1741-2560},
	doi = {10.1088/1741-2560/5/2/001},
	url = {http://www.ncbi.nlm.nih.gov/pubmed/18367779},
	author = {Dennis J. McFarland and Krusienski, Dean J. and Sarnacki, William A. and Jonathan Wolpaw}
}
@article {3135,
	title = {BCI Meeting 2005{\textendash}workshop on BCI signal processing: feature extraction and translation.},
	journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
	volume = {14},
	year = {2006},
	month = {06/2006},
	pages = {135{\textendash}138},
	abstract = {This paper describes the outcome of discussions held during the Third International BCI Meeting at a workshop charged with reviewing and evaluating the current state of and issues relevant to brain-computer interface (BCI) feature extraction and translation. The issues discussed include a taxonomy of methods and applications, time-frequency spatial analysis, optimization schemes, the role of insight in analysis, adaptation, and methods for quantifying BCI feedback.},
	keywords = {Brain-computer interface (BCI), prediction, Signal Processing},
	issn = {1534-4320},
	doi = {10.1109/TNSRE.2006.875637},
	url = {http://www.ncbi.nlm.nih.gov/pubmed/16792278},
	author = {Dennis J. McFarland and Anderson, Charles W. and M{\"u}ller, Klaus-Robert and Schl{\"o}gl, Alois and Krusienski, Dean J.}
}
@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 {3212,
	title = {A comparison of classification techniques for the P300 Speller.},
	journal = {Journal of neural engineering},
	volume = {3},
	year = {2006},
	month = {12/2006},
	pages = {299{\textendash}305},
	abstract = {This study assesses the relative performance characteristics of five established classification techniques on data collected using the P300 Speller paradigm, originally described by Farwell and Donchin (1988 Electroenceph. Clin. Neurophysiol. 70 510). Four linear methods: Pearson{\textquoteright}s correlation method (PCM), Fisher{\textquoteright}s linear discriminant (FLD), stepwise linear discriminant analysis (SWLDA) and a linear support vector machine (LSVM); and one nonlinear method: Gaussian kernel support vector machine (GSVM), are compared for classifying offline data from eight users. The relative performance of the classifiers is evaluated, along with the practical concerns regarding the implementation of the respective methods. The results indicate that while all methods attained acceptable performance levels, SWLDA and FLD provide the best overall performance and implementation characteristics for practical classification of P300 Speller data.},
	keywords = {Normal Distribution},
	issn = {1741-2560},
	doi = {10.1088/1741-2560/3/4/007},
	url = {http://www.ncbi.nlm.nih.gov/pubmed/17124334},
	author = {Krusienski, Dean J. and Sellers, Eric W. and Cabestaing, Fran{\c c}ois and Bayoudh, Sabri and Dennis J. McFarland and Theresa M Vaughan and Jonathan Wolpaw}
}
@article {3134,
	title = {A P300 event-related potential brain-computer interface (BCI): the effects of matrix size and inter stimulus interval on performance.},
	journal = {Biological psychology},
	volume = {73},
	year = {2006},
	month = {10/2006},
	pages = {242{\textendash}252},
	abstract = {We describe a study designed to assess properties of a P300 brain-computer interface (BCI). The BCI presents the user with a matrix containing letters and numbers. The user attends to a character to be communicated and the rows and columns of the matrix briefly intensify. Each time the attended character is intensified it serves as a rare event in an oddball sequence and it elicits a P300 response. The BCI works by detecting which character elicited a P300 response. We manipulated the size of the character matrix (either 3 x 3 or 6 x 6) and the duration of the inter stimulus interval (ISI) between intensifications (either 175 or 350 ms). Online accuracy was highest for the 3 x 3 matrix 175-ms ISI condition, while bit rate was highest for the 6 x 6 matrix 175-ms ISI condition. Average accuracy in the best condition for each subject was 88\%. P300 amplitude was significantly greater for the attended stimulus and for the 6 x 6 matrix. This work demonstrates that matrix size and ISI are important variables to consider when optimizing a BCI system for individual users and that a P300-BCI can be used for effective communication.},
	keywords = {Amyotrophic Lateral Sclerosis, brain-computer interface, electroencephalogram, event-related potentials, P300, Rehabilitation},
	issn = {0301-0511},
	doi = {10.1016/j.biopsycho.2006.04.007},
	url = {http://www.ncbi.nlm.nih.gov/pubmed/16860920},
	author = {Sellers, Eric W. and Krusienski, Dean J. and Dennis J. McFarland and Theresa M Vaughan and Jonathan Wolpaw}
}
