A collection of tools for capturing and visualizing non-ordinary states of consciousness and their precipitating contexts. Developed for the Arthur C. Clarke Center for Human Imagination and the Center for Human Transformation.
Currently this project interfaces with the following sensors:
- motion capture - Motion Studio Shadow suits
- EEG - muse
- HR/HVR - Polar H7 or H10
- webcam (reference video)
- breath (not yet implemented)
Data is captured using LabRecorder from the Lab Streaming Layer project.
Data are visualized in Unity.
Assumes you have done all software setup below.
We are currently using LabRecorder from the LSL project to record data. You can get the LabRecorder app here: LabRecorder-1.13.zip ftp://sccn.ucsd.edu/pub/software/LSL/Apps/
Start LabRecorder.exe.
As you start each of the individual data streams below, hit "update" in the LabRecorder "Record from Streams" window to refresh to list of available data streams.
Pair the Muse with your computer, wear the device, and start the outstream software.
In a command prompt, run: muse/StreamMuse.bat
In Windows Explorer, navigate to lsl/flow-lsl-interface and run lsl/outstream_muse.py.
You should now see the Muse as a data source in LabRecorder.
Put on the Polar H10 monitor and start the data outstream software.
In Windows Explorer, navigate to ble/BLEPolarDirect/bin/Debug/ and run ble/BLEPolarDirect/bin/Debug/BLEPolarDirect.exe
You should now see the Polar as a data source in LabRecorder.
Start the Shadow desktop app. (Called "Shadow")
Put on the Shadow Suit and power it on. (See the video tutorial here)
Once the indicator light is pulsing a slow blue, connect to the available Shadow1 WiFi network from your laptop. (SSID: "Shadow1", pwd: 2062012708). In the Shadow app you should see the live skeleton.
In Windows Explorer, navigate to lsl/flow-lsl-interface and run lsl/outstream_shadow.py.
You should now see the shadowsuit as a data source in LabRecorder.
Plug in the webcam to your USB port.
In Windows Explorer, navigate to lsl/flow-lsl-interface and run lsl/outstream_webcam.py.
You should now see the Webcam as a data source in LabRecorder.
If you now update the lis of available streams in labrecorder, you should see the Polar, Muse, Webcam, and Mocap/Shadow Suit. Click the checkbox next to each of these you wish to record to.
Set a storage location for your data using the "Browse" button. I suggest using the data/ directory. Give your file/take a meaningful name.
Use the "Start" and "Stop" buttons under Recording Control to make your recordings.
Our main software for previewing the recorded data is gui.py found in lsl
Run the program from git bash. From the lsl directory, run:
python gui.py your_data_file.xdf
Replace your_data_file.xdf with the name of the file you wish to preview.
More coming soon. For now we have very preliminary visualization in Unity.
Start the Shadow desktop app. Wear the shadow suit. Connect your laptop to the shadow WiFi network. You should see the shadow suit in the shadow software.
With Unity, open the unity/shadow-unity-test project.
Play SampleScene.
You should see the rigged skeleton follow the motions of your shadow suit.
Wear the Polar sensor and start the BLEPolarDirect program.
With Unity, open the unity/unity%20LSL%20test project.
Play the lsl to unity test scene.
The floating text panel in the scene should show the current polar readings.
Coming soon.
Download the Muse SDK:
http://storage.googleapis.com/ix_downloads/musesdk-3.4.1/musesdk-3.4.1-windows-installer.exe
or on Khan:
Khan\Assembly\Dependencies\musesdk-3.4.1-windows-installer.exe
Pair the Muse headset:
- Hold Muse On button for 6 seconds.
- Open Control Panel --> Add A device
- Choose yes that you see the pairing code.
Download and install the Shadow desktop app.
https://www.motionshadow.com/software/5753105639538688
For help on how to put on the shadow suit, see the video tutorial here).
Download LabRecorder-1.13.zip from here:
ftp://sccn.ucsd.edu/pub/software/LSL/Apps/
We are currently testing with 2018.2.15f1
https://unity3d.com/get-unity/download
Do the necessary python installation for the python lsl software. See lsl/readme.md.
Download github desktop for windows and install: https://desktop.github.com/
- textually annotate data
- unity playback of LSL data
- exploratory data analysis in python with scikitlearn, matplotlib, other tools
- use the hrv analysis package: https://github.com/rhenanbartels/hrv
- manual, then automatic classification of peak moments/NOSC