Using fast matrix multiplication to find basic solutions
Theoretical Computer Science
Compressed Sensing Framework for EEG Compression
SSP '07 Proceedings of the 2007 IEEE/SP 14th Workshop on Statistical Signal Processing
Event-related brain dynamics in continuous sustained-attention tasks
FAC'07 Proceedings of the 3rd international conference on Foundations of augmented cognition
Low power technology for wearable cognition systems
FAC'07 Proceedings of the 3rd international conference on Foundations of augmented cognition
Augmenting task-centered design with operator state assessment technologies
FAC'07 Proceedings of the 3rd international conference on Foundations of augmented cognition
The effect of fatigue on cognitive and psychomotor skills of surgical residents
FAC'07 Proceedings of the 3rd international conference on Foundations of augmented cognition
Exploring neural trajectories of scientific problem solving skill acquisition
FAC'07 Proceedings of the 3rd international conference on Foundations of augmented cognition
IEEE Transactions on Information Theory
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The EEG for use in augmented cognition produces large amounts of compressible data from multiple electrodes mounted on the scalp. This huge amount of data needs to be processed, stored and transmitted and consumes large amounts of power. In turn this leads to physically large EEG units with limited lifetimes which limit the ease of use, and robustness and reliability of the recording. This work investigates the suitability of compressive sensing, a recent development in compression theory, for providing online data reduction to decrease the amount of system power required. System modeling which incorporates a review of state-of-the-art EEG suitable integrated circuits shows that compressive sensing offers no benefits when using an EEG system with only a few channels. It can, however, lead to significant power savings in situations where more than approximately 20 channels are required. This result shows that the further investigation and optimization of compressive sensing algorithms for EEG data is justified.