Towards neuromorphic compression based neural sensing for next-generation wireless implantable brain machine interface
This work introduces a neuromorphic compression based neural sensing architecture with address-event representation inspired readout protocol for massively parallel, next-gen wireless implantable brain machine interface (iBMI). The architectural trade-offs and implications of the proposed method are...
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| Format: | Article |
| Language: | English |
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IOP Publishing
2025-01-01
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| Series: | Neuromorphic Computing and Engineering |
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| Online Access: | https://doi.org/10.1088/2634-4386/adad10 |
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| _version_ | 1832576065203077120 |
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| author | Vivek Mohan Wee Peng Tay Arindam Basu |
| author_facet | Vivek Mohan Wee Peng Tay Arindam Basu |
| author_sort | Vivek Mohan |
| collection | DOAJ |
| description | This work introduces a neuromorphic compression based neural sensing architecture with address-event representation inspired readout protocol for massively parallel, next-gen wireless implantable brain machine interface (iBMI). The architectural trade-offs and implications of the proposed method are quantitatively analyzed in terms of compression ratio (CR) and spike information preservation. For the latter, we used metrics such as root-mean-square error and correlation coefficient (CC) between the original and recovered signals to assess the effect of neuromorphic compression on the spike shape. Furthermore, we use accuracy, sensitivity, and false detection rate to understand the effect of compression on downstream iBMI tasks, specifically, spike detection. We demonstrate that a data CR of 15–265 per channel can be achieved by transmitting address-event pulses for two different biological datasets. The CR further increases to 200– $50\mathrm{K}$ per channel, 50 × more than in prior works, by the selective transmission of event pulses corresponding to neural spikes. A CC of ≈0.9 and spike detection accuracy of over 90% were obtained for the worst-case analysis involving $10\mathrm{K}$ -channel simulated recording and typical analysis using 100 or 384-channel real neural recordings. We also analyzed the collision handling capability for up to 10K channels and observed no significant error, indicating the scalability of the proposed pipeline. We also present initial results to show the ability of intention decoders to work directly on the events generated by the neuromorphic front-end. |
| format | Article |
| id | doaj-art-aafc83dfc13e4db4a1303271ebbaba82 |
| institution | Kabale University |
| issn | 2634-4386 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | Neuromorphic Computing and Engineering |
| spelling | doaj-art-aafc83dfc13e4db4a1303271ebbaba822025-01-31T12:07:36ZengIOP PublishingNeuromorphic Computing and Engineering2634-43862025-01-015101400410.1088/2634-4386/adad10Towards neuromorphic compression based neural sensing for next-generation wireless implantable brain machine interfaceVivek Mohan0https://orcid.org/0000-0002-0248-6417Wee Peng Tay1https://orcid.org/0000-0002-1543-195XArindam Basu2https://orcid.org/0000-0003-1035-8770School of Electrical and Electronic Engineering, Nanyang Technological University , Singapore, Singapore; Imperial Global Singapore, Imperial College London , Singapore, SingaporeSchool of Electrical and Electronic Engineering, Nanyang Technological University , Singapore, SingaporeDepartment of Electrical Engineering, City University of Hong Kong , Hong Kong SAR, People’s Republic of ChinaThis work introduces a neuromorphic compression based neural sensing architecture with address-event representation inspired readout protocol for massively parallel, next-gen wireless implantable brain machine interface (iBMI). The architectural trade-offs and implications of the proposed method are quantitatively analyzed in terms of compression ratio (CR) and spike information preservation. For the latter, we used metrics such as root-mean-square error and correlation coefficient (CC) between the original and recovered signals to assess the effect of neuromorphic compression on the spike shape. Furthermore, we use accuracy, sensitivity, and false detection rate to understand the effect of compression on downstream iBMI tasks, specifically, spike detection. We demonstrate that a data CR of 15–265 per channel can be achieved by transmitting address-event pulses for two different biological datasets. The CR further increases to 200– $50\mathrm{K}$ per channel, 50 × more than in prior works, by the selective transmission of event pulses corresponding to neural spikes. A CC of ≈0.9 and spike detection accuracy of over 90% were obtained for the worst-case analysis involving $10\mathrm{K}$ -channel simulated recording and typical analysis using 100 or 384-channel real neural recordings. We also analyzed the collision handling capability for up to 10K channels and observed no significant error, indicating the scalability of the proposed pipeline. We also present initial results to show the ability of intention decoders to work directly on the events generated by the neuromorphic front-end.https://doi.org/10.1088/2634-4386/adad10neural implantneuromorphic sensingneuromorphic compressionimplantable brain machine interfaceaddress event representation (AER) |
| spellingShingle | Vivek Mohan Wee Peng Tay Arindam Basu Towards neuromorphic compression based neural sensing for next-generation wireless implantable brain machine interface Neuromorphic Computing and Engineering neural implant neuromorphic sensing neuromorphic compression implantable brain machine interface address event representation (AER) |
| title | Towards neuromorphic compression based neural sensing for next-generation wireless implantable brain machine interface |
| title_full | Towards neuromorphic compression based neural sensing for next-generation wireless implantable brain machine interface |
| title_fullStr | Towards neuromorphic compression based neural sensing for next-generation wireless implantable brain machine interface |
| title_full_unstemmed | Towards neuromorphic compression based neural sensing for next-generation wireless implantable brain machine interface |
| title_short | Towards neuromorphic compression based neural sensing for next-generation wireless implantable brain machine interface |
| title_sort | towards neuromorphic compression based neural sensing for next generation wireless implantable brain machine interface |
| topic | neural implant neuromorphic sensing neuromorphic compression implantable brain machine interface address event representation (AER) |
| url | https://doi.org/10.1088/2634-4386/adad10 |
| work_keys_str_mv | AT vivekmohan towardsneuromorphiccompressionbasedneuralsensingfornextgenerationwirelessimplantablebrainmachineinterface AT weepengtay towardsneuromorphiccompressionbasedneuralsensingfornextgenerationwirelessimplantablebrainmachineinterface AT arindambasu towardsneuromorphiccompressionbasedneuralsensingfornextgenerationwirelessimplantablebrainmachineinterface |