Electroencephalogram Signal Clustering With Convex Cooperative Games
- 22 February 2021
- journal article
- research article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Knowledge and Data Engineering
- Vol. 34 (12), 5755-5769
- https://doi.org/10.1109/tkde.2021.3060742
Abstract
Currently, electroencephalogram (EEG) is mostly analyzed in a supervised way, which requires EEG labels (e.g., EEG classification). With the ever-increasing amount of unlabeled/mislabeled EEG in neuropsychiatric disorder diagnosis, BCI, and rehabilitation, manually labeling of EEG data is a labor intensive and time-consuming process, and few labs have developed algorithms to analyze EEG in an unsupervised manner (i.e., EEG clustering). In this paper, we propose a cooperative game inspired approach to cluster multi-trial EEG data. The idea is to map multi-trial EEG clustering to the coalition formation in a cooperative game, and then identify cluster center (the EEG trial with highest Shapley value) and assign EEG trials into proper clusters based on their cross correlation-transformed Shapley values. We demonstrate the mapped EEG cooperative game is convex, and it leads to an algorithm for multi-trial EEG clustering named CoGEEGc. The CoGEEGc yields high-quality multi-trial EEG clustering with respect to intra-cluster compactness and inter-cluster scatter. We show that CoGEEGc outperforms 15 state-of-the-art EEG or time series clustering approaches through detailed experimentation on real-world multi-trial EEG datasets. Comparison against 15 methods with four theoretical properties of clustering further illustrates the superiority of CoGEEGc, as it satisfies two properties while other approaches only satisfy one.Keywords
Funding Information
- Fundamental Research Funds for the Central Universities (NP2017208)
- National Natural Science Foundation of China (61702355, U1433116)
This publication has 42 references indexed in Scilit:
- A global averaging method for dynamic time warping, with applications to clusteringPattern Recognition, 2011
- An improvement of robot arm for EEG-based brain-computer interface systemNeuroscience Research, 2010
- Clustering of time series data—a surveyPattern Recognition, 2005
- Finding Clusters of Different Sizes, Shapes, and Densities in Noisy, High Dimensional DataPublished by Society for Industrial & Applied Mathematics (SIAM) ,2003
- Methods for robust clustering of epileptic EEG spikesIEEE Transactions on Biomedical Engineering, 2000
- OPTICSACM SIGMOD Record, 1999
- Comparing partitionsJournal of Classification, 1985
- Cores of convex gamesInternational Journal of Game Theory, 1971
- Measuring nominal scale agreement among many raters.Psychological Bulletin, 1971
- An algorithm for the machine calculation of complex Fourier seriesMathematics of Computation, 1965