Artificial Neural Network-assisted Amplitude Thresholding Improves Spike Detection.

As brain-related research presents increasing importance, the requirement for automatic spike detection algorithms also emerges. Traditional spike detection algorithms, including amplitude thresholding and wavelet transformation, show several shortcomings that impede the practical application. Here, we propose an artificial neural network-assisted amplitude thresholding algorithm and conduct experiments with raw signals collected from the primary somatosensory cortex and primary motor cortex of macaques. Using F1 score as an evaluation index, artificial neural networks, as well as its lightweight version, effectively help the amplitude thresholding to achieve better performance, showing enormous potential for real-time spike detection application.