An auditory brain-computer interface (BCI). - Nijboer, Femke; Adrian Furdea; Gunst, Ingo; Mellinger, Jürgen; Dennis J. McFarland; Niels Birbaumer; Kübler, Andrea (Journal of neuroscience methods, 2008)
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

A P300-based brain-computer interface for people with amyotrophic lateral sclerosis. - Nijboer, F.; Sellers, E. W.; Mellinger, J.; Jordan, M. A.; Matuz, T.; Adrian Furdea; S Halder; Mochty, U.; Krusienski, D. J.; Theresa M Vaughan; Jonathan Wolpaw; Niels Birbaumer; et al. (Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology, 2008)
OBJECTIVE: The current study evaluates the efficacy of a P300-based brain-computer interface (BCI) communication device for individuals with advanced ALS. METHODS: Participants attended to one cell of a N x N matrix while the N rows and N columns flashed randomly. Each cell of the matrix contained one character. Every flash of an attended character served as a rare event in an oddball sequence and elicited a P300 response. Classification coefficients derived using a stepwise linear discriminant function were applied to the data after each set of flashes. The character receiving the highest discriminant score was presented as feedback. RESULTS: In Phase I, six participants used a 6 x 6 matrix on 12 separate days with a mean rate of 1.2 selections/min and mean online and offline accuracies of 62% and 82%, respectively. In Phase II, four participants used either a 6 x 6 or a 7 x 7 matrix to produce novel and spontaneous statements with a mean online rate of 2.1 selections/min and online accuracy of 79%. The amplitude and latency of the P300 remained stable over 40 weeks. CONCLUSIONS: Participants could communicate with the P300-based BCI and performance was stable over many months. SIGNIFICANCE: BCIs could provide an alternative communication and control technology in the daily lives of people severely disabled by ALS.
Keywords: Amyotrophic Lateral Sclerosis; brain-computer interface; electroencephalogram; event-related potentials; P300; Rehabilitation