Quantifying the Feasibility of Compressive Sensing in Portable Electroencephalography Systems
FAC '09 Proceedings of the 5th International Conference on Foundations of Augmented Cognition. Neuroergonomics and Operational Neuroscience: Held as Part of HCI International 2009
Compressive sampling of EEG signals with finite rate of innovation
EURASIP Journal on Advances in Signal Processing
A new non-uniform adaptive-sampling successive approximation ADC for biomedical sparse signals
Analog Integrated Circuits and Signal Processing
Enabling advanced inference on sensor nodes through direct use of compressively-sensed signals
DATE '12 Proceedings of the Conference on Design, Automation and Test in Europe
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Many applications in signal processing require the efficient representation and processing of data. The traditional approach to efficient signal representation is compression. In recent years, there has been a new approach to compression at the sensing level. Compressed sensing (CS) is an emerging field which is based on the revelation that a small collection of linear projections of a sparse signal contains enough information for reconstruction. In this paper, we propose an application of compressed sensing in the field of biomedical signal processing, particularly electroencophelogram (EEG) collection and storage. A compressed sensing framework is introduced for efficient representation of multichannel, multiple trial EEG data. The proposed framework is based on the revelation that EEG signals are sparse in a Gabor frame. The sparsity of EEG signals in a Gabor frame is utilized for compressed sensing of these signals. A simultaneous orthogonal matching pursuit algorithm is shown to be effective in the joint recovery of the original multiple trail EEG signals from a small number of projections.