@article {3120,
	title = {Characterizing multivariate decoding models based on correlated EEG spectral features.},
	journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
	volume = {124},
	year = {2013},
	month = {07/2013},
	pages = {1297{\textendash}1302},
	abstract = {OBJECTIVE:
Multivariate decoding methods are popular techniques for analysis of neurophysiological data. The present study explored potential interpretative problems with these techniques when predictors are correlated.
METHODS:
Data from sensorimotor rhythm-based cursor control experiments was analyzed offline with linear univariate and multivariate models. Features were derived from autoregressive (AR) spectral analysis of varying model order which produced predictors that varied in their degree of correlation (i.e., multicollinearity).
RESULTS:
The use of multivariate regression models resulted in much better prediction of target position as compared to univariate regression models. However, with lower order AR features interpretation of the spectral patterns of the weights was difficult. This is likely to be due to the high degree of multicollinearity present with lower order AR features.
CONCLUSIONS:
Care should be exercised when interpreting the pattern of weights of multivariate models with correlated predictors. Comparison with univariate statistics is advisable.
SIGNIFICANCE:
While multivariate decoding algorithms are very useful for prediction their utility for interpretation may be limited when predictors are correlated.},
	keywords = {brain-computer interface, multicollinearity, multivariate decoding, sensorimotor rhythm},
	issn = {1872-8952},
	doi = {10.1016/j.clinph.2013.01.015},
	url = {http://www.ncbi.nlm.nih.gov/pubmed/23466267},
	author = {Dennis J. McFarland}
}
@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 {3132,
	title = {An auditory brain-computer interface (BCI).},
	journal = {Journal of neuroscience methods},
	volume = {167},
	year = {2008},
	month = {01/2008},
	pages = {43{\textendash}50},
	abstract = {Brain-computer interfaces (BCIs) translate brain activity into signals controlling external devices. BCIs based on visual stimuli can maintain communication in severely paralyzed patients, but only if intact vision is available. Debilitating neurological disorders however, may lead to loss of intact vision. The current study explores the feasibility of an auditory BCI. Sixteen healthy volunteers participated in three training sessions consisting of 30 2-3 min runs in which they learned to increase or decrease the amplitude of sensorimotor rhythms (SMR) of the EEG. Half of the participants were presented with visual and half with auditory feedback. Mood and motivation were assessed prior to each session. Although BCI performance in the visual feedback group was superior to the auditory feedback group there was no difference in performance at the end of the third session. Participants in the auditory feedback group learned slower, but four out of eight reached an accuracy of over 70\% correct in the last session comparable to the visual feedback group. Decreasing performance of some participants in the visual feedback group is related to mood and motivation. We conclude that with sufficient training time an auditory BCI may be as efficient as a visual BCI. Mood and motivation play a role in learning to use a BCI.},
	keywords = {auditory feedback, brain-computer interface, EEG, locked-in state, motivation, sensorimotor rhythm},
	issn = {0165-0270},
	doi = {10.1016/j.jneumeth.2007.02.009},
	url = {http://www.ncbi.nlm.nih.gov/pubmed/17399797},
	author = {Nijboer, Femke and Adrian Furdea and Gunst, Ingo and Mellinger, J{\"u}rgen and Dennis J. McFarland and Niels Birbaumer and K{\"u}bler, Andrea}
}
@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.}
}
