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

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 outp...

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Bibliographic Details
Main Authors: Sukumaran, D., Enyi, Y., Sun Shuo, Basu, A., Dongning Zhao, Dauwels, J.
Format: Conference Proceeding
Language:English
Subjects:
Detectors
Electroencephalography
Feature extraction
Intelligent sensors
Machine learning
Neurons
Support vector machine classification
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Description
Summary: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