| Summary: | 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 field-programmable 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.
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