Abstract
A novel adaptive recovery method in the emerging compressed sensing theory is described and applied to extracellular neural recordings in order to reduce data rate in wireless neural recording systems. To strike a balance between high compression ratio and high spike reconstruction quality, a novel method that employs a group-sparsity recovery algorithm, prior information about the input neural signal, learning prior supports of spikes, and a matched wavelet technique is introduced. Our simulation results, using four different sets of real extracellular recordings from four distinct neural sources, show that our proposed method is effective, viable, and outperforms the state-of-the-art compressed sensing-based methods, in particular, when the number of the measurement is two times of the sparsity.
Highlights
To respect the several constraints imposed on the implant side of the wireless neural recording systems (WNRS), our proposed adaptive recovery method employs an emerging compressed sensing (CS) theory
Each trial was conducted by extracting from a synthetic neural signal, presented in Section IV, a frame x of length N=1024, performing spike detection (Abs technique), computing M linear random Bernoulli measurements, recovering the frame x, and recording the magnitude of the recovery error for different values of the ratio M/K
Results presented in this paper illustrate that our proposed method allows achievement of a compromise between high compression ratio and good recovery quality, while keeping CR at an acceptable level even when FR increases (Figure 10)
Summary
Polytechnique Montreal 2900, EdouardMontpetit Montreal (QC), Canada H3T1J4. Citation: Hesse S, Werner C. One of the challenges in the wireless neural recording systems (WNRS) is the correct and efficient transmission of these data out of the body from the implanted device The transmission of these large volumes of data requires a high data rate communication link. For a 100-microelectrode array, with the resolution of 10-bits and the sampling frequency of 20 kHz, data will be generated at an enormous rate of 20 Mb/s Given those limitations, several methods have been proposed to reduce data on the implant side prior transmitting them [1,2,3,4]. To respect the several constraints imposed on the implant side of the WNRS (e.g., small-size circuit and low-power consumption), our proposed adaptive recovery method employs an emerging compressed sensing (CS) theory.
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