Embedded Classification of Local Field Potentials Recorded from Rat Barrel Cortex with Implanted Multi-Electrode Array
| Autor: | Xiaying Wang, Michele Magno, Lukas Cavigelli, Mufti Mahmud, Claudia Cecchetto, Stefano Vassanelli, Luca Benini |
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| Přispěvatelé: | Claudia Cecchetto, Xiaying Wang, Mufti Mahmud, Michele Magno, Luca Benini, Lukas Cavigelli, Stefano Vassanelli |
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
Neuroscience
Machine Learning Brain-chipInterface Image Processing Bio-sensors Implantable Sensors Brain-chip Interface Image Processing 02 engineering and technology implantable devices Implantable Sensors Neuroscience Machine learning brain-chip interface Image processing bio-sensors Machine Learning 03 medical and health sciences 0302 clinical medicine 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Bio-sensors 030217 neurology & neurosurgery |
| Zdroj: | 2018 IEEE Biomedical Circuits and Systems Conference (BioCAS) |
| Popis: | This paper focuses on ultra-low power embedded classification of neural activities. The machine learning (ML) algorithm has been trained using evoked local field potentials (LFPs) recorded with an implanted 16x16 multi-electrode array (MEA) from the rat barrel cortex while stimulating the whisker. Experimental results demonstrate that ML can be successfully applied to noisy single-trial LFPs. We achieved up to 95.8% test accuracy in predicting the whisker deflection. The trained ML model is successfully implemented on a low-power embedded system with an average consumption of 2.6mW. 2018 IEEE Biomedical Circuits and Systems Conference (BioCAS) ISBN:978-1-5386-3603-9 ISBN:978-1-5386-3604-6 |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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