Session-independent EEG-based Workload Recognition

Felix Putze, Markus Mülller, Dominic Heger, Tanja Schultz


In this paper, we investigate the development of a session-independent EEG-based workload recognition system with minimal calibration time. On a corpus of ten sessions with the same subject, we investigate three different approaches: Accumulation of training data, an adaptive classifier (adaptive LDA) and feature selection algorithm (based on Mutual Information) to improve generalizability of the classifier. In a detailed evalution, we investigate how each approach performs under different conditions and show how we can use those methods to improve classification accuracy by more than 22% and make transfer of models between sessions more reliable.


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Paper Citation

in Harvard Style

Putze F., Mülller M., Heger D. and Schultz T. (2013). Session-independent EEG-based Workload Recognition . In Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2013) ISBN 978-989-8565-36-5, pages 360-363. DOI: 10.5220/0004250703600363

in Bibtex Style

author={Felix Putze and Markus Mülller and Dominic Heger and Tanja Schultz},
title={Session-independent EEG-based Workload Recognition},
booktitle={Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2013)},

in EndNote Style

JO - Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2013)
TI - Session-independent EEG-based Workload Recognition
SN - 978-989-8565-36-5
AU - Putze F.
AU - Mülller M.
AU - Heger D.
AU - Schultz T.
PY - 2013
SP - 360
EP - 363
DO - 10.5220/0004250703600363