Abstract
Next generation neural interfaces for upper-limb (and other) prostheses aim to develop implantable interfaces for one or more nerves, each interface having many neural signal channels that work reliably in the stump without harming the nerves. To achieve real-time multi-channel processing it is important to integrate spike sorting on-chip to overcome limitations in transmission bandwidth. This requires computationally efficient algorithms for feature extraction and clustering suitable for low-power hardware implementation. This paper describes a new feature extraction method for real-time spike sorting based on extrema analysis (namely positive peaks and negative peaks) of spike shapes and their discrete derivatives at different frequency bands. Employing simulation across different datasets, the accuracy and computational complexity of the proposed method are assessed and compared with other methods. The average classification accuracy of the proposed method in conjunction with online sorting (O-Sort) is 91.6%, outperforming all the other methods tested with the O-Sort clustering algorithm. The proposed method offers a better tradeoff between classification error and computational complexity, making it a particularly strong choice for on-chip spike sorting.
Highlights
A LTHOUGH mankind has provided artificial limbs for amputees for millennia, modern upper limb prostheses are far from ideal
With the aim of developing an energy-efficient neural recording and spike sorting chip for the targeted application, this paper reports a new feature extraction method based on extrema analysis of spike shapes and their discrete derivatives [6] with different sampling intervals
Principal component analysis (PCA) [7] has been the most commonly used algorithm for spike sorting because it yields an efficient coding of spikes
Summary
A LTHOUGH mankind has provided artificial limbs for amputees for millennia, modern upper limb prostheses are far from ideal. Fitzgerald et al [5] have shown that peripheral nerves grow into microchannels, giving large action potentials (spikes), characteristic of the active neurons, and are consistent for months Using such an implantable neural interface might offer the possibility of controlling upper-limb prostheses with many actuators, enabling more natural and wide ranging movements rather than just a few basic grasps. To achieve real-time online (on-chip) processing it is important to design an accurate feature extraction with low-power hardware resources, suitable for implantable devices (including, but not limited to, the microchannel nerve interface). With the aim of developing an energy-efficient neural recording and spike sorting chip for the targeted application, this paper reports a new feature extraction method based on extrema analysis (positive and negative peaks) of spike shapes and their discrete derivatives [6] with different sampling intervals.
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