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