Multi-channel EEG compression based on matrix and tensor decompositions

• المؤلفون الرئيسيون: Srinivasan, K., Dauwels, Justin, Reddy, M. Ramasubba, Cichocki, Andrzej
• مؤلفون آخرون: School of Electrical and Electronic Engineering
• التنسيق: Conference or Workshop Item
• اللغة: English
• منشور في: 2013
• الموضوعات: DRNTU::Engineering::Electrical and electronic engineering::Electronic systems::Signal processing
• الوصول للمادة أونلاين: https://hdl.handle.net/10356/101285; http://hdl.handle.net/10220/18354
• المؤسسة: Nanyang Technological University

Compression schemes for EEG signals are developed based on matrix and tensor decomposition. Various ways to arrange EEG signals into matrices and tensors are explored, and several matrix and tensor decomposition schemes are applied, including SVD, CUR, PARAFAC, the Tucker decomposition, and recent random fiber selection approaches. Rate-distortion curves for the proposed matrix and tensor-based EEG compression schemes are computed. It shown that PARAFAC has the best compression performance in this context.