Importing data from EEG devices

MNE includes various functions and utilities for reading EEG data and electrode locations.

BrainVision (.vhdr, .vmrk, .eeg)

The BrainVision file format consists of three separate files:

1. A text header file (.vhdr) containing meta data

2. A text marker file (.vmrk) containing information about events in the data

3. A binary data file (.eeg) containing the voltage values of the EEG

Both text files are based on the Microsoft Windows INI format consisting of:

• sections marked as [square brackets]

• comments marked as ; comment

• key-value pairs marked as key=value

A documentation for core BrainVision file format is provided by Brain Products. You can view the specification hosted on the Brain Products website

BrainVision EEG files can be read in using mne.io.read_raw_brainvision() with the .vhdr header file as an input.

Warning

Renaming BrainVision files can be problematic due to their multifile structure. See this example for an instruction.

Note

For writing BrainVision files, you can use the Python package pybv.

European data format (.edf)

EDF and EDF+ files can be read using mne.io.read_raw_edf().

EDF (European Data Format) and EDF+ are 16-bit formats.

The EDF+ files may contain an annotation channel which can be used to store trigger information. These annotations are available in raw.annotations.

Saving EDF files is not supported natively yet. This gist can be used to save any mne.io.Raw into EDF/EDF+/BDF/BDF+.

BioSemi data format (.bdf)

The BDF format is a 24-bit variant of the EDF format used by EEG systems manufactured by BioSemi. It can be imported with mne.io.read_raw_bdf().

BioSemi amplifiers do not perform “common mode noise rejection” automatically. The signals in the EEG file are the voltages between each electrode and CMS active electrode, which still contain some CM noise (50 Hz, ADC reference noise, etc., see the BioSemi FAQ for further detail). Thus, it is advisable to choose a reference (e.g., a single channel like Cz, average of linked mastoids, average of all electrodes, etc.) on import of BioSemi data to avoid losing signal information. The data can be re-referenced later after cleaning if desired.

Warning

The data samples in a BDF file are represented in a 3-byte (24-bit) format. Since 3-byte raw data buffers are not presently supported in the fif format these data will be changed to 4-byte integers in the conversion.

General data format (.gdf)

GDF files can be read in using mne.io.read_raw_gdf().

GDF (General Data Format) is a flexible format for biomedical signals that overcomes some of the limitations of the EDF format. The original specification (GDF v1) includes a binary header and uses an event table. An updated specification (GDF v2) was released in 2011 and adds fields for additional subject-specific information (gender, age, etc.) and allows storing several physical units and other properties. Both specifications are supported in MNE.

Neuroscan CNT data format (.cnt)

CNT files can be read in using mne.io.read_raw_cnt(). The channel locations can be read from a montage or the file header. If read from the header, the data channels (channels that are not assigned to EOG, ECG, EMG or misc) are fit to a sphere and assigned a z-value accordingly. If a non-data channel does not fit to the sphere, it is assigned a z-value of 0.

Warning

Reading channel locations from the file header may be dangerous, as the x_coord and y_coord in ELECTLOC section of the header do not necessarily translate to absolute locations. Furthermore, EEG-electrode locations that do not fit to a sphere will distort the layout when computing the z-values. If you are not sure about the channel locations in the header, use of a montage is encouraged.

EGI simple binary (.egi)

EGI simple binary files can be read in using mne.io.read_raw_egi(). The EGI raw files are simple binary files with a header and can be exported from using the EGI Netstation acquisition software.

EGI MFF (.mff)

These files can also be read with mne.io.read_raw_egi().

EEGLAB set files (.set)

EEGLAB .set files can be read in using mne.io.read_raw_eeglab() and mne.read_epochs_eeglab().

Nicolet (.data)

These files can be read with mne.io.read_raw_nicolet().

eXimia EEG data (.nxe)

EEG data from the Nexstim eXimia system can be read in using the mne.io.read_raw_eximia() function.

Persyst EEG data (.lay, .dat)

EEG data from the Persyst system can be read in using the mne.io.read_raw_persyst() function.

Note that not all the subject metadata may be properly read in due to the fact that Persyst changes its specification sometimes from version to version. Please submit an issue, or pull request if you encounter a problem.

Nihon Kohden EEG data (.EEG, .21E, .PNT, .LOG)

EEG data from the Nihon Kohden (NK) system can be read using the mne.io.read_raw_nihon() function.

Files with the following extensions will be read:

• The .EEG file contains the actual raw EEG data.

• The .PNT file contains the metadata related to the recording, such as the measurement date.

• The .LOG file contains annotations for the recording.

• The .21E file contains the channel and electrode recording system information.

Reading .11D, .CMT, .CN2, and .EDF files is currently not supported.

Note that not all the subject metadata may be properly read in due to the fact that NK changes the specification sometimes from version to version. Please submit an issue, or pull request if you encounter a problem.

Setting EEG references

The preferred method for applying an EEG reference in MNE is mne.set_eeg_reference(), or equivalent instance methods like raw.set_eeg_reference(). By default, the data are assumed to already be properly referenced. See Setting the EEG reference for more information.

Reading electrode locations and head shapes for EEG recordings

Some EEG formats (EGI, EDF/EDF+, BDF) neither contain electrode location information nor head shape digitization information. Therefore, this information has to be provided separately. For that purpose all raw instances have a mne.io.Raw.set_montage() method to set electrode locations.

When using the locations of the fiducial points the digitization data are converted to the MEG head coordinate system employed in the MNE software, see MEG/EEG and MRI coordinate systems.

Estimated memory usage: 8 MB