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      • Record: found
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      Multisubject Learning for Common Spatial Patterns in Motor-Imagery BCI

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          Abstract

          Motor-imagery-based brain-computer interfaces (BCIs) commonly use the common spatial pattern filter (CSP) as preprocessing step before feature extraction and classification. The CSP method is a supervised algorithm and therefore needs subject-specific training data for calibration, which is very time consuming to collect. In order to reduce the amount of calibration data that is needed for a new subject, one can apply multitask (from now on called multisubject) machine learning techniques to the preprocessing phase. Here, the goal of multisubject learning is to learn a spatial filter for a new subject based on its own data and that of other subjects. This paper outlines the details of the multitask CSP algorithm and shows results on two data sets. In certain subjects a clear improvement can be seen, especially when the number of training trials is relatively low.

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          Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials

          L.A. Farwell, E Donchin (1988)
          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.
          Article 0 comments Cited 384 times – based on 0 reviews     Review now
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            Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials.

            Lawrence A. Farwell, E Donchin, L Farwell (1988)
            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.
            Article 0 comments Cited 158 times – based on 0 reviews     Review now
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              Designing optimal spatial filters for single-trial EEG classification in a movement task.

              Johannes Müller-Gerking, Gert Pfurtscheller, Henrik Flyvbjerg (1999)
              We devised spatial filters for multi-channel EEG that lead to signals which discriminate optimally between two conditions. We demonstrate the effectiveness of this method by classifying single-trial EEGs, recorded during preparation for movements of the left or right index finger or the right foot. The classification rates for 3 subjects were 94, 90 and 84%, respectively. The filters are estimated from a set of multichannel EEG data by the method of Common Spatial Patterns, and reflect the selective activation of cortical areas. By construction, we obtain an automatic weighting of electrodes according to their importance for the classification task. Computationally, this method is parallel by nature, and demands only the evaluation of scalar products. Therefore, it is well suited for on-line data processing. The recognition rates obtained with this relatively simple method are as good as, or higher than those obtained previously with other methods. The high recognition rates and the method's procedural and computational simplicity make it a particularly promising method for an EEG-based brain-computer interface.
              Article 0 comments Cited 125 times – based on 0 reviews     Review now
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                Author and article information

                Journal
                Journal ID (nlm-ta): Comput Intell Neurosci
                Journal ID (publisher-id): CIN
                Title: Computational Intelligence and Neuroscience
                Publisher: Hindawi Publishing Corporation
                ISSN (Print): 1687-5265
                ISSN (Electronic): 1687-5273
                Publication date (Print): 2011
                Publication date (Electronic): 11 October 2011
                Volume: 2011
                Electronic Location Identifier: 217987
                Affiliations
                1Electrical Energy, Systems and Automation, Ghent University, Technologiepark 913, Zwijnaarde, 9052 Gent, Belgium
                2Department of Empirical Inference, Max Planck Institute for Biological Cybernetics, Spemannstr. 38, 72076 Tübingen, Germany
                3P.C. Dr. Guislain, Fr. Ferrerlaan 88A, 9000 Gent, Belgium
                4Department of Neurology, Ghent University Hospital, De Pintelaan 185, 9000 Gent, Belgium
                Author notes
                *Dieter Devlaminck: ddvlamin@ 123456gmail.com

                Academic Editor: Fabio Babiloni

                Article
                DOI: 10.1155/2011/217987
                PMC ID: 3191786
                PubMed ID: 22007194
                SO-VID: 8509c941-78a2-480d-8456-d2820e0ec1b8
                Copyright © Copyright © 2011 Dieter Devlaminck et al.
                License:

                This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                Date received : 14 March 2011
                Date revision received : 28 July 2011
                Date accepted : 29 July 2011
                Categories
                Subject: Research Article

                ScienceOpen disciplines: Neurosciences
                Data availability:
                ScienceOpen disciplines: Neurosciences

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