TY - GEN
T1 - Quantifying the feasibility of compressive sensing in portable electroencephalography systems
AU - Abdulghani, Amir M.
AU - Casson, Alexander J.
AU - Rodriguez-Villegas, Esther
PY - 2009
Y1 - 2009
N2 - 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.
AB - 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.
KW - Compressive sensing
KW - Electroencephalogram
KW - Power efficient
KW - Wireless systems
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U2 - 10.1007/978-3-642-02812-0_38
DO - 10.1007/978-3-642-02812-0_38
M3 - Conference contribution
AN - SCOPUS:77952003811
SN - 364202811X
SN - 9783642028113
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 319
EP - 328
BT - Foundations of Augmented Cognition
T2 - 5th International Conference on Foundations of Augmented Cognition, FAC 2009, Held as Part of HCI International 2009
Y2 - 19 July 2009 through 24 July 2009
ER -