A simple classification tool for single-trial analysis of ERP components
Event‐related potentials (ERPs) were recorded by measuring a dense sensor EEG from eight healthy volunteers in a visual oddball experiment. Single trials were analyzed with an extremely simple high‐dimensional version of discriminant analysis. The question was how many of the target trials contribut...
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| Published in | Psychophysiology Vol. 46; no. 4; pp. 747 - 757 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Malden, USA
Blackwell Publishing Inc
01.07.2009
Blackwell Publishing Ltd |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0048-5772 1469-8986 1469-8986 1540-5958 |
| DOI | 10.1111/j.1469-8986.2009.00816.x |
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| Abstract | Event‐related potentials (ERPs) were recorded by measuring a dense sensor EEG from eight healthy volunteers in a visual oddball experiment. Single trials were analyzed with an extremely simple high‐dimensional version of discriminant analysis. The question was how many of the target trials contribute to the average P3, and to test whether other components in the ERP are sensitive to discriminate between target and non‐target trials. One common classification rule for all participants expressing the P3 component correctly classified 88% of the ERPs of all subjects in response to a target or non‐target trial. For four of the eight participants, there were strong differences in an early ERP component over the occipital recording sites. Their individual classification rules, obtained from the training data in the time interval up to 200 ms, correctly classified 85% of the trials of the test data. |
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| AbstractList | Event-related potentials (ERPs) were recorded by measuring a dense sensor EEG from eight healthy volunteers in a visual oddball experiment. Single trials were analyzed with an extremely simple high-dimensional version of discriminant analysis. The question was how many of the target trials contribute to the average P3, and to test whether other components in the ERP are sensitive to discriminate between target and non-target trials. One common classification rule for all participants expressing the P3 component correctly classified 88% of the ERPs of all subjects in response to a target or non-target trial. For four of the eight participants, there were strong differences in an early ERP component over the occipital recording sites. Their individual classification rules, obtained from the training data in the time interval up to 200 ms, correctly classified 85% of the trials of the test data. [PUBLICATION ABSTRACT] Event-related potentials (ERPs) were recorded by measuring a dense sensor EEG from eight healthy volunteers in a visual oddball experiment. Single trials were analyzed with an extremely simple high-dimensional version of discriminant analysis. The question was how many of the target trials contribute to the average P3, and to test whether other components in the ERP are sensitive to discriminate between target and non-target trials. One common classification rule for all participants expressing the P3 component correctly classified 88% of the ERPs of all subjects in response to a target or non-target trial. For four of the eight participants, there were strong differences in an early ERP component over the occipital recording sites. Their individual classification rules, obtained from the training data in the time interval up to 200 ms, correctly classified 85% of the trials of the test data.Event-related potentials (ERPs) were recorded by measuring a dense sensor EEG from eight healthy volunteers in a visual oddball experiment. Single trials were analyzed with an extremely simple high-dimensional version of discriminant analysis. The question was how many of the target trials contribute to the average P3, and to test whether other components in the ERP are sensitive to discriminate between target and non-target trials. One common classification rule for all participants expressing the P3 component correctly classified 88% of the ERPs of all subjects in response to a target or non-target trial. For four of the eight participants, there were strong differences in an early ERP component over the occipital recording sites. Their individual classification rules, obtained from the training data in the time interval up to 200 ms, correctly classified 85% of the trials of the test data. Event-related potentials (ERPs) were recorded by measuring a dense sensor EEG from eight healthy volunteers in a visual oddball experiment. Single trials were analyzed with an extremely simple high-dimensional version of discriminant analysis. The question was how many of the target trials contribute to the average P3, and to test whether other components in the ERP are sensitive to discriminate between target and non-target trials. One common classification rule for all participants expressing the P3 component correctly classified 88% of the ERPs of all subjects in response to a target or non-target trial. For four of the eight participants, there were strong differences in an early ERP component over the occipital recording sites. Their individual classification rules, obtained from the training data in the time interval up to 200 ms, correctly classified 85% of the trials of the test data. AbstractEvent-related potentials (ERPs) were recorded by measuring a dense sensor EEG from eight healthy volunteers in a visual oddball experiment. Single trials were analyzed with an extremely simple high-dimensional version of discriminant analysis. The question was how many of the target trials contribute to the average P3, and to test whether other components in the ERP are sensitive to discriminate between target and non-target trials. One common classification rule for all participants expressing the P3 component correctly classified 88% of the ERPs of all subjects in response to a target or non-target trial. For four of the eight participants, there were strong differences in an early ERP component over the occipital recording sites. Their individual classification rules, obtained from the training data in the time interval up to 200 ms, correctly classified 85% of the trials of the test data. |
| Author | Bandt, Christoph Hamm, Alfons O. Weymar, Mathias Samaga, Daniel |
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| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/19386045$$D View this record in MEDLINE/PubMed |
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| References_xml | – reference: Hanley, J. A. (1989). Receiver operating characteristic (ROC) methodology. The state of the art. Critical Reviews in Diagnostic Imaging, 29 (3), 307-335. – reference: Jansen, B. H., Allam, A., Kota, P., Lachance, K., Osho, A., & Sundaresan, K. (2004). An exploratory study of factors affecting single trial P300 detection. IEEE Transactions on Biomedical Engineering, 51, 973-978. – reference: Krusienski, D. J., Sellers, E. W., McFarland, D. J., Vaughan, T. M., & Wolpaw, J. R. (2008). Toward enhanced P300 speller performance. Journal of Neuroscience Methods, 167, 15-21. – reference: Effern, A., Lehnertz, K., Grunwald, T., Fernández, G., David, P., & Elger, C. E. (2000). Time adaptive denoising of single trial event-related potentials in the wavelet domain. Psychophysiology, 37, 859-865. – reference: Makeig, S., Debener, S., Onton, J., & Delorme, A. (2004). Mining event-related brain dynamics. Trends in Cognitive Sciences, 8 (5), 204-210. – reference: Thorpe, S., Fize, D., & Marlot, C. (1996). Speed of processing in the human visual system. Nature, 381, 520-522. – reference: Armitage, P., & Berry, G. (1994). Statistical methods in medical research. Boston: Blackwell Scientific Publications. – reference: Horvath, J., Czigler, I., Jacobsen, T., Maess, B., Schröger, E., & Winkler, I. (2008). MMN or no MMN: No magnitude of deviance effect on the MMN amplitude. Psychophysiology, 45, 60-69. – reference: Huberty, C. J. (1994). Applied discriminant analysis. New York: Wiley and Sons. – reference: Farwell, L. A., & Donchin, E. (1988). Talking off the top of your head: Toward a mental prothesis utilizing event-related brain potentials. Electroencephalography and Clinical Neurophysiology, 70, 510-523. – reference: Mensh, B. D., Werfel, J., & Seung, H. S. (2004). BCI Competition 2003-data set 1a: Combining gamma-band power with slow cortical potentials to improve single-trial classification of electroencephalographic signals. IEEE Transactions on Biomedical Engineering, 51, 1052-1056. – reference: Jáskowski, P., & Verleger, R. (2000). An evaluation of methods for single-trial estimation of P3 latency. Psychophysiology, 37, 153-162. – reference: Wolpaw, J. R., Birbaumer, N., McFarland, D. J., Pfurtscheller, G., & Vaughan, T. M. (2002). Brain-computer interfaces for communication and control. Clinical Neurophysiology, 113, 767-791. – reference: Delorme, A., & Makeig, S. (2004). EEGLAB: An open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. Journal of Neuroscience Methods, 134, 9-21. – reference: McLachlan, G. J. (1992). Discriminant analysis and statistical pattern recognition. New York: Wiley and Sons. – reference: Schinkel, S., Marwan, N., & Kurths, J. (2007). 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| Snippet | Event‐related potentials (ERPs) were recorded by measuring a dense sensor EEG from eight healthy volunteers in a visual oddball experiment. Single trials were... Event-related potentials (ERPs) were recorded by measuring a dense sensor EEG from eight healthy volunteers in a visual oddball experiment. Single trials were... AbstractEvent-related potentials (ERPs) were recorded by measuring a dense sensor EEG from eight healthy volunteers in a visual oddball experiment. Single... |
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| SubjectTerms | Adult Classification Data Interpretation, Statistical Discriminant analysis Electroencephalography - classification ERP components Evoked Potentials - physiology Humans Learning - physiology Male Neurosciences Physiological psychology Psychomotor Performance - physiology Single trial Visual oddball experiment Young Adult |
| Title | A simple classification tool for single-trial analysis of ERP components |
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