Dear all,
We are performing some tests using the ADS1299 front-end, and want to measure EEG in Fp1 (above my left eyebrow). Reference and GND are in the left earlobe. How can the relaxation and attention indexes be estimated from this EEG? There are commercial devices available such as the NeuroSky Mindwave, but they do not inform how attention and relaxation are estimated.
Thank you in advance.
Regards from Brazil,
Fernando
As fas as I know, alpha waves are related to relaxation, and beta waves are associated with attention. A priori, relaxation (concentration) could be measured as the power of the alpha (beta) waves with respect to a given baseline, possibly measured at the beginning of the experiment. Is this ok?
Any suggestions, please?
Thanks!
Fernando
Dear Fernando,
I used NeuroSky Mindwave several years ago, and you are right: alpha is mainly related to relaxation and beta with concentration and arousal. As you probably know, EEG potentials measured at the single frontal sensor and reference at ear lobe are subtracted through common mode rejection.
I recomend you the paper DOI: 10.1177/1550059411435857 by Johnston et al. 2012, in which the authors use several mental states (for instance, rest, concentration, relaxation) to elicit the typical EEG frequency bands associated to each state. Moreover, this paper aims to validate this system compared to other 'standard' EEG system (NuAmps).
I hope you find this helpful.
Best,
Francisco.
A nice idea would be to apply nonlinear analysis. Nonlinear deterministic features are related with the amount of information being processed in the respective lobe, in this case, frontal lobe. More specifically, some measures of complexity or entropy, in alpha and/or beta waves, might help to determine relaxation/concentration levels from frontal channels. Maybe complexity indices are lower in relaxation condition?
I have used the NeuroSky's Mindwave for research. The attention and relaxation values lay in a range between 0 and 100. These are explained as it follows: Values in [1, 20] are considered strongly lowered. [20, 40] are considered reduced levels. [40, 60] are considered neutral. [60, 80] are considered slightly elevated. [80, 100] are considered elevated. Most of the times these are inversely proportional: More attention leads to less relaxation and viceversa. As you pointed out, the company does not inform how the attention and relaxation values are estimated but I could assume these are mereley calculated and related to alpha and beta waves since the device also makes the distinction between high and low alpha and beta values.
Thank you very much for your kind answers Francisco Muñoz , Alexander Cerquera , Joseph A. Sandford
, Ricardo Diaz-Rincon . we had some challenges with our device and it is now working better. I've downloaded the paper suggested by Francisco here:Article EEG From a Single-Channel Dry-Sensor Recording Device
Alexander Cerquera , which paper do you recommend for complexity or entropy, in alpha and/or beta waves? I guess you refer to the entropy for detecting narrowband alpha and beta waves, right? Thanks!
Fernando A. Marengo Rodriguez I published this paper in 2017:
Cerquera, A., Gjini, K., Bowyer, S. M., & Boutros, N. (2017). Comparing EEG nonlinearity in deficit and nondeficit schizophrenia patients: preliminary data. Clinical EEG and neuroscience, 48(6), 376-382.
where we worked with the complexity of alpha waves on frontal sites. We found that complexity in deficit-schizophrenia patients is lower than in non-deficit and control patients. We associated this finding to cognitive impairments in deficit schizophrenia patients. Try to download it; if you can't, then send me by inbox your email account where I can send you it to. I think that I may not share it here.
A very similar situation was faced in the case of the prediction of deadly, medical drug-induced Torsades de Pointes arrhythmias (a variant of ventricular tachycardia) from ECG recordings. Finally, I went for entropy measures -- in this case permutation entropy -- which is capable to discern even minuscule changes on biosignals.
Apparently, the same ECG curves discern arrhythmic from non-arrhythmias rabbits up to one hour before the onset of arrhythmia!
In cooperation with a colleague, AI & ML methods were applied to entropy curves to classify both cases.
Entropy measures are gaining greater attention in the classification and prediction of biosignals.
Details of the early preprint of this methodic paper -- due to COVID19 -- can be found here
https://www.researchgate.net/publication/340460356_Application_of_Machine_Learning_and_Complexity_in_Medicine_Prediction_of_Drug_Induced_Arrhythmias_in_Rabbit_Model_up_to_One_Hour_Before_Their_Onset_early_version_requested_due_to_COVID-19_unfinished_m
- Badges
- Science topic
- Similar topics
- Biological Science
- Bioecology
- Systems Ecology
- Landscape Ecology
- Connectivity