01990nas a2200361   4500008004100000020002200041245002800063210002700091260003500118520105300153100002101206700001601227700001501243700002401258700001901282700001301301700001501314700001501329700001401344700001701358700001401375700001401389700001901403700001601422700001501438700002201453700002501475700001301500700001201513700002201525700001501547856006601562       2013                        eng d  a978-3-642-29745-800aBCI Software Platforms.0 aBCI Software Platforms  bBiological and Medical Physics3 aIn this chapter, we provide an overview of publicly available software platforms for brain–computer interfaces. We have identified seven major BCI platforms and one platform specifically targeted towards feedback and stimulus presentation. We describe the intended target user group (which includes researchers, programmers, and end users), the most important features of each platform such as availability on different operating systems, licences, programming languages involved, supported devices, and so on. These seven platforms are: (1) BCI2000, (2) OpenViBE, (3) TOBI Common Implementation Platform (CIP), (4) BCILAB, (5) BCI++, (6) xBCI, and (7) BF++. The feedback framework is called Pyff. Our conclusion discusses possible synergies and future developments, such as combining different components of different platforms. With this overview, we hope to identify the strengths and weaknesses of each available platform, which should help anyone in the BCI research field in their decision which platform to use for their specific purposes.1 aBrunner, Clemens1 aAndreoni, G1 aBianchi, L1 aBlankertz, Benjamin1 aBreitwieser, C1 aKanoh, S1 aKothe, C A1 aLecuyer, A1 aMakeig, S1 aMellinger, J1 aPerego, P1 aRenard, Y1 aSchalk, Gerwin1 aSusila, I P1 aVenthur, B1 aMueller-Putz, G R1 aAllison, Brendan, Z.1 aDunne, S1 aLeeb, R1 aDel R. Millán, J1 aNijholt, A  uhttp://link.springer.com/chapter/10.1007/978-3-642-29746-5_1602392nas a2200361   4500008004100000022001400041245008900055210006900144260001200213300001600225490000800241520135100249653003401600653002901634653002501663653002901688653000901717653001901726100001601745700002001761700001701781700001901798700001301817700001901830700001401849700001501863700002301878700002401901700002101925700002101946700001501967856004801982       2008                        eng d  a1388-245700aA P300-based brain-computer interface for people with amyotrophic lateral sclerosis.0 aP300based braincomputer interface for people with amyotrophic la  c08/2008  a1909–19160 v1193 aOBJECTIVE:
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.10aAmyotrophic Lateral Sclerosis10abrain-computer interface10aelectroencephalogram10aevent-related potentials10aP30010aRehabilitation1 aNijboer, F.1 aSellers, E., W.1 aMellinger, J1 aJordan, M., A.1 aMatuz, T1 aFurdea, Adrian1 aHalder, S1 aMochty, U.1 aKrusienski, D., J.1 aVaughan, Theresa, M1 aWolpaw, Jonathan1 aBirbaumer, Niels1 aKübler, A  uhttp://www.ncbi.nlm.nih.gov/pubmed/1857198401195nas a2200253   4500008004100000022001400041245009000055210006900145260001200214300001600226490000700242520044000249653002800689100001500717700001600732700001700748700002400765700001700789700001900806700002600825700002100851700002100872856004800893       2005                        eng d  a1526-632X00aPatients with ALS can use sensorimotor rhythms to operate a brain-computer interface.0 aPatients with ALS can use sensorimotor rhythms to operate a brai  c05/2005  a1775–17770 v643 aPeople with severe motor disabilities can maintain an acceptable quality of life if they can communicate. Brain-computer interfaces (BCIs), which do not depend on muscle control, can provide communication. Four people severely disabled by ALS learned to operate a BCI with EEG rhythms recorded over sensorimotor cortex. These results suggest that a sensorimotor rhythm-based BCI could help maintain quality of life for people with ALS.10aUser-Computer Interface1 aKübler, A1 aNijboer, F.1 aMellinger, J1 aVaughan, Theresa, M1 aPawelzik, H.1 aSchalk, Gerwin1 aMcFarland, Dennis, J.1 aBirbaumer, Niels1 aWolpaw, Jonathan  uhttp://www.ncbi.nlm.nih.gov/pubmed/15911809