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A low-power, reconfigurable smart sensor system for EEG acquisition and classification

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6 Author(s)
Dinup Sukumaran ; School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798 ; Yao Enyi ; Sun Shuo ; Arindam Basu
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We describe a smart sensor for EEG acquisition comprising a programmable gain low-noise amplifier followed by an integrated feature extraction and classification circuits. The feature extraction block comprises a bank of four band-pass filters followed by a wide dynamic range peak detector. The output of the peak detector is fed into a spiking neural network implementing the extreme learning machine (ELM) for classification. The advantage of ELM is that it has been shown to attain comparable performance to support vector machine (SVM) but with fewer computational nodes. We describe simulation results of each block designed in 0.35 um CMOS and demonstrate system level performance by using this to detect seizure onset in epileptic patients. The system can be reconfigured for other applications like speech classification.

Published in:

Circuits and Systems (APCCAS), 2012 IEEE Asia Pacific Conference on

Date of Conference:

2-5 Dec. 2012