@article {3356,
	title = {Assessing dynamics, spatial scale, and uncertainty in task-related brain network analyses.},
	journal = {Frontiers in Computational Neuroscience},
	volume = {8},
	year = {2014},
	month = {03/2014},
	abstract = {The brain is a complex network of interconnected elements, whose interactions evolve dynamically in time to cooperatively perform specific functions. A common technique to probe these interactions involves multi-sensor recordings of brain activity during a repeated task. Many techniques exist to characterize the resulting task-related activity, including establishing functional networks, which represent the statistical associations between brain areas. Although functional network inference is commonly employed to analyze neural time series data, techniques to assess the uncertainty{\textemdash}both in the functional network edges and the corresponding aggregate measures of network topology{\textemdash}are lacking. To address this, we describe a statistically principled approach for computing uncertainty in functional networks and aggregate network measures in task-related data. The approach is based on a resampling procedure that utilizes the trial structure common in experimental recordings. We show in simulations that this approach successfully identifies functional networks and associated measures of confidence emergent during a task in a variety of scenarios, including dynamically evolving networks. In addition, we describe a principled technique for establishing functional networks based on predetermined regions of interest using canonical correlation. Doing so provides additional robustness to the functional network inference. Finally, we illustrate the use of these methods on example invasive brain voltage recordings collected during an overt speech task. The general strategy described here{\textemdash}appropriate for static and dynamic network inference and different statistical measures of coupling{\textemdash}permits the evaluation of confidence in network measures in a variety of settings common to neuroscience.},
	keywords = {canonical correlation, coherence, ECoG, EEG, functional connectivity, MEG},
	doi = {10.3389/fncom.2014.00031},
	url = {http://www.ncbi.nlm.nih.gov/pubmed/24678295},
	author = {Stephen, Emily P and Lepage, Kyle Q and Eden, Uri T and Peter Brunner and Gerwin Schalk and Jonathan S Brumberg and Guenther, Frank H and Kramer, Mark A}
}
@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.}
}
