Informative Oscillatory EEG Components and their Persistence in Time and Frequency

Michael Tangermann, Andreas Meinel

Abstract

Oscillatory brain activity measured by the electroencephalogram, local field potentials or magnetoencephalogram can reflect cognitive processes. It can be used to run brain-computer interfaces or to analyze information processing, user learning and rehabilitation progress, e.g., after stroke. To extract oscillatory components, which are informative about a user’s task and which show an enhanced signal-to-noise compared to raw multivariate recordings, data-driven spatial filtering methods are widely applied. Some of these approaches can learn spatial filters from labeled data. They typically require the data analyst to at least define a frequency band of interest and time interval relative to the course of events in the experiment. These hyperparameters are exploited by the filtering method in order to extract informative oscillatory features. Their choice typically is domain-specific and may require adaptations to individuals. Post-hoc data analysis, however, should not be restricted to the initial hyperparameter ranges. Thus we present an approach, which allows to characterize a given oscillatory component with respect to the frequency bands and the temporal windows for which it contains task-relevant information. The approach allows to track task-informative persistence of components over multiple experimental sessions and may be helpful to monitor motor learning and rehabilitation over time.

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Paper Citation


in Harvard Style

Tangermann M. and Meinel A. (2017). Informative Oscillatory EEG Components and their Persistence in Time and Frequency.In NEUROTECHNIX 2017 - Extended Abstracts - Volume 1: CogNeuroEng, ISBN , pages 17-21


in Bibtex Style

@conference{cogneuroeng17,
author={Michael Tangermann and Andreas Meinel},
title={Informative Oscillatory EEG Components and their Persistence in Time and Frequency},
booktitle={NEUROTECHNIX 2017 - Extended Abstracts - Volume 1: CogNeuroEng,},
year={2017},
pages={17-21},
publisher={SciTePress},
organization={INSTICC},
doi={},
isbn={},
}


in EndNote Style

TY - CONF

JO - NEUROTECHNIX 2017 - Extended Abstracts - Volume 1: CogNeuroEng,
TI - Informative Oscillatory EEG Components and their Persistence in Time and Frequency
SN -
AU - Tangermann M.
AU - Meinel A.
PY - 2017
SP - 17
EP - 21
DO -