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Depth Estimation

Creates a JSON file with information about the DC offset of each channel, as well as the channel closest to the brain surface. This information is needed to perform the median subtraction step.

SpikeInterface implementation

detect_bad_channels() can be used to detect which channels are outside the brain, as well as channels that have abnormally high levels of noise.

This function returns both the bad_channel_ids and channel_labels, which can be good, noise, dead, or out (outside of the brain). These can then be removed from the recording so they are ignored by the spike sorter:

from spikeinterface.preprocessing import detect_bad_channels

# detect noisy, dead, and out-of-brain channels
bad_channel_ids, channel_labels = detect_bad_channels(recording)
rec_clean = recording.remove_channels(remove_channel_ids=bad_channel_ids)

More information can be found in the documentation for the Preprocessing module.

Method

Depth estimation

This module uses the sharp increase in low-frequency LFP band power to estimate the brain surface location.

Running

python -m ecephys_spike_sorting.modules.depth_estimation --input_json <path to input json> --output_json <path to output json>

Two arguments must be included:

  1. The location of an existing file in JSON format containing a list of paths and parameters.
  2. The location to write a file in JSON format containing information generated by the module while it was run.

See the _schemas.py file for detailed information about the contents of the input JSON.

Input data

  • AP band and LFP band .dat or .bin files : int16 binary files written by Open Ephys, SpikeGLX, or the extract_from_npx module.

Output data

  • probe_info.json : contains information about each channel, as well as the surface channel for the probe
  • probe_depth.png : image showing the estimated surface channel location