Load Wearable Sensing Data#

Load EEG recordings from your Wearable Sensing DSI-24, DSI-VR300, or DSI-7 headset into MNE-Python for offline analysis.

Data Source

The example data were collected using a DSI-24 headset at 300 Hz sampling rate. There were two experimental conditions: eyes closed and eyes open, demonstrating alpha rhythm modulation.

Example Data#

To follow along with this tutorial, you can download a sample data file from this dropbox.

Import using MNE#

Load Wearable Sensing EDF file with MNE-Python#
import mne

# Define the file path of the EDF file on you computer
edf_file_path = 'Sample_DSI_24_Eyes_Closed.edf'

# Load data with mne
wearable_sensing_data = mne.io.read_raw_edf(edf_file_path, preload = True)

mne.io.read_raw_edf() is designed by the MNE-Python library to read data from EDF files. When this EDF file is read, we capture the data and create a raw object that holds not only the actual EEG data, but all the relevant metadata. This includes:

  • Channel Names

  • Channel Types

  • Sampling Frequency

  • Event Markers

  • And lots more!

Preload option

The preload=True option loads the data into memory, allowing for faster access and manipulation. By default, MNE does not preload the data, which means it will read the data from disk each time you access it.

Next Steps#

After loading your data:

  1. Configure channels and references - Set channel types and re-reference

  2. Filter your data - Remove noise and isolate frequency bands

  3. Remove artifacts - Clean data using ICA

  4. Create epochs - Extract event-related segments

Resources#