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Techniques commonly found in digital signal analysis have been extended to the analysis of electroencephalography (EEG). Techniques include wavelet analysis and Fourier analysis.

The analog signal comprising the microvoltage time series of the EEG, is sampled digitally with analog to digital technology and 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.

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 use of qEEG techniques in investigations in clinical and research settings are on going.