Real-time FPGA-based multichannel spike sorting using Hebbian eigenfilters
Real-time multichannel neuronal signal recording has spawned broad applications in neuro-prostheses and neuro-rehabilitation. Detecting and discriminating neuronal spikes from multiple spike trains in real-time require significant computational efforts and present major challenges for hardware desig...
| Main Authors: | , , , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
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Institute of Electrical and Electronics Engineers (IEEE)
2012
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| Online Access: | http://hdl.handle.net/1721.1/69129 |
| _version_ | 1826192843763351552 |
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| author | Yu, Bo Mak, Terrence Li, Xiangyu Xia, Fei Yakovlev, Alex Sun, Yihe Poon, Chi-Sang |
| author2 | Harvard University--MIT Division of Health Sciences and Technology |
| author_facet | Harvard University--MIT Division of Health Sciences and Technology Yu, Bo Mak, Terrence Li, Xiangyu Xia, Fei Yakovlev, Alex Sun, Yihe Poon, Chi-Sang |
| author_sort | Yu, Bo |
| collection | MIT |
| description | Real-time multichannel neuronal signal recording has spawned broad applications in neuro-prostheses and neuro-rehabilitation. Detecting and discriminating neuronal spikes from multiple spike trains in real-time require significant computational efforts and present major challenges for hardware design in terms of hardware area and power consumption. This paper presents a Hebbian eigenfilter spike sorting algorithm, in which principal components analysis (PCA) is conducted through Hebbian learning. The eigenfilter eliminates the need of computationally expensive covariance analysis and eigenvalue decomposition in traditional PCA algorithms and, most importantly, is amenable to low cost hardware implementation. Scalable and efficient hardware architectures for real-time multichannel spike sorting are also presented. In addition, folding techniques for hardware sharing are proposed for better utilization of computing resources among multiple channels. The throughput, accuracy and power consumption of our Hebbian eigenfilter are thoroughly evaluated through synthetic and real spike trains. The proposed Hebbian eigenfilter technique enables real-time multichannel spike sorting, and leads the way towards the next generation of motor and cognitive neuro-prosthetic devices. |
| first_indexed | 2024-09-23T09:30:03Z |
| format | Article |
| id | mit-1721.1/69129 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T09:30:03Z |
| publishDate | 2012 |
| publisher | Institute of Electrical and Electronics Engineers (IEEE) |
| record_format | dspace |
| spelling | mit-1721.1/691292022-09-30T14:49:06Z Real-time FPGA-based multichannel spike sorting using Hebbian eigenfilters Yu, Bo Mak, Terrence Li, Xiangyu Xia, Fei Yakovlev, Alex Sun, Yihe Poon, Chi-Sang Harvard University--MIT Division of Health Sciences and Technology Poon, Chi-Sang Poon, Chi-Sang Real-time multichannel neuronal signal recording has spawned broad applications in neuro-prostheses and neuro-rehabilitation. Detecting and discriminating neuronal spikes from multiple spike trains in real-time require significant computational efforts and present major challenges for hardware design in terms of hardware area and power consumption. This paper presents a Hebbian eigenfilter spike sorting algorithm, in which principal components analysis (PCA) is conducted through Hebbian learning. The eigenfilter eliminates the need of computationally expensive covariance analysis and eigenvalue decomposition in traditional PCA algorithms and, most importantly, is amenable to low cost hardware implementation. Scalable and efficient hardware architectures for real-time multichannel spike sorting are also presented. In addition, folding techniques for hardware sharing are proposed for better utilization of computing resources among multiple channels. The throughput, accuracy and power consumption of our Hebbian eigenfilter are thoroughly evaluated through synthetic and real spike trains. The proposed Hebbian eigenfilter technique enables real-time multichannel spike sorting, and leads the way towards the next generation of motor and cognitive neuro-prosthetic devices. 2012-02-16T18:23:20Z 2012-02-16T18:23:20Z 2011-12 Article http://purl.org/eprint/type/JournalArticle 2156-3357 2156-3365 http://hdl.handle.net/1721.1/69129 Yu, Bo et al. “Real-Time FPGA-Based Multichannel Spike Sorting Using Hebbian Eigenfilters.” IEEE Journal on Emerging and Selected Topics in Circuits and Systems 1.4 (2011): 502-515. Web. 16 Feb. 2012. © 2012 Institute of Electrical and Electronics Engineers en_US http://dx.doi.org/10.1109/JETCAS.2012.2183430 IEEE Journal on Emerging and Selected Topics in Circuits and Systems Creative Commons Attribution-Noncommercial-Share Alike 3.0 http://creativecommons.org/licenses/by-nc-sa/3.0/ application/pdf Institute of Electrical and Electronics Engineers (IEEE) Chi-Sang Poon |
| spellingShingle | Yu, Bo Mak, Terrence Li, Xiangyu Xia, Fei Yakovlev, Alex Sun, Yihe Poon, Chi-Sang Real-time FPGA-based multichannel spike sorting using Hebbian eigenfilters |
| title | Real-time FPGA-based multichannel spike sorting using Hebbian eigenfilters |
| title_full | Real-time FPGA-based multichannel spike sorting using Hebbian eigenfilters |
| title_fullStr | Real-time FPGA-based multichannel spike sorting using Hebbian eigenfilters |
| title_full_unstemmed | Real-time FPGA-based multichannel spike sorting using Hebbian eigenfilters |
| title_short | Real-time FPGA-based multichannel spike sorting using Hebbian eigenfilters |
| title_sort | real time fpga based multichannel spike sorting using hebbian eigenfilters |
| url | http://hdl.handle.net/1721.1/69129 |
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