Large Margin Multi-channel Analog-to-Digital Conversion with Applications to Neural Prosthesis

Part of Advances in Neural Information Processing Systems 19 (NIPS 2006)

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Amit Gore, Shantanu Chakrabartty


A key challenge in designing analog-to-digital converters for cortically implanted prosthesis is to sense and process high-dimensional neural signals recorded by the micro-electrode arrays. In this paper, we describe a novel architecture for analog-to-digital (A/D) conversion that combines conversion with spatial de-correlation within a single module. The architecture called multiple-input multiple-output (MIMO) is based on a min-max gradient descent optimization of a regularized linear cost function that naturally lends to an A/D formulation. Using an online formulation, the architecture can adapt to slow variations in cross-channel correlations, observed due to relative motion of the microelectrodes with respect to the signal sources. Experimental results with real recorded multi-channel neural data demonstrate the effectiveness of the proposed algorithm in alleviating cross-channel redundancy across electrodes and performing data-compression directly at the A/D converter.