Halovision – dream like a boss!

28 01 2016

Michael of Lucid Code has refined his own REM detection system project known as Halovision which is great news for us interested in sleep and dream research.

Halograph dream headband with camera

The device measures subtle electrooculography (EOG) muscle changes by means of a small 5 megapixel camera over the eye which records your eyelid movements digitally frame by frame via your associated computer device.


Eye movement detection recording using an IR cam


A Raspberry PI interface and NoIR camera make up the hardware which Michael used with his halo headband, plus his tried and tested Lucid Scribe software platform to good effect.

lucidcode camera set up

Early prototype using a red LED light source


Early versions tested produced too much noise either due to the lighting used, lens focusing issues, or the shadow produced by the camera headpiece mount.


For the majority of these experiments a *Raspberry PI board has been used as an interface for the NoIR camera which picks up infrared light.

These are the main pieces of hardware needed, apart from the standard network and USB cables, a headband, and the PC itself.

Optional extras are a 2 metre ribbon cable extension, a ScorPI mount and Camlot cover for the camera and a protective Raspberry PI box.


ScorPI camera mount

ScorPI mount


The main software needed to run the hardware and firmware includes Lucid Scribe, along with the latest Halovision plugin to enable you to detect REM eye pattern changes from the camera frame data via the algorithm, where an audio track of your choice is played.

The Halovision plugin will also work with traditional web cams via USB, or a built-in camera.

Both are also available via the Lucid Code website.


Michael has since used an external infrared source in the room with better, more stable results including trials with a fixed camera.


Recording of eye movement during sleep by using a fixed camera source


Michael has also released an Android version of Halovision which for the first time offers the home researcher a cheaper, but nevertheless a viable option using a smartphone with a built-in camera.

Michael does point out that the REM detection algorithm is experimental. The hardware will work either with a light source at night or during an afternoon nap where you have to be still with the camera facing you.


For anybody requiring more information on this project, or to participate further then Michael can be contacted on his website at Lucidcode.com


*(Models ‘1’ and above).

Photos courtesy of Lucidcode.com




One response

28 01 2016
Michael Paul Coder

Reblogged this on lucidcode.

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