A Computerized Bioinspired Methodology for Lightweight and Reliable Neural Telemetry

Personalized health monitoring of neural signals usually results in a very large dataset, the processing and transmission of which require considerable energy, storage, and processing time. We present bioinspired electroceptive compressive sensing (BeCoS) as an approach for minimizing these penaltie...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2020-11, Vol.20 (22), p.6461
Main Authors: Adeluyi, Olufemi, Risco-Castillo, Miguel A, Liz Crespo, María, Cicuttin, Andres, Lee, Jeong-A
Format: Article
Language:English
Subjects:
Algorithms
Bayes Theorem
compressed sensing (CS)
Compression ratio
Data Compression
Datasets
Dictionaries
Electric fields
electroencephalogram (EEG)
Electroencephalography
Energy storage
Lightweight
Optimization
personalized health monitoring
Signal monitoring
Signal processing
Signal Processing, Computer-Assisted
Software
Telemetry
virtual instrumentation
Wavelet transforms
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Description
Summary:Personalized health monitoring of neural signals usually results in a very large dataset, the processing and transmission of which require considerable energy, storage, and processing time. We present bioinspired electroceptive compressive sensing (BeCoS) as an approach for minimizing these penalties. It is a lightweight and reliable approach for the compression and transmission of neural signals inspired by active electroceptive sensing used by weakly electric fish. It uses a signature signal and a sensed pseudo-sparse differential signal to transmit and reconstruct the signals remotely. We have used EEG datasets to compare BeCoS with the block sparse Bayesian learning-bound optimization (BSBL-BO) technique-A popular compressive sensing technique used for low-energy wireless telemonitoring of EEG signals. We achieved average coherence, latency, compression ratio, and estimated per-epoch power values that were 35.38%, 62.85%, 53.26%, and 13 mW better than BSBL-BO, respectively, while structural similarity was only 6.295% worse. However, the original and reconstructed signals remain visually similar. BeCoS senses the signals as a derivative of a predefined signature signal resulting in a pseudo-sparse signal that significantly improves the efficiency of the monitoring process. The results show that BeCoS is a promising approach for the health monitoring of neural signals.
ISSN:1424-8220
1424-8220
DOI:10.3390/s20226461