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...
| Main Authors: | , , , , , |
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| Other Authors: | |
| Format: | Conference Paper |
| Language: | English |
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2013
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| Online Access: | https://hdl.handle.net/10356/101548 http://hdl.handle.net/10220/16338 |
| _version_ | 1811677151873728512 |
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| author | Sukumaran, Dinup Enyi, Yao Shuo, Sun Basu, Arindam Zhao, Dongning Dauwels, Justin |
| author2 | School of Electrical and Electronic Engineering |
| author_facet | School of Electrical and Electronic Engineering Sukumaran, Dinup Enyi, Yao Shuo, Sun Basu, Arindam Zhao, Dongning Dauwels, Justin |
| author_sort | Sukumaran, Dinup |
| collection | NTU |
| description | 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. |
| first_indexed | 2024-10-01T02:32:49Z |
| format | Conference Paper |
| id | ntu-10356/101548 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T02:32:49Z |
| publishDate | 2013 |
| record_format | dspace |
| spelling | ntu-10356/1015482020-03-07T13:24:50Z A low-power, reconfigurable smart sensor system for EEG acquisition and classification Sukumaran, Dinup Enyi, Yao Shuo, Sun Basu, Arindam Zhao, Dongning Dauwels, Justin School of Electrical and Electronic Engineering IEEE Asia Pacific Conference on Circuits and Systems (2012 : Kaohsiung, Taiwan) DRNTU::Engineering::Electrical and electronic engineering 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. Accepted version 2013-10-10T02:51:55Z 2019-12-06T20:40:24Z 2013-10-10T02:51:55Z 2019-12-06T20:40:24Z 2012 2012 Conference Paper 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. https://hdl.handle.net/10356/101548 http://hdl.handle.net/10220/16338 10.1109/APCCAS.2012.6418958 en © 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: [http://dx.doi.org/10.1109/APCCAS.2012.6418958]. application/pdf |
| spellingShingle | DRNTU::Engineering::Electrical and electronic engineering Sukumaran, Dinup Enyi, Yao Shuo, Sun Basu, Arindam Zhao, Dongning Dauwels, Justin A low-power, reconfigurable smart sensor system for EEG acquisition and classification |
| title | A low-power, reconfigurable smart sensor system for EEG acquisition and classification |
| title_full | A low-power, reconfigurable smart sensor system for EEG acquisition and classification |
| title_fullStr | A low-power, reconfigurable smart sensor system for EEG acquisition and classification |
| title_full_unstemmed | A low-power, reconfigurable smart sensor system for EEG acquisition and classification |
| title_short | A low-power, reconfigurable smart sensor system for EEG acquisition and classification |
| title_sort | low power reconfigurable smart sensor system for eeg acquisition and classification |
| topic | DRNTU::Engineering::Electrical and electronic engineering |
| url | https://hdl.handle.net/10356/101548 http://hdl.handle.net/10220/16338 |
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