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An ultra-low power neural spike sorting technique for implantable, multi-channel neural implant is proposed. It involves spiking neural network (SNN) with binary weights as an energy and area efficient classifier, along with a suitable frontend for spike encoding of the recorded neuro-potential. The proposed scheme employs two step training to implement supervised learning for the classifier, in order...
Currently, state-of-the-art motor intention decoding algorithms in brain-machine interfaces are mostly implemented on a PC and consume significant amount of power. A machine learning coprocessor in 0.35-m CMOS for the motor intention decoding in the brain-machine interfaces is presented in this paper. Using Extreme Learning Machine algorithm and low-power analog processing, it achieves an energy...
Current brain-machine interfaces have two machine learners-one for spike sorting and the second for intention decoding that acts on the sorted spatio-temporal spike train. In this paper, we propose a pulse-based feature extractor that can enable these two machine learners to be combined into one. We show from simulations and measurements that the information about the spike shape is still retained...
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