Quantitative Electroencephalography (QEEG) is the use of digital signal analysis to extend the analysis of electroencephalography (EEG). Though more modern approaches such as wavelet analysis exist, the most common analytic approach uses Fourier analysis. The analog signal comprising the microvoltage time series of the EEG is sampled digitally with analog to digital technology with sampling rates adequate to over-sample the signal (using the Nyquist principle of exceeding twice the highest frequency being detected). Modern EEG amplifiers use adequate sampling to resolve the EEG across the traditional medical band from DC to 70 or 100 Hz, using sample rates of 250/256, 500/512, to over 1000 samples per second, depending on the intended application.
The Fourier decomposes the EEG time series into a voltage by frequency spectral graph commonly called the “power spectrum”, with power being the square of the EEG magnitude, and magnitude being the integral average of the amplitude of the EEG signal, measured from(+) peak-to-(-)peak), across the time sampled, or epoch. The epoch length determines the frequency resolution of the Fourier, with a 1 second epoch providing a 1 Hz resolution (plus/minus 0.5 Hz resolution), and a 4 second epoch providing ¼ Hz, or plus/minus 0.125 Hz resolution.
The assumptions of the Fourier include stationarity, or the persistence of the signal from the beginning of time to eternity, so EEG transients are not well described with the Fourier. The Fourier also assumes a stable state, so wake/drowsiness transitions or other state changes are not described well with the Fourier. Never-the-less the Fourier remains the most common form of qEEG analysis. QEEG has been accepted by for clinical application in some areas, such as cerebro-vascular disorders and epilepsy, though it remains yet to be accepted in other clinical areas, such as diagnosing mild traumatic brain injury or psychiatric disorders. The American Academy of Neurology has a position paper delineating the accepted areas of clinical application, with the research areas and other uses for non-diagnostic purposes comprising a wide range of applications, including being areas such as prediction of medication outcomes or other neuromodulatory protocol.
The underlying qEEG patterns remain stable over days, weeks, months and years, providing a reliable signal for analysis. These patterns appear to be generally inheritable, with similar spectral profiles for monozygotic twins, and have been proposed as endophenotypes which are seen in the normal population as well as in clinical groupings, with these genetically determined patterns predicting similar treatment approaches.
The underlying EEG variance is almost totally predicted by the endophenotypes, with little added variance seen between clinical DSM groupings due to the lack of grounding physiologically of the DSM behavioral categories. Applications of the qEEG for protocol development , treating a client for a disorder using medication or neuromodulation, also seems to be a fruitful area of investigation.
The use of qEEG techniques in investigations of healing and consciousness seems to be a fruitful area of application for some higher levels of analysis, including cross-spectral coupling of frequencies and nested rhythms.
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