02766nas a2200205   4500008004100000245009500041210007100136260001200207490000700219520209000226653002902316653002102345653003002366653002102396653002702417100002502444700002402469700001902493856004802512       2014                        eng d00aA general method for assessing brain–computer interface performance and its limitations.0 ageneral method for assessing brain–computer interface performanc  c03/20140 v113 aObjective. When researchers evaluate brain–computer interface (BCI) systems, we want quantitative answers to questions such as: How good is the system's performance? How good does it need to be? and: Is it capable of reaching the desired level in future? In response to the current lack of objective, quantitative, study-independent approaches, we introduce methods that help to address such questions. We identified three challenges: (I) the need for efficient measurement techniques that adapt rapidly and reliably to capture a wide range of performance levels; (II) the need to express results in a way that allows comparison between similar but non-identical tasks; (III) the need to measure the extent to which certain components of a BCI system (e.g. the signal processing pipeline) not only support BCI performance, but also potentially restrict the maximum level it can reach. Approach. For challenge (I), we developed an automatic staircase method that adjusted task difficulty adaptively along a single abstract axis. For challenge (II), we used the rate of information gain between two Bernoulli distributions: one reflecting the observed success rate, the other reflecting chance performance estimated by a matched random-walk method. This measure includes Wolpaw's information transfer rate as a special case, but addresses the latter's limitations including its restriction to item-selection tasks. To validate our approach and address challenge (III), we compared four healthy subjects' performance using an EEG-based BCI, a 'Direct Controller' (a high-performance hardware input device), and a 'Pseudo-BCI Controller' (the same input device, but with control signals processed by the BCI signal processing pipeline). Main results. Our results confirm the repeatability and validity of our measures, and indicate that our BCI signal processing pipeline reduced attainable performance by about 33% (21 bits/min). Significance. Our approach provides a flexible basis for evaluating BCI performance and its limitations, across a wide range of tasks and task difficulties.10abrain-computer interface10ainformation gain10ainformation transfer rate10aNeuroprosthetics10aperformance evaluation1 aHill, Jeremy, Jeremy1 aHäuser, Ann-Katrin1 aSchalk, Gerwin  uhttp://www.ncbi.nlm.nih.gov/pubmed/2465840602832nas a2200253   4500008004100000022001400041245012700055210006900182260001200251490000600263520200400269653002302273653003802296653002902334653002302363653001702386653001702403100002502420700002102445700002402466700002102490700001902511856004802530       2012                        eng d  a1662-453X00aCommunication and control by listening: towards optimal design of a two-class auditory streaming brain-computer interface.0 aCommunication and control by listening towards optimal design of  c12/20120 v63 aMost brain-computer interface (BCI) systems require users to modulate brain signals in response to visual stimuli. Thus, they may not be useful to people with limited vision, such as those with severe paralysis. One important approach for overcoming this issue is auditory streaming, an approach whereby a BCI system is driven by shifts of attention between two simultaneously presented auditory stimulus streams. Motivated by the long-term goal of translating such a system into a reliable, simple yes-no interface for clinical usage, we aim to answer two main questions. First, we asked which of two previously published variants provides superior performance: a fixed-phase (FP) design in which the streams have equal period and opposite phase, or a drifting-phase (DP) design where the periods are unequal. We found FP to be superior to DP (p = 0.002): average performance levels were 80 and 72% correct, respectively. We were also able to show, in a pilot with one subject, that auditory streaming can support continuous control and neurofeedback applications: by shifting attention between ongoing left and right auditory streams, the subject was able to control the position of a paddle in a computer game. Second, we examined whether the system is dependent on eye movements, since it is known that eye movements and auditory attention may influence each other, and any dependence on the ability to move one’s eyes would be a barrier to translation to paralyzed users. We discovered that, despite instructions, some subjects did make eye movements that were indicative of the direction of attention. However, there was no correlation, across subjects, between the reliability of the eye movement signal and the reliability of the BCI system, indicating that our system was configured to work independently of eye movement. Together, these findings are an encouraging step forward toward BCIs that provide practical communication and control options for the most severely paralyzed users.
10aauditory attention10aauditory event-related potentials10abrain-computer interface10adichotic listening10aN1 potential10aP3 potential1 aHill, Jeremy, Jeremy1 aMoinuddin, Aisha1 aHäuser, Ann-Katrin1 aKienzle, Stephan1 aSchalk, Gerwin  uhttp://www.ncbi.nlm.nih.gov/pubmed/23267312