Evidence Accumulation Approach applied to EEG Analysis

Helena Aidos, Carlos Carreiras, Hugo Silva, Ana Fred


Human-machine interaction is a rapidly expanding field which benefits from automatic emotion recognition. Therefore, methods that can automatically detect the emotional state of a person are important for this field, as well as for fields such as psychology and psychiatry. This paper proposes the use of clustering ensembles (CEs) to achieve such detection. We use CEs on a dataset containing EEG signals from subjects who performed a stress-inducing task. From the raw EEG data we apply filtering and processing techniques leading to three dataset types: simple EEG, EEG with eye-movement artifacts removed through Independent Component Analysis, and data-driven modes extracted using Empirical Mode Decomposition. Then, for each of these three data types, we compute band power features and phase-locking factors, yielding a total of six different feature spaces. These spaces are then analyzed using the CE framework which combines results of multiple clustering algorithms in a voting scheme. This procedure yields interesting clusters, in particular a natural tendency for finding low numbers of clusters per subject and finding clusters which are composed of consecutive test lines. These two facts combined may indicate that a change in the emotional state of the subject was detected by the proposed framework.


  1. Almeida, M., Schleimer, J.-H., Vigário, R., and BioucasDias, J. (2011). Source separation and clustering of phase-locked subspaces. IEEE Transactions on Neural Networks, 22:1419-1434.
  2. Ayad, H. G. and Kamel, M. S. (2005). Cluster-based cumulative ensembles. In Proc. Int. Workshop on Multiple Classifier Systems.
  3. Carreiras, C., de Almeida, L. B., and Sanches, J. M. (2012). Phase-locking factor in a motor imagery brain-computer interface. In Eng. in Medicine and Biology Society, 2012. 34th Annual Int. Conf. of the IEEE.
  4. Fred, A. (2001). Finding consistent clusters in data partitions. In Proc. Int. Workshop on Multiple Classifier Systems, pages 309-318.
  5. Fred, A. and Jain, A. (2005). Combining multiple clusterings using evidence accumulation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(6):835-850.
  6. Fred, A. and Jain, A.K. (2002). Evidence Accumulation Clustering based on the K-Means Algorithm. In Proc. Joint IAPR Int. Workshop on Structural, Syntactic and Statistical Pattern Recognition, pages 442-451.
  7. Fulton, J. (2000). The Mensa Book of Total Genius. Carlton Books.
  8. Gamboa, H., Silva, H., and Fred, A. (2007). HiMotion project. Technical report, Instituto Superior Técnico, Lisbon, Portugal.
  9. Huang, N., Shen, Z., Long, S., Wu, M., Shih, H., Zheng, Q., Yen, N., Tung, C., and Liu, H. (1998). The empirical mode decomposition and the hilbert spectrum for nonlinear and non-stationary time series analysis. Proc. Royal Society of London. Series A: Mathematical, Physical and Eng. Sciences, 454(1998):903-995.
  10. Hyvärinen, A., Karhunen, J., and Oja, E. (2001). Independent component analysis, volume 26. Wileyinterscience.
  11. Jain, A. K., Murty, M. N., and Flynn, P. J. (1999). Data clustering: a review. ACM Computing Surveys, 31(3):264-323.
  12. Jung, T., Makeig, S., Westerfield, M., Townsend, J., Courchesne, E., and Sejnowski, T. (2000). Removal of eye activity artifacts from visual event-related potentials in normal and clinical subjects. Clinical Neurophysiology, 111(10):1745-1758.
  13. Kuncheva, L. I. and Hadjitodorov, S. T. (2004). Using diversity in cluster ensembles. In Proc. Int. Conf. on Systems, Man and Cybernetics, pages 1214-1219.
  14. Pfurtscheller, G. and Lopes da Silva, F. H. (1999). Eventrelated EEG/MEG synchronization and desynchronization: basic principles. Clinical Neurophysiology, 110:1842 - 1857.
  15. Strehl, A. and Ghosh, J. (2002). Cluster ensembles - a knowledge reuse framework for combining multiple partitions. Journal of Machine Learning Research, 3:583-617.
  16. Theodoridis, S. and Koutroumbas, K. (2009). Pattern Recognition. Elsevier Academic Press, 4th edition.
  17. Vega-Pons, S. and Ruiz-Shulcloper, J. (2011). A survey of clustering ensemble algorithms. Int. Journal of Patt. Recog. and Artificial Intelligence, 25(3):337-372.

Paper Citation

in Harvard Style

Aidos H., Carreiras C., Silva H. and Fred A. (2013). Evidence Accumulation Approach applied to EEG Analysis . In Proceedings of the 2nd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-8565-41-9, pages 479-484. DOI: 10.5220/0004267804790484

in Bibtex Style

author={Helena Aidos and Carlos Carreiras and Hugo Silva and Ana Fred},
title={Evidence Accumulation Approach applied to EEG Analysis},
booktitle={Proceedings of the 2nd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},

in EndNote Style

JO - Proceedings of the 2nd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Evidence Accumulation Approach applied to EEG Analysis
SN - 978-989-8565-41-9
AU - Aidos H.
AU - Carreiras C.
AU - Silva H.
AU - Fred A.
PY - 2013
SP - 479
EP - 484
DO - 10.5220/0004267804790484