QEEG database comparisons
Download the ISNR Conference 2015 oral presentation that addressed research comparing 4 major QEEG databases: qEEG-Pro, Neuroguide, BrainDX and HBI.
Comparing connectivity measures between qEEG-Pro and Neuroguide
In order to analyse the agreement between the qEEG-Pro database and the Neuroguide database for connectivity measures (Phase Lag and Phase Coherence), we analysed 10 different EEGs and calculated z-scores for ages ranging between 6 and 60 years old.
What is the qEEG-Pro database?
The qEEG-Pro database consists of a large number of resting state EEGs recorded from clients as part of the intake-procedure before the start of a Neurofeedback treatment, between 2008 and 2014. There are 1482 and 1231 clients in the qEEG-Pro database for the eyes closed and eyes open condition, respectively. The resting-state EEGs were recorded using high-end, modern EEG amplifiers.
Wait, the qEEG-Pro database consists of clients?
One of the pillars of QEEG is the assumption that psychopathology is correlated with features in resting-state EEG recordings. So comparing the EEG of your client with a database that consists of subjects with psychopathology can’t be a good idea … right?
Wrong. Each client that is present in the qEEG-Pro database also filled out an extensive, DSM-based questionnaire. Using statistical regression, the variance that is explained each client’s psychopathology was removed from the EEG data (download the qEEG-Pro manual for a detailed description of the methods used). For example, when Attention Deficit Disorder (ADD) questionnaire score positively correlates with frontal Theta, the individual variance in frontal Theta power can be explained by the ADD score to a certain extent. With regression analysis, the individual variance in frontal Theta power that can be explained by ADD score is removed from the EEG data. This means that the corrected frontal Theta power of all clients corresponds with an ADD score of zero.
The qEEG-Pro database, client-based!
In our view, each normative database should perform regression analyses using questionnaire data, even if the normative database consists of ‘healthy’ controls, since it is not very likely that each and every one of these subjects will score zero on all the DSM categories. Removing the variance from the EEG of ‘healthy’ subjects that can be explained by the variance in the questionnaire is arguably the most efficient way to ensure a ‘psychopathology free’ qEEG normative database.
Using a client-based normative database also has advantages of its own. First of all, while clients have to pay for a clinical EEG recording, ‘healthy’ subjects are generally paid for their participation in the EEG measurement. Research has shown that this can make a significant difference in brain activity and the resulting power distribution of the frequency band spectrum (e.g. Doñamayor et al., 2012, Cohen et al., 2007, Sobotka et al., 1992).
Moreover, clients may have different expectations than ‘healthy’ subjects regarding the EEG recording. Clients may expect there to be certain deviations in the EEG that accompany their psychopathology and it is very common for clients to be worried or stressed during the recording. In contrast, it can be argued that ‘healthy’ subjects probably experience less anxiety during the EEG recording than clients. Again, research has shown that anxiety level is significantly correlated with the power distribution of the frequency band spectrum (e.g. Knyazev et al., 2011, Putman, 2011, Andersen et al., 2009, Papousek et al., 2003).
In summary, there may be profound differences between the resting state EEG recordings of clients versus ‘healthy’ subjects that are not related to the psychological complaints of the clients. Comparing the EEG of a client with a normative database consisting of ‘healthy’ subjects might therefore lead to incorrect conclusions and ineffective treatment protocols.
Andersen, S. B., Moore, R. A., Venables, L., & Corr, P. J. (2009). Electrophysiological correlates of anxious rumination. International Journal of Psychophysiology, 71(2), 156-169.
Cohen, M. X., Elger, C. E., & Ranganath, C. (2007). Reward expectation modulates feedback-related negativity and EEG spectra. Neuroimage, 35(2), 968-978.
Doñamayor, N., Schoenfeld, M. A., & Munte, T. F. (2012). Magneto- and electroencephalographic manifestations of reward anticipation and delivery. Neuroimage, 62(1), 17-29.
Knyazev, G. G. (2011). Cross-frequency coupling of brain oscillations: An impact of state anxiety. International Journal of Psychophysiology, 80(3), 236-245.
Papousek, I., & Schulter, G. (2002). Covariations of EEG asymmetries and emotional states indicate that activity at frontopolar locations is particularly affected by state factors. Psychophysiology, 39(3), 350-360.
Putman, P. (2011). Resting state EEG delta-beta coherence in relation to anxiety, behavioral inhibition, and selective attentional processing of threatening stimuli. International Journal of Psychophysiology, 80(1), 63-68.
Sobotka, S. S., Davidson, R. J., & Senulis, J. A. (1992). Anterior brain electrical asymmetries in response to reward and punishment. Electroencephalography and Clinical Neurophysiology, 83(4), 236-247.