Can we cluster brainwaves (EEG) according to certain symptoms? ex ADHD or anxiety. not like delta, theta, beta....
I know that EEG can be categorized according to frequency. However, we need more sub-categorization according to decease or symptoms. If anyone have source to learn this please send me.
when EEG data is used in frequency domain, there would be a wide range of frequencies with various intensities. according to the physiological and behavioral status, the intensity of some frequencies are increased or decreased. these variation is categorized as delta, theta....however, i think it is hard to find a relation between some disorders like anxiety and EEG data in frequency domain to cluster them like delta or theta.... i think that diseases and disorders will affect EEG frequencies but not in the same way in different individuals.
1) Classical studies have shown differential EEG effects in some frequency bands between symptomatic population (i.e., ADHD, depression, anxiety) and age-matched healthy controls during resting state and also task conditions. According to your case population and considering previous works, I guess that first, you would have to show how your results resemble previous ones (i.e., with standard analyses) and also discuss new findings (I guess you have previous hypotheses on your experiment). By the way, are you considering resting state or task-related signals?
2) "However, we need more sub-categorization according to disease or symptoms". Which is your criteria to perform a more detailed sub-categorization? This question would raise to a subsequent analysis where you can discuss the results of your approximation (i.e. different frequency waves or spikes could be defined with a new index to mark those segments satisfying certain condition).
Article The EEG and reaction to photic stimulation as an index of an...
Using EEG source analysis, we compared a small group of patients with myalgic encephalomyelitis (ME) with a group healthy controls and identified the sources that discriminated between the two groups at better than 95% accuracy. However, this does not mean that dysregulation patient vs HC group was due solely to illness - ME was only a selection criteria. We also regressed self-report data on these same subjects to find a strong correlation between scores on certain cognitive symptoms and the EEG sources in certain frequency bands. However, there are many possible reasons the correlations were found. For example, the patients were all recruited many years after being diagnosed with ME and other factors such as stressful life events, toxic exposure, deconditioning effects, events prior to illness, sleep deprivation, poor nutrition, co-morbid anxiety, depression, etc are difficult to control for. This is among the reasons why is EEG, as with other imaging modalities, is not a stand alone diagnostic and should only be used in conjunction with clinical evaluation, neuropsychological assessment, and other diagnostic tests taken altogether to derive a clinical diagnosis. I've attached a link to an excellent resource for more information on the latest quantitative EEG assessment techniques and foremost EEG biofeedback treatments available.
http://www.appliedneuroscience.com
- Badges
- Science topic