Empatica Connect currently offers data visualization but the most powerful insights will come from objective quantification of Empatica data. If you're looking for some tools for signal processing and analysis we can help!
For systematic Skin Conductance analysis from our EDA files we recommend Ledalab (for MatLab). You may need to adjust skin conductance response (SCR) thresholds to account for lower magnitudes in typical wrist data. Instructions for importing Empatica E4 session data into Ledalab can be found in the appendix below.
For heart rate variability (HRV) analysis we recommend Kubios (also for MatLab). Kubios takes in instantaneous heart rate data and allows you to correct errant beats and conduct HRV analysis respectively. Kubios allows you to overlay our IBI files on the BVP data to assist in error correction.
If you prefer there are a number of signal processing toolkits available in the free software domain. Python's SciPy libraries have outstanding signal processing and data visualization capabilities.
Importing data into Ledalab:
Before you begin please make sure to have configured MatLab with Ledalab on your system and recoded and downloaded some sessions using the E4.
- Launch MatLab and point the command window at the Ledalab directory
- Launch Ledalab
- From the Ledalab "File" menu, select "Import Data" and then "Text Type 2"
- Under the "Chose a text2 data-file" wizard, select "All Files" under the Enable menu at the bottom of the window
- Confirm your sampling rate when prompted. Note the sampling rate can be found in the second row of the "eda.csv" file.
- On import, the first row in the file (the UNIX start date-time) is used to configure the Y-Axis scale (SC [µS]) so you will need to adjust the window to an appropriate level (on the order of 10-100 µS depending on the data). The Y-Axis range window should be in the upper right of the window as depicted in the image below.
- For Skin Conductance response detection you should adjust the Ledalab settings to a µS magnitude that is appropriate for your data. This should be somewhere between 0.05-0.5 µS.