Design of a closed-loop, bi-directional brain-machine-interface integrated on-chip spike sorting

This paper proposed a design of a closed-loop, bi-directional brain-machine-interface (BMI). The proposed chip consists of 16-channel neural acquisition, 8-channel neural stimulation, action potential detection, feature extraction and support vector machine (SVM) for spike sorting. A closed-loop control strategy is utilized to trigger different stimulation pattern according to different detected spike categories. The 16-channel neural signal acquisition is comprised of 16-channel low noise amplifier (LNA) which shares a single Programmable Gain Amplifier (PGA) draws a total current of 218uA under a power supply of 3.3V. Arbitrary combination is realized for the stimulator channel control. The frequency, the output current amplitude, the pulse width and the burst number are all tunable. The first and second derivative extrema method is applied to the detected action potential before spike sorting, realizing a great reduction on the data to be processed in the SVM. A single two-class sorting module is implemented as a results from the tradeoff between area and latency. More than two-class spike sorting is realized by a repeating of the two-class sorting procedure. The design has been fabricated in TSMC 180nm HV COMS process, occupying a silicon area of 5mm × 2mm.