cfs2mat

Convert CED Signal .cfs files to MATLAB .mat format with full metadata preservation.

CFS (CED Filing System) is the native binary format used by Cambridge Electronic Design's Signal software for sweep-based neurophysiology recordings (EMG, EEG, evoked potentials, etc.). This tool converts .cfs files into .mat files that can be loaded directly in MATLAB, preserving all channel metadata, sweep structure, events, and calibration info.

Prerequisites

cfs2mat uses biosig to read CFS files. The biosig Python package requires the libbiosig C library on your system:

# macOS
brew install biosig

# Linux (Debian/Ubuntu)
sudo apt install libbiosig-dev

# Linux (Fedora)
sudo dnf install biosig-devel

Installation

# With uv (recommended)
uv tool install cfs2mat

# With pip
pip install cfs2mat

CLI Usage

Convert files

# Single file
cfs2mat convert recording.cfs

# Multiple files / glob patterns
cfs2mat convert *.cfs
cfs2mat convert data/**/*.cfs

# Output to a specific directory
cfs2mat convert *.cfs -o converted/

# Store data flat (samples x channels) without sweep reshaping
cfs2mat convert recording.cfs --flat

# Quiet mode (no summary output)
cfs2mat convert recording.cfs -q

Inspect file metadata

# Human-readable summary
cfs2mat info recording.cfs

# Full header as JSON
cfs2mat info recording.cfs --json

Example info output:

File:       recording.cfs
Type:       CFS v6.06
Recorded:   2026-02-07 09:13:18.999991
Rate:       2000.00 Hz
Sweeps:     10
Samples:    8000 total (800/sweep)
Channels:   6
  [1] EMG1          unit=V     rate=2000.0 Hz  scale=0.000152588
  [2] EMG2          unit=V     rate=2000.0 Hz  scale=0.000152588
  [3] EMG3          unit=V     rate=2000.0 Hz  scale=0.000152588
  [4] EMG4          unit=V     rate=2000.0 Hz  scale=0.000152588
  [5] Marker        unit=s     rate=2000.0 Hz  scale=0.0005
  [6] Keyboard      unit=?     rate=2000.0 Hz  scale=1
Events:     9
  2026-02-07 09:14:02.592794      start of a new segment (after a break)
  ...

Python API

from cfs2mat import cfs_to_mat, read_cfs

# Convert to .mat (returns output path)
cfs_to_mat("recording.cfs")
cfs_to_mat("recording.cfs", mat_path="output.mat", flat=True)

# Read header + data without converting
header, data = read_cfs("recording.cfs")
# header: dict with all CFS metadata
# data: numpy array (samples x channels)

Output .mat Structure

The output .mat file can be loaded in MATLAB with load('recording.mat') and contains:

Data

Recording metadata

Channel metadata (arrays indexed by channel)

Events

Other

MATLAB Example

d = load('recording.mat');

% Plot first sweep, first channel
plot(squeeze(d.data(1, :, 1)));
title(d.channel_labels{1});
ylabel(d.channel_units{1});
xlabel(sprintf('Samples @ %.0f Hz', d.sampling_rate));

% Access all metadata
disp(d.start_of_recording);
disp(d.channel_labels);

License

MIT