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
Claudia Cecchetto, Xiaying Wang, Mufti Mahmud, Michele Magno, Luca Benini, Lukas Cavigelli, et al. (2018). Embedded classification of local field potentials recorded from rat barrel cortex with implanted multi-electrode array [10.1109/BIOCAS.2018.8584830].
Embedded classification of local field potentials recorded from rat barrel cortex with implanted multi-electrode array
Michele Magno;Luca Benini;
2018
Abstract
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 2.6mWI documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
