|Appears in Collections:||Computing Science and Mathematics Conference Papers and Proceedings|
|Peer Review Status:||Refereed|
|Title:||Memory Efficient On-Line Streaming for Multichannel Spike Train Analysis|
|Citation:||Yu B, Mak T, Smith L, Sun Y, Yakovlev A & Poon C (2011) Memory Efficient On-Line Streaming for Multichannel Spike Train Analysis In: Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC, 2011, Boston: Institute of Electrical and Electronics Engineers (IEEE). 33rd Annual International IEEE EMBS Conference, pp. 2315-2318.|
|Conference Name:||33rd Annual International IEEE EMBS Conference|
|Abstract:||Rapid advances in multichannel neural signal recording technologies in recent years have spawned broad applications in neuro-prostheses and neuro-rehabilitation. The dramatic increase in data bandwidth and volume associated with multichannel recording requires a significant computational effort which presents major design challenges for brain-machine interface (BMI) system in terms of power dissipation and hardware area. In this paper, we present a streaming method for implementing real-time memory efficient neural signal processing hardware. This method exploits the pseudo-stationary property of neural signals and, thus, eliminates the need of temporal storage in batch-based processing. The proposed technique can significantly reduce memory size and dynamic power while effectively maintaining the accuracy of algorithms. The streaming kernel is robust when compared to the batch processing over a range of BMI benchmark algorithms. The advantages of the streaming kernel when implemented on field-programmable gate array (FPGA) devices are also demonstrated.|
|Rights:||Published in Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC, 2011. © Copyright 2011 IEEE.; © 2011 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.|
Computing Science - CSM Dept
Massachusetts Institute of Technology
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