How to identify, detect, classify the Concentration
Hi, OpenBCI!
We are going to use Focus Widget to identify the concentration and then send the user's concentration to the Unity3D project through Network Widget to proceed with the game.
As we know that brain waves related to concentration are well collected from the frontal lobe, we placed the electrodes in the positions AF7, Fp1, Fpz, Fp2, and AF8.
I'm looking at FFT, Focus Widget, Band Power, and I'm especially looking at Band Power's Beta, Gamma waves.
Resting, watching videos, or solving problems does not show any pattern in Band Power.
Is there a proper way to identify the concentration?
Thank you always.
Comments
Beta or gamma band power as measured in frontal regions of the scalp can be considered a lossy function that discards all of cognition except whether one is awake.
To get the information you want to extract properly, you might need to measure gamma at the brain surface or even within the brain, with something such as implanted electrodes. The signal/noise ratio at the usual distance from brain to scalp surface electrode is otherwise far too small.
This type of classification is still an active research subject. As can be seen from the range of papers:
https://www.google.com/search?q=detecting+concentration+from+eeg
In the academic publishing field, a "review paper" is one that surveys the published research in a particular area, in a fairly exhaustive manner. Attempting to find patterns and consensus. In that search above, I came across at least one such review, below. It is focused on 'attention' vs 'concentration', but I believe there is significant overlap in those terms.
I also assume you have read the material on the Focus Widget, and how the metric was derived. The page also advises there are different variants of the ML that can be selected.
https://docs.openbci.com/Software/OpenBCISoftware/GUIWidgets/#focus-widget
Since the ML models were trained independent of the current subject, one would expect that fact to strongly influence accuracy of the widget.
https://brainflow.readthedocs.io/en/stable/UserAPI.html?highlight=mindfulness#brainflow-ml-model
Note also that Brainflow API itself uses the term 'mindfulness' vs 'concentration' or 'attention'. I don't know how these models were trained, but in meditation disciplines such as vipassana and TM, there are distinct differences in object-less meditation, remaining in the present moment, and concentration based practices. If you have further questions on how the Brainflow metrics were trained, I suggest asking on the Brainflow Slack, which you can sign up for here:
https://brainflow.org/
William
Very Thanks Open BCI Team.
Hi @Han, I merged your new thread into your previous thread on the same subject.
Yes, Java examples are on the Brainflow docs site.
https://brainflow.readthedocs.io/en/stable/Examples.html#java-eeg-metrics
https://brainflow.readthedocs.io/en/stable/Examples.html#java-band-power
https://brainflow.readthedocs.io/en/stable/UserAPI.html#java-api-reference [search for 'fft' in this page]
My hunch is that this is going to be too crude, as it is looking at the entirety of beta activity, not selectively.
https://brainflow.readthedocs.io/en/stable/Examples.html#java-eeg-metrics
Regards, William
As an addendum to the March 29 comments quoted above, I would suggest you investigate how you could TRAIN the Brainflow ML 'mindfulness' ('concentration') models to specifically work with you or your preferred subjects. On sample scenarios where you ask the subjects to engage in the desired task.
So to facilitate that, post questions on the Brainflow Slack, where the developer Andrey is responding.
Regards,