Decoding perceptual states of ambiguous motion from high gamma EEG
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1
University of Bremen, Department for Theoretical Physics, Center for Cognitive Sciences, Germany
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2
University of Bremen, Institute of Psychology and Cognition Research, Germany
Recently, it was shown that the perceptual experience of a viewer can be tracked using multivariate analysis on non-invasive functional magnetic resonance imaging (fMRI) data. In these experiments time series of three-dimensional images related to brain activity were successfully classified using machine learning methods like Support Vector Machines (SVM). In a similar line of research, cognitive states were distinguished in individual trials, such as the two possible perspectives in binocular rivalry. In this project we investigate if and how the bistable perception of a human viewer observing an ambiguous stimulus could be decoded from electroencephalographic (EEG) time series. For this purpose, we classify the direction of motion of the stroboscopic ambiguous motion (SAM) pattern, which is known to be functionally related to oscillatory activity in the delta, alpha and gamma band of the EEG. Taking advantage of the high temporal resolution of EEG data, we use SVMs that operate in the time-frequency domain in order to study the oscillative coding of an ambiguous visual stimulus in the brain. Furthermore, by applying the same method to an unambiguous variant of the SAM we aim to study the specific coding of ambiguous stimuli.
Our results show that it is possible to detect the direction of motion on a single trial basis (data from 500 ms windows) with accuracy far above chance level (up to 69% with significance at least p<0.001). The best classification performance is reached using high frequency gamma-band power above 80 Hz, which suggests an underlying percept-related neuronal synchronization. In contrast, for the unambiguous stimulus variant no specific frequency band allows decoding, which possibly indicates the existence of a gamma-related Gestalt interpretation mechanism in the brain. Our findings demonstrate that dynamical mechanisms underlying specific mental contents in the human brain can be studied using modern machine learning methods in extension of conventional EEG research which uses average quantities to spatially and temporally localize cognitive features.
Conference:
Bernstein Conference on Computational Neuroscience, Frankfurt am Main, Germany, 30 Sep - 2 Oct, 2009.
Presentation Type:
Poster Presentation
Topic:
Neural encoding and decoding
Citation:
Schmiedt
JT,
Rotermund
D and
Basar-Eroglu
C
(2009). Decoding perceptual states of ambiguous motion from high gamma EEG.
Front. Comput. Neurosci.
Conference Abstract:
Bernstein Conference on Computational Neuroscience.
doi: 10.3389/conf.neuro.10.2009.14.102
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Received:
27 Aug 2009;
Published Online:
27 Aug 2009.
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Correspondence:
Joscha T Schmiedt, University of Bremen, Department for Theoretical Physics, Center for Cognitive Sciences, Bremen, Germany, joscha.schmiedt@esi-frankfurt.de