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      P300 brain computer interface: current challenges and emerging trends

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          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          A brain-computer interface (BCI) enables communication without movement based on brain signals measured with electroencephalography (EEG). BCIs usually rely on one of three types of signals: the P300 and other components of the event-related potential (ERP), steady state visual evoked potential (SSVEP), or event related desynchronization (ERD). Although P300 BCIs were introduced over twenty years ago, the past few years have seen a strong increase in P300 BCI research. This closed-loop BCI approach relies on the P300 and other components of the ERP, based on an oddball paradigm presented to the subject. In this paper, we overview the current status of P300 BCI technology, and then discuss new directions: paradigms for eliciting P300s; signal processing methods; applications; and hybrid BCIs. We conclude that P300 BCIs are quite promising, as several emerging directions have not yet been fully explored and could lead to improvements in bit rate, reliability, usability, and flexibility.

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          Most cited references 63

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          Human EEG responses to 1-100 Hz flicker: resonance phenomena in visual cortex and their potential correlation to cognitive phenomena.

          The individual properties of visual objects, like form or color, are represented in different areas in our visual cortex. In order to perceive one coherent object, its features have to be bound together. This was found to be achieved in cat and monkey brains by temporal correlation of the firing rates of neurons which code the same object. This firing rate is predominantly observed in the gamma frequency range (approx. 30-80 Hz, mainly around 40 Hz). In addition, it has been shown in humans that stimuli which flicker at gamma frequencies are processed faster by our brains than when they flicker at different frequencies. These effects could be due to neural oscillators, which preferably oscillate at certain frequencies, so-called resonance frequencies. It is also known that neurons in visual cortex respond to flickering stimuli at the frequency of the flickering light. If neural oscillators exist with resonance frequencies, they should respond more strongly to stimulation with their resonance frequency. We performed an experiment, where ten human subjects were presented flickering light at frequencies from 1 to 100 Hz in 1-Hz steps. The event-related potentials exhibited steady-state oscillations at all frequencies up to at least 90 Hz. Interestingly, the steady-state potentials exhibited clear resonance phenomena around 10, 20, 40 and 80 Hz. This could be a potential neural basis for gamma oscillations in binding experiments. The pattern of results resembles that of multiunit activity and local field potentials in cat visual cortex.
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            Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials.

            This paper describes the development and testing of a system whereby one can communicate through a computer by using the P300 component of the event-related brain potential (ERP). Such a system may be used as a communication aid by individuals who cannot use any motor system for communication (e.g., 'locked-in' patients). The 26 letters of the alphabet, together with several other symbols and commands, are displayed on a computer screen which serves as the keyboard or prosthetic device. The subject focuses attention successively on the characters he wishes to communicate. The computer detects the chosen character on-line and in real time. This detection is achieved by repeatedly flashing rows and columns of the matrix. When the elements containing the chosen character are flashed, a P300 is elicited, and it is this P300 that is detected by the computer. We report an analysis of the operating characteristics of the system when used with normal volunteers, who took part in 2 experimental sessions. In the first session (the pilot study/training session) subjects attempted to spell a word and convey it to a voice synthesizer for production. In the second session (the analysis of the operating characteristics of the system) subjects were required simply to attend to individual letters of a word for a specific number of trials while data were recorded for off-line analysis. The analyses suggest that this communication channel can be operated accurately at the rate of 0.20 bits/sec. In other words, under the conditions we used, subjects can communicate 12.0 bits, or 2.3 characters, per min.
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              Temporary suppression of visual processing in an RSVP task: an attentional blink? .

              Through rapid serial visual presentation (RSVP), we asked Ss to identify a partially specified letter (target) and then to detect the presence or absence of a fully specified letter (probe). Whereas targets are accurately identified, probes are poorly detected when they are presented during a 270-ms interval beginning 180 ms after the target. Probes presented immediately after the target or later in the RSVP stream are accurately detected. This temporary reduction in probe detection was not found in conditions in which a brief blank interval followed the target or Ss were not required to identify the target. The data suggest that the presentation of stimuli after the target but before target-identification processes are complete produces interference at a letter-recognition stage. This interference may cause the temporary suppression of visual attention mechanisms observed in the present study.
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                Author and article information

                Journal
                Front Neuroeng
                Front Neuroeng
                Front. Neuroeng.
                Frontiers in Neuroengineering
                Frontiers Media S.A.
                1662-6443
                17 July 2012
                2012
                : 5
                Affiliations
                1simpleBiomedical Signal and Image Processing Laboratory, Department of Electrical Engineering, University of North Dakota, Grand Forks ND, USA
                2simpleCognitive Neuroscience Laboratory, Department of Cognitive Science, University of California at San Diego, La Jolla CA, USA
                3simpleg.tec Medical Engineering GmbH/Guger Technologies OG Graz, Austria
                4simpleETSU Brain-Computer Interface Laboratory, East Tennessee State University, Johnson City TN, USA
                5simpleDepartment of Psychology I, University of Würzburg Würzburg, Germany
                Author notes

                Edited by: Ulrich G. Hofmann, Biosignal Processing and Neuro-Engineering Institute for Signal Processing University of Lübeck, Germany

                Reviewed by: Mehrnaz Hazrati, Institute of Signal Processing University of Lubeck, Germany; Tonio Ball, University of Freiburg, Germany

                *Correspondence: Reza Fazel-Rezai, Biomedical Signal and Image Processing Laboratory, Department of Electrical Engineering, University of North Dakota, 243 Centennial Dr., Stop 7165, Grand Forks, ND 58202-7165, USA. e-mail: reza@ 123456engr.und.edu
                Andrea Kübler, Department of Psychology I, University of Würzburg, Würzburg, Germany. e-mail: andrea.kuebler@ 123456uni-wuerzburg.de
                Article
                10.3389/fneng.2012.00014
                3398470
                22822397
                Copyright © 2012 Fazel-Rezai, Allison, Guger, Sellers, Kleih and Kübler.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in other forums, provided the original authors and source are credited and subject to any copyright notices concerning any third-party graphics etc.

                Page count
                Figures: 7, Tables: 1, Equations: 0, References: 80, Pages: 14, Words: 10581
                Categories
                Neuroscience
                Review Article

                Neurosciences

                p300, brain computer interface, event-related potential

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