Single-trial EEG classification in a visual detection task
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1
German Centre for Neurodegenerative Diseases (DZNE), Germany
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2
Carl-von-Ossietzky Universität, Germany
Real-time recognition of cognitive or perceptive states is of special relevance for applications in human-machine interaction. Techniques based on functional magnetic resonance imaging have already been applied with success. Classification relying on features of the electroencephalogram (EEG), however, stays to be challenging due to the inherently low signal-to-noise ratio of electrophysiological single-trial activity. We used data from a visual detection task with varying stimulus intensities to predict the performance of 14 subjects at single trials from electrophysiological responses. Two sets of EEG features were derived from occipital electrodes via t-test based selection procedures: amplitudes of N200 and P300 event-related potentials (ERPs) as well as power in four frequency bands (delta: 1-4 Hz, theta: 5-8 Hz, alpha: 9-12 Hz, beta: 13-30 Hz) between 200 and 300 ms after stimulus presentation. For each set of features a support vector machine was trained using a radial basis kernel function to classify trials according to whether a stimulus was detected or not. From the total number of trials available (about 600 per subject) a fifth was used to conduct parameter estimation. On the remaining trials four-fold cross-validation was employed to assess the classifiers’ performance. Across conditions ERP-based classification performed at chance level only. Using time-frequency features a maximum of 84% with an average of 63% correct identifications was achieved. Theta and alpha frequency bands served as significantly better predictors than delta and beta band activity. Hence, time-frequency decomposition of electrophysiological signals provides a tool for feature generation even in low-signal conditions obtained under realistic task constellations.
Keywords:
Brain Signals,
EEG
Conference:
XI International Conference on Cognitive Neuroscience (ICON XI), Palma, Mallorca, Spain, 25 Sep - 29 Sep, 2011.
Presentation Type:
Poster Presentation
Topic:
Poster Sessions: Modeling and Analysis of Brain Signals
Citation:
Naue
N,
Huster
RJ and
Herrmann
CS
(2011). Single-trial EEG classification in a visual detection task.
Conference Abstract:
XI International Conference on Cognitive Neuroscience (ICON XI).
doi: 10.3389/conf.fnhum.2011.207.00172
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Received:
18 Nov 2011;
Published Online:
28 Nov 2011.
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Correspondence:
Dr. Nicole Naue, German Centre for Neurodegenerative Diseases (DZNE), Magdeburg, Germany, nicole.naue@gmx.de