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.
This paper presents the development and use of free libraries and software tools that can be employed to construct different basic electronics laboratory instruments, as an oscilloscope, wave generator, data recorder, custom instruments, etc. using the flexibility and power of an advance reconfigurable logic device as a field programmable gate array (FPGA). The libraries and tools have a modular design approach so that it is easily adaptable to different kinds and manufacturers of hardware and FPGA, the PC open source software and the hardware description language (HDL) used to code the FPGA are easily extensible to add functionalities or applications. The use of the SBA architecture allows shortening development time and as low resources usage.
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