@article {3415,
	title = {Toward independent home use of brain-computer interfaces: a decision algorithm for selection of potential end-users.},
	journal = {Arch Phys Med Rehabil},
	volume = {96},
	year = {2015},
	month = {03/2015},
	pages = {S27-32},
	abstract = {<p>Noninvasive brain-computer interfaces (BCIs) use scalp-recorded electrical activity from the brain to control an application. Over the past 20 years, research demonstrating that BCIs can provide communication and control to individuals with severe motor impairment has increased almost exponentially. Although considerable effort has been dedicated to offline analysis for improving signal detection and translation, far less effort has been made to conduct online studies with target populations. Thus, there remains a great need for both long-term and translational BCI studies that include individuals with disabilities in their own homes. Completing these studies is the only sure means to answer questions about BCI utility and reliability. Here we suggest an algorithm for candidate selection for electroencephalographic (EEG)-based BCI home studies. This algorithm takes into account BCI end-users and their environment and should assist in study design and substantially improve subject retention rates, thereby improving the overall efficacy of BCI home studies. It is the result of a workshop at the Fifth International BCI Meeting that allowed us to leverage the expertise of multiple research laboratories and people from multiple backgrounds in BCI research.</p>},
	keywords = {Algorithms, brain-computer interfaces, Cognition, Disabled Persons, Electroencephalography, Environment, Humans, Patient Selection, Physical Therapy Modalities},
	issn = {1532-821X},
	doi = {10.1016/j.apmr.2014.03.036},
	url = {http://www.ncbi.nlm.nih.gov/pubmed/25721544},
	author = {K{\"u}bler, Andrea and Holz, Elisa Mira and Sellers, Eric W and Theresa M Vaughan}
}
@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 {3226,
	title = {Brain-computer interface technology: a review of the Second International Meeting.},
	journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
	volume = {11},
	year = {2003},
	month = {06/2003},
	pages = {94{\textendash}109},
	abstract = {This paper summarizes the Brain-Computer Interfaces for Communication and Control, The Second International Meeting, held in Rensselaerville, NY, in June 2002. Sponsored by the National Institutes of Health and organized by the Wadsworth Center of the New York State Department of Health, the meeting addressed current work and future plans in brain-computer interface (BCI) research. Ninety-two researchers representing 38 different research groups from the United States, Canada, Europe, and China participated. The BCIs discussed at the meeting use electroencephalographic activity recorded from the scalp or single-neuron activity recorded within cortex to control cursor movement, select letters or icons, or operate neuroprostheses. The central element in each BCI is a translation algorithm that converts electrophysiological input from the user into output that controls external devices. BCI operation depends on effective interaction between two adaptive controllers, the user who encodes his or her commands in the electrophysiological input provided to the BCI, and the BCI that recognizes the commands contained in the input and expresses them in device control. Current BCIs have maximum information transfer rates of up to 25 b/min. Achievement of greater speed and accuracy requires improvements in signal acquisition and processing, in translation algorithms, and in user training. These improvements depend on interdisciplinary cooperation among neuroscientists, engineers, computer programmers, psychologists, and rehabilitation specialists, and on adoption and widespread application of objective criteria for evaluating alternative methods. The practical use of BCI technology will be determined by the development of appropriate applications and identification of appropriate user groups, and will require careful attention to the needs and desires of individual users.},
	keywords = {augmentative communication, Brain-computer interface (BCI), electroencephalography (EEG), Rehabilitation},
	issn = {1534-4320},
	doi = {10.1109/TNSRE.2003.814799},
	url = {http://www.ncbi.nlm.nih.gov/pubmed/12899247},
	author = {Theresa M Vaughan and Heetderks, William J. and Trejo, Leonard J. and Rymer, William Z. and Weinrich, Michael and Moore, Melody M. and K{\"u}bler, Andrea and Dobkin, Bruce H. and Niels Birbaumer and Emanuel Donchin and Wolpaw, Elizabeth Winter and Jonathan Wolpaw}
}
