TY - GEN
T1 - Power analysis of a mobile EEG system with compressed sensing
AU - Senevirathna, Bathiya
AU - Abshire, Pamela
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/2
Y1 - 2017/7/2
N2 - We analyze the power tradeoffs for computation and transmission in a mobile electroencephalography (EEG) system. The EEG system comprises an analog front end, microcontroller, and wireless transceiver. We measured the power consumption of the system under a variety of conditions in order to estimate the power attributable to each component separately. We developed simple models for power consumption that incorporate transient power behavior of the devices and estimated parameters by fitting experimental data to the models. We found that the costs of transmission and computation were similar, with transmission power decreasing and computation power increasing with the compression ratio and all costs increasing with the number of channels. For the system configuration reported, the transmission costs dominated, leading to the conclusion that the system should be operated with: a) the lowest clock rate for the microcontroller; and b) the highest data compression consistent with system fidelity requirements. A discussion of tradeoffs in alternate system configurations is provided.
AB - We analyze the power tradeoffs for computation and transmission in a mobile electroencephalography (EEG) system. The EEG system comprises an analog front end, microcontroller, and wireless transceiver. We measured the power consumption of the system under a variety of conditions in order to estimate the power attributable to each component separately. We developed simple models for power consumption that incorporate transient power behavior of the devices and estimated parameters by fitting experimental data to the models. We found that the costs of transmission and computation were similar, with transmission power decreasing and computation power increasing with the compression ratio and all costs increasing with the number of channels. For the system configuration reported, the transmission costs dominated, leading to the conclusion that the system should be operated with: a) the lowest clock rate for the microcontroller; and b) the highest data compression consistent with system fidelity requirements. A discussion of tradeoffs in alternate system configurations is provided.
KW - compressed sensing
KW - EEG
KW - low cost
KW - power modeling
UR - https://www.scopus.com/pages/publications/85050008590
U2 - 10.1109/BIOCAS.2017.8325159
DO - 10.1109/BIOCAS.2017.8325159
M3 - Conference contribution
AN - SCOPUS:85050008590
T3 - 2017 IEEE Biomedical Circuits and Systems Conference, BioCAS 2017 - Proceedings
SP - 1
EP - 4
BT - 2017 IEEE Biomedical Circuits and Systems Conference, BioCAS 2017 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 IEEE Biomedical Circuits and Systems Conference, BioCAS 2017
Y2 - 19 October 2017 through 21 October 2017
ER -