A 41 μW real-time adaptive neural spike classifier
A. Zjajo (TU Delft - Signal Processing Systems)
R. van Leuken (TU Delft - Signal Processing Systems)
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Abstract
Robust, power- and area-efficient spike classifier, capable of accurate identification of the neural spikes even for low SNR, is a prerequisite for the real-time, implantable, closed-loop brain-machine interface. In this paper, we propose an easily-scalable, 128-channel, programmable, neural spike classifier based on nonlinear energy operator spike detection, and a boosted cascade, multiclass kernel support vector machine classification. The power-efficient classification is obtained with a combination of the algorithm and circuit techniques. The classifier implemented in a 65 nm CMOS technology consumes less than 41 μW of power, and occupy an area of 2.64 mm2.