Signal-to-noise ratio (`snr`)¶

Calculation¶

\(A_{\mu s}\) : maximum amplitude of the mean spike waverform (on the best channel).
\(\sigma_b\) : standard deviation of the background noise on the same channel (usually computed via the median absolute deviation).

\[\textrm{SNR} = \frac{A_{\mu s}}{\sigma_b}\]

Expectation and use¶

A high SNR unit has a signal which is greater in amplitude than the background noise and is likely to correspond to a neuron [Jackson], [Lemon]. A low SNR value (close to 0) suggests that the unit is highly contaminated by noise (type I error).

Example code¶

Without SpikeInterface:

import numpy as np
import scipy.stats

data        # The data from your recording in shape (channel, time)
mean_wvf    # The mean waveform of your unit in shape (channel, time)
# If your data is filtered, then both data and mean_wvf need to be filtered the same.

best_channel = np.argmax(np.max(np.abs(mean_wvf), axis=1))
noise_level = scipy.stats.median_abs_deviation(data[best_channel], scale="normal")
amplitude = np.max(np.abs(mean_wvf))

SNR = amplitude / noise_level

With SpikeInterface:

import spikeinterface.qualitymetrics as sqm

# Combining sorting and recording into a sorting_analzyer
SNRs = sqm.compute_snrs(sorting_analzyer=sorting_analzyer)
# SNRs is a dict containing the unit IDs as keys and their SNRs as values.

Links to original implementations¶

From the AllenSDK

References¶

spikeinterface.qualitymetrics.misc_metrics.compute_snrs(sorting_analyzer, peak_sign: str = 'neg', peak_mode: str = 'extremum', unit_ids=None)¶

Compute signal to noise ratio.

Parameters:

sorting_analyzerSortingAnalyzer: A SortingAnalyzer object.
peak_sign“neg” | “pos” | “both”, default: “neg”: The sign of the template to compute best channels.
peak_mode“extremum” | “at_index” | “peak_to_peak”, default: “extremum”: How to compute the amplitude. Extremum takes the maxima/minima At_index takes the value at t=sorting_analyzer.nbefore.
unit_idslist or None: The list of unit ids to compute the SNR. If None, all units are used.

Returns:

snrsdict: Computed signal to noise ratio for each unit.

Literature¶

Presented by [Lemon] and useful initial discussion by [Jackson].

Signal-to-noise ratio (snr)¶