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A Computerized Bioinspired Methodology for Lightweight and Reliable Neural Telemetry.
Olufemi Adeluyi1, Miguel A Risco-Castillo2, María Liz Crespo3
1Ministry of Communications and Digital Economy, Federal Secretariat, Abuja 900001, Nigeria.
Sensors (Basel, Switzerland)
|November 17, 2020
Summary
Bioinspired electroceptive compressive sensing (BeCoS) offers efficient neural signal monitoring. This method significantly reduces energy, storage, and processing time for personalized health applications.
Area of Science:
- Biomedical Engineering
- Signal Processing
- Neuroscience
Background:
- Personalized neural signal monitoring generates large datasets, demanding significant resources.
- Existing methods face challenges in energy, storage, and processing efficiency.
Purpose of the Study:
- To introduce bioinspired electroceptive compressive sensing (BeCoS) for efficient neural signal compression and transmission.
- To evaluate BeCoS's performance against established compressive sensing techniques.
Main Methods:
- BeCoS utilizes a signature signal and a pseudo-sparse differential signal for remote signal reconstruction.
- Comparison with block sparse Bayesian learning-bound optimization (BSBL-BO) using EEG datasets.
Main Results:
- BeCoS demonstrated superior average coherence, latency, compression ratio, and power efficiency compared to BSBL-BO.
- Structural similarity was slightly reduced but visual similarity was maintained.
- Achieved 35.38% better coherence, 62.85% lower latency, 53.26% higher compression ratio, and 13 mW lower power consumption.
Conclusions:
- BeCoS is a lightweight and reliable approach for minimizing penalties in neural signal monitoring.
- The pseudo-sparse nature of BeCoS signals enhances monitoring efficiency.
- BeCoS shows promise for advanced health monitoring of neural signals.
