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Authors: Simon Bergstrand ; Malin Åberg ; Timo Niiniskorpi and Johan Wessberg

Affiliation: University of Gothenburg, Sweden

ISBN: 978-989-8111-65-4

Keyword(s): fMRI, EEG, Pattern recognition, Support vector machines, Artificial neural networks.

Related Ontology Subjects/Areas/Topics: Applications and Services ; Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Computer Vision, Visualization and Computer Graphics ; Data Manipulation ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Medical Image Detection, Acquisition, Analysis and Processing ; Methodologies and Methods ; Neural Networks ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Signal Processing ; Soft Computing ; Theory and Methods ; Wavelet Transform

Abstract: Pattern recognition methods, which recently have shown promising potential in the analysis of neurophysiological data, are typically model-free and can thus be applied in the analysis of any type of signal. This study demonstrates the feasibility of, after suitable pre-processing steps, applying identical state-of-the-art pattern recognition method to single-trial classification of brain state data acquired with the fundamentally different techniques EEG and fMRI.We investigated linear and non-linear support vector machines (SVM) and artificial neural networks (ANNs), and it was found that the SVM is highly suitable for the classification of both fMRI and EEG single patterns. However, the non-linear classifiers performed better than the linear ones on the EEG data (linear ANN: 66.2%, SVM: 78.9% vs. non-linear ANN: 71.8%, SVM: 83.2%), whereas the opposite was true for the fMRI dataset (linear ANN: 74.4%, SVM: 77.2% vs. non-linear ANN: 70.5%, SVM: 74.2%). The exciting possibility of con current EEG and fMRI registration warrants a need for a unified analysis method for both modalities, and we propose pattern recognition for this purpose. The ability to identify cortical patterns on a single-trial basis allows for brain computer interfaces, lie detection, bio-feedback, the tracking of mental states over time, and in the design of interactive, dynamic fMRI and EEG studies. (More)

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Paper citation in several formats:
Bergstrand S.; Åberg M.; Niiniskorpi T.; Wessberg J. and (2009). TOWARDS UNIFIED ANALYSIS OF EEG AND FMRI - A Comparison of Classifiers for Single-trial Pattern Recognition.In Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2009) ISBN 978-989-8111-65-4, pages 273-278. DOI: 10.5220/0001535002730278

@conference{biosignals09,
author={Simon Bergstrand and Malin Åberg and Timo Niiniskorpi and Johan Wessberg},
title={TOWARDS UNIFIED ANALYSIS OF EEG AND FMRI - A Comparison of Classifiers for Single-trial Pattern Recognition},
booktitle={Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2009)},
year={2009},
pages={273-278},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001535002730278},
isbn={978-989-8111-65-4},
}

TY - CONF

JO - Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2009)
TI - TOWARDS UNIFIED ANALYSIS OF EEG AND FMRI - A Comparison of Classifiers for Single-trial Pattern Recognition
SN - 978-989-8111-65-4
AU - Bergstrand, S.
AU - Åberg, M.
AU - Niiniskorpi, T.
AU - Wessberg, J.
PY - 2009
SP - 273
EP - 278
DO - 10.5220/0001535002730278

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