In this paper, three easily implemented hardware algorithms, including the adaptive prediction error filter based on the Gram-Schmidt algorithm (GS-APEF), the least mean square adaptive filter and the comb filter, are extensively investigated for artifact denoising on a constructed semi-simulated database with varied ten-fold frequency stimulation. By implementing the GS-APEF in the fieldprogrammable gate array (FPGA) and using the edge noise mitigating technique, a stimulation artifact denoising system is designed to realize real-time stimulation artifact removal under varied ten-fold frequency functional electrical stimulation. Good performance of the artifact denoising is demonstrated in proof-of-concept experiments on able-bodied subjects with a mean correlation coefficient between the root mean square profile of denoised surface electromyography and volitional force of 0.94, verifying the validity of the proposed prototype.INDEX TERMS Functional electrical stimulation (FES), stimulus artifact removal (SAR), surface electromyography (sEMG), adaptive filter, field-programmable gate array (FPGA)

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The low-frequency and low-amplitude characteristics of neural signals poses challenges to neural signals recording. A low noise amplifier (LNA) plays an important role in the recording front-end. A chopper-stabilized analog front-end amplifier (FEA) for neural signal acquisition is presented in this paper. It solves the noise and offset interference caused by the servo loop in the chopper amplifier structure. The proposed FEA employs a switched-capacitor (SC) integrator with offset and low-frequency noise compensation. Moreover, a dc-blocking impedance is placed for ripple-rejection (RR), and a positive feedback loop is employed to increase input impedance. The proposed circuit is design in a 0.18-µm 1.8-V CMOS process. It achieves a bandwidth of up to 9 kHz for local field potential and action potential signals acquisition. The referred-to-input (RTI) noise is 0.72 µVrms in the 1 Hz~200 Hz frequency band and 3.46 µVrms in the 200 Hz~5 kHz frequency band. The noise effect factor is 0.43 (1 Hz~200 Hz) and 2.08 (200 Hz~5 kHz). CMRR higher than 87 dB and PSRR higher than 85 dB are achieved in the entire pass-band. It consumes a power of 3.96 µW/channel and occupies an area of 0.244 mm2/channel.