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Title: A low-power, reconfigurable smart sensor system for EEG acquisition and classification
Authors: Sukumaran, Dinup
Enyi, Yao
Shuo, Sun
Basu, Arindam
Zhao, Dongning
Dauwels, Justin
Keywords: DRNTU::Engineering::Electrical and electronic engineering
Issue Date: 2012
Source: Sukumaran, D., Enyi, Y., Shuo, S., Basu, A., Zhao, D., & Dauwels, J. (2012). A low-power, reconfigurable smart sensor system for EEG acquisition and classification. 2012 IEEE Asia Pacific Conference on Circuits and Systems (APCCAS), pp.9-12.
Abstract: 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.
DOI: 10.1109/APCCAS.2012.6418958
Rights: © 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: [].
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Conference Papers

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