Abstract
Abstract Neural action potential recordings are applied widely in medical applications and research. The action potential detection approach is essential to evaluation the times at which spike activated. Recordings from the multi-electrode cuff typically have a minimum signal to noise ratio less than 10. In this paper, an LSM adaptive filter is designed and implemented employing Matlab and Xilinx Spartan 3E-100 (xc3s100e) software and hardware tool. The proposal filter shows a significant enhancement in the noise rejection and power-consuming and hardware size reduction. Furthermore, the outcomes illustrate that the LSM adaptive filter efficiently works in online neural recording with essential upgrading in the signal to noise ratio (SNR). Consequently, this improvement could lead to more spike detection accuracy. It can make the sensitivity of > 80% with a false positive rate of less than 6 Hz in recordings with SNR = 5, and it performs better than an optimal threshold detector using bandpass filter in recordings with SNR > 3. The present filter configuration offers a notable reduction of 10% in power consumption and 25% of the hardware required contrasting with the conventional approach of digital filter algorithm. Therefore, this study could suggest a crucial improvement to boost the neural recording to be applied as a control signal for the prosthetics devices.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call