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Augmenting real-time DSP in implantable high-density neuroprosthetic devices

Developments in microfabrication of high-density electronic interfaces to the central nervous system rapidly evolved in recent years. Nonetheless, there is a lack of higher digital signal processing capability to cope with the larger data throughput. Multiresolution analysis by means of the wavelet transform has shown to yield substantial processing and compression capabilities of high volumes of neural signals while preserving the necessary information needed to understand the neural coding mechanism. We describe a systems approach for reducing the complexity of on-chip discrete wavelet transform (DWT) computation for multiple data channels. The reduction is achieved by exploiting regularity in the filtering steps of the lifting-based DWT algorithm associated with negligible degradation in signal fidelity with integer fixed point arithmetic representation. The main advantages of the proposed architecture lie in the scalability to an arbitrary number of channels and decomposition levels. The results demonstrate that on-chip computation is feasible prior to data transmission, permitting large savings in bandwidth requirements and communication costs.