| Paper: | MLSP-P6.5 | ||
| Session: | Biomedical and Other Applications | ||
| Time: | Friday, May 19, 16:30 - 18:30 | ||
| Presentation: | Poster | ||
| Topic: | Machine Learning for Signal Processing: Biomedical Applications and Neural Engineering | ||
| Title: | Constrained Non-negative Matrix Factorization Method for EEG Analysis in Early Detection of Alzheimer Disease | ||
| Authors: | Zhe Chen, Andrzej Cichocki, Tomasz Rutkowski, RIKEN Brain Science Institute, Japan | ||
| Abstract: | Approximate non-negative matrix factorization (NMF) is an emerging technique with a wide spectrum of potential applications in biomedical data analysis. In this paper, we proposed a new NMF algorithm with temporal smoothness constraint that aims to extract non-negative components that have meaningful physical or physiological interpretations. We propose two constraints and derive new multiplicative learning rules. Specifically, we apply the proposed algorithm, combined with advanced time-frequency analysis and machine learning techniques, to early detection of Alzheimer disease using the clinic EEG recordings. Empirical results show promising performance. | ||
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