Bens Spiker Algorithm

bens_spiker(signal: ndarray, window_length: int, threshold: float) → ndarray[source]

Perform spike detection using Bens Spiker Algorithm.

This function detects spikes in an input signal based on the comparison of cumulative errors calculated over a segment of the signal, which is filtered using a boxcar window. A spike is detected if the cumulative error between the filtered signal and the raw signal is below a certain threshold.

Refer to the Ben’s Spiker Encoding for a detailed explanation of the Ben’s Spiker algorithm.

Code Example:

import numpy as np
from spikify.encoding.temporal.deconvolution import bens_spiker
signal = np.array([0.1, 0.2, 0.3, 1.0, 0.5, 0.3, 0.1])
window_length = 3
threshold = 0.5
spikes = bens_spiker(signal, window_length, threshold)
Parameters:

  • signal (numpy.ndarray) – The input signal to be analyzed. This should be a numpy ndarray.

  • window_length (int) – The length of the boxcar filter window.

  • threshold (float) – Threshold value used to detect spikes.

Returns:

A 1D numpy array representing the detected spikes.

Return type:

numpy.ndarray

Raises:

  • ValueError – If the input signal is empty or if the window length is greater than the signal length.

  • TypeError – If the signal is not a numpy ndarray.