Heuristic adaptive threshold detection method for neuronal spikes

In recent years, the development of microelectrode arrays and multichannel recordings has provided opportunities for high-precision detection in signal processing. The study of neuronal frontal potentials has been rapidly emerging as an important component in brain-computer interface and neuroscience research. Neuronal spike detection provides a basis for neuronal discharge analysis and nucleus cluster identification; its accuracy depends on feature extraction and classification, which affect neuronal decoding analysis. However, improving the detection accuracy of spike potentials in highly noisy signals remains a problem. IThe authors propose a heuristic adaptive threshold spike-detection algorithm that removes noise and reduces the phase shift using a zero-phase Butterworth infinite impulse response filter. Next, heuristic thresholding is applied to obtain spike points, remove repetitions, and achieve robust spike detection. The proposed algorithm achieved an average accuracy of 95.40% using extracellular spiked datasets and effectively detected spikes.