Low-power machine learners for implantable decoding
Approximately 6 million people in the US and roughly 1 in 50 people worldwide suffer from paralysis. Intracortical brain machine interfaces (iBMIs) have shown promise in aiding movement, self-feeding and communication abilities of these severely motor-impaired patients. iBMIs essentially take neural...
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| Format: | Thesis-Doctor of Philosophy |
| Language: | English |
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Nanyang Technological University
2021
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| Online Access: | https://hdl.handle.net/10356/146463 |
| _version_ | 1811692819953221632 |
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| author | Shaikh, Shoeb Dawood |
| author2 | Arindam Basu |
| author_facet | Arindam Basu Shaikh, Shoeb Dawood |
| author_sort | Shaikh, Shoeb Dawood |
| collection | NTU |
| description | Approximately 6 million people in the US and roughly 1 in 50 people worldwide suffer from paralysis. Intracortical brain machine interfaces (iBMIs) have shown promise in aiding movement, self-feeding and communication abilities of these severely motor-impaired patients. iBMIs essentially take neural activity as an input, which is then subjected to signal processing and neural decoding, in order to drive prosthetics. However, the current systems are bulky, wired, immobile, conspicuous and require frequent calibration procedures often in the presence of a neural engineer. In this thesis, we have explored algorithmic and circuit and system level solutions to the aforementioned problems. Accordingly, we have presented circuit and system-level studies on offline and real-time non-human primate (NHP) data in order to aid development of scalable fully implantable wireless iBMIs. Furthermore, we have looked at novel algorithmic solutions to reduce calibration procedures. |
| first_indexed | 2024-10-01T06:41:51Z |
| format | Thesis-Doctor of Philosophy |
| id | ntu-10356/146463 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T06:41:51Z |
| publishDate | 2021 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1464632023-07-04T17:03:32Z Low-power machine learners for implantable decoding Shaikh, Shoeb Dawood Arindam Basu School of Electrical and Electronic Engineering arindam.basu@ntu.edu.sg Engineering::Electrical and electronic engineering Approximately 6 million people in the US and roughly 1 in 50 people worldwide suffer from paralysis. Intracortical brain machine interfaces (iBMIs) have shown promise in aiding movement, self-feeding and communication abilities of these severely motor-impaired patients. iBMIs essentially take neural activity as an input, which is then subjected to signal processing and neural decoding, in order to drive prosthetics. However, the current systems are bulky, wired, immobile, conspicuous and require frequent calibration procedures often in the presence of a neural engineer. In this thesis, we have explored algorithmic and circuit and system level solutions to the aforementioned problems. Accordingly, we have presented circuit and system-level studies on offline and real-time non-human primate (NHP) data in order to aid development of scalable fully implantable wireless iBMIs. Furthermore, we have looked at novel algorithmic solutions to reduce calibration procedures. Doctor of Philosophy 2021-02-18T02:09:19Z 2021-02-18T02:09:19Z 2021 Thesis-Doctor of Philosophy Shaikh, S. D. (2021). Low-power machine learners for implantable decoding. Doctoral thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/146463 10.32657/10356/146463 en This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0). application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Electrical and electronic engineering Shaikh, Shoeb Dawood Low-power machine learners for implantable decoding |
| title | Low-power machine learners for implantable decoding |
| title_full | Low-power machine learners for implantable decoding |
| title_fullStr | Low-power machine learners for implantable decoding |
| title_full_unstemmed | Low-power machine learners for implantable decoding |
| title_short | Low-power machine learners for implantable decoding |
| title_sort | low power machine learners for implantable decoding |
| topic | Engineering::Electrical and electronic engineering |
| url | https://hdl.handle.net/10356/146463 |
| work_keys_str_mv | AT shaikhshoebdawood lowpowermachinelearnersforimplantabledecoding |