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We've come a long way from an empty room with no internet! Hello_World? is live.
Feedback from the Exhibition
During the 2 days of exhibition Mastermind was used by about 30-40 people. most people gave good feedback on the the whole idea of controlling the computer with just their mind. Although the room was noisy most people were able to achieve a level of success that made them feel like they were really in control of the program.Â
the main area of criticism was that the the feedback to tell them that they were being successful was not clear (i.e. that simply jumping from a mixed video to a clear video was not sufficient to show them they were having an effect). As a result I incorporated a couple of graphical queues that made it clearer when they were changing the display with “concentration”. This involved including a text message to “CONCENTRATE _ NEED MORE BRAIN POWER” when not concentrating and “CLEAR SIGNAL” when the concentration threshold was exceeded.
Further feedback suggested that it was not clear what to focus on. as a result I am developing a circle that changes with concentration level.
The noisy video (which users have no control of) at the start of the active app, also proved too long, so this was reduced to 8s from 16s, to allow users to get straight to the action.
othersÂ
Project muse begins to take shape!
Jumpy Video
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Back to basics
After a number of days trying to get the program working with the returnJSON() method with little success I have made the decision to return to the long-hand version where the programming is written out within the draw() method. I will then launch the timing using the millis() function.
the rough outline should be like this
draw(){
connect to headset, filter signal and return JSON object containing EEG power data
if time < 30s {play the video}
if time >30s and < 45s {play “RELAX” message and record baseline EEG }
if time > 45s { launch the application
       if attentionActive = false {play the mixed videos}
       if attentionActive = true{play the clear video
} }
}
Storyboarding
following on from our very productive meeting with Dr Marguritte Barry last week, I am developing a storyboard of what a user will actually experience when the enter the exhibit. this will allow us to focus on the final product that the user experiences and allow us to finally script the experience.Â
it is important that we provide a rewarding experience for the user. In terms of Mastermind, one of the key elements to consider is the ability of the user to successfully filter the signal using the EEG input. Testing so far has shown that the the current threshold results in the video jumping between clear and noisy states. to enhance the UX it may be useful to decrease the threshold to the “clear” state as time progresses, to prevent users becoming frustrated and not getting the optimum experience from the exhibit. More updates to follow as it develops.
Full Screen UX
in order the keep this app consistent with the other exhibits, and so that it gives the user the impression that they are interacting directly with a “mind reading machine” as opposed to simply a programme running on a computer screen, I have investigated the options of placing the sketch into a browser window. Current attempts focus on getting the sketch to display in a canvas element within a html page.Â
Alternatively, it may be possible to simply display the sketch on the full screen. Superduper.og appears to provide an API which will allow the sketch to be displayed full screen. http://www.superduper.org/processing/fullscreen_api/
over the next few days I will investigate both these options to see which can provide the best results.
Testing Begins...
So I’ve finally got the app to a point where were can begin to test what actually constitutes “concentration”. As mentioned before there appears to be a correlation with increased concentration and increased power in the high frequency EEG bands.
To test this theory with the current EEG setup I created an experimental set-up with 8 participants.Â
Step 1 involved getting the participant to relax while wearing the headset. Baseline EEG was then recorded for 1 min. They were then asked to watch a video on youtube where they had to pay attention https://www.youtube.com/watch?v=z9aUseqgCiY
The data was collected and the relative power of each powerband was analysed using a paired Student t-test. the data showed a significant increase in the gamma power bands when the participants were concentrating.Â
Therefore at the moment I will use the change in lowGamma power as the indicator of concentration. I will investigate what threshold will work best to provide a good user experience
The hedset provides a JSON object with data on EEG powerbands. the link shows what frequency of signal that each band represents.

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So the key point of this app is to be able to detect when somone is concentrating and then make something happen on the computer. The science behind this is inconclusive to say the least. while there is some evidence from clinical studies, trying to use commercially available units are mixed. One thing that is clear is that one of the best indicators of a shift to concentration is an increase in the relative power of the higher frequency EEG power bands (>20Hz). Â Â
Early sketch of what the eeg exhibit will look like. The user will listen to many audio tracks playing simultaneously. When a certain threshold of concentration is reached only one clear audio may be heard.
As I mentioned in my last post, we're going to be using a person's own brainwaves to allow them to filter an audio mix, with nothing but their minds. Here's a little schematic of how we hope to ach...
another blog post
think gear connector is a background process provided by Neurosky is connects to the serial port and distributes the JSON data through a TCP Socket server (local host). this will make accessing the data through processing easier.
We have never had access to more data. We are constantly connected through mobile networks, and we’ve never had more media with which to communicate. Yet, as the Internet continues to grow we are c...
my latest blog post in the group blog - What’s it all about

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Pick a Language, Any Language
Now that our Neurosky headset has arrived it is time to look at how to access, analyse and present the data. As mentioned elsewhere there is a well-developed python library looking at eeg analysis, so that was the first place to start. a number of repositories exist to access the Neurosky headset https://pypi.python.org/pypi/NeuroPy/0.1.
there also appears to be code available in javascript https://github.com/dluxemburg/node-neurosky,
however, when cosidering the overall product I decided to investigate the possibility of using Processing www.processing.org given the visual nature of the final product. there appears to be plently of code available online which might provide a solid basis to start coding.
http://www.magicandlove.com/blog/2012/03/15/neurosky-mindwave-and-processing/
Developing EEG App
the basic principle of EEG is that an electrical signal is recorded from the brain. By analysing this signal, it is possible to get insights into the workings of the brain. This area is well established in clinical research, but not so well studied in a commercial setting. CLinical EEG systems are extremely expensive and work by recording multiple channels of EEG from different areas of the brain. Some researchers have begun to develop open source code to help analyse this code.
One of these avenues is pyeeg, a python module to extract EEG information. I am currently investigating this as a means to analysing any EEG signals we record.
https://code.google.com/p/pyeeg/