Recovering Partially Sampled EEG Signals Using Learned Dictionaries

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 390)


Prior studies have proposedmethods to recover multi-channel EEG signal ensembles from their partially sampled entries. These techniques are only limited to multi-channel scenarios, especially when the number of channels is very large. Most biomedical signals (apart from EEG) are acquired from a single channel or from very few channels. Existing techniques cannot be employed to recover such signals from their partial samples. In this work, we propose a dictionary learning-based technique to overcome the problem. A sparsifying dictionary is learnt from the training examples; the trained dictionary is then used as a sparsifying transform in compressed sensing settings to recover the partially sampled test signal. Our proposed dictionary learning-based technique shows significant improvement in recovery over fixed sparsifying basis.


Dictionary learning Compressed sensing Wireless body area network 


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Copyright information

© Springer India 2016

Authors and Affiliations

  1. 1.Indraprastha Institute of Information Technology DelhiNew DelhiIndia

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