This chapter focuses on quantitative EEG processing, also known as EEG trend analysis. These types of algorithms process raw EEG data in a quantitative way, and display the data in more compressed forms that may be easier for the amateur EEG reader to interpret. Specific QEEG algorithms described in this chapter include amplitude integrated EEG, FFT spectrogram, rhythmicity spectrogram, asymmetry indices, and seizure detectors. Specific roles for QEEG covered include seizure detection and detection of focal cerebral ischemia.

Speech consists of sound waves that propagate in the air from talker to hearer. Sound waves consist of rapidly changing pressure within the medium, normally air in the instance of speech. If the variations of pressure are regular, then the sound has a tonal quality. Most, but not all, speech sounds have this tonal quality. Sound amplitude relates to the amount of energy and is perceived as loudness. The speed of pressure variation is called frequency and is perceived as pitch. Individual sound waves combine additively, creating complex sound waves. Acoustic analysis of speech creates spectrograms, giving a visual representation of the original sound. In certain conditions, such as the interior of the vocal tract, sound reverberates in a self-additive way, giving rise to cavity resonance. The frequencies of this resonance can be modified by adjustments in the position of the speech articulators, creating different speech sounds, which occurs through the source--filter theory.

This chapter discusses the drawback of Fourier analysis and the methods that can overcome its limitations. In general, Fourier analysis does not include information about time, particularly events. A slight modification of Fourier analysis can allow the addition of a dimension in time: by dividing the time series into smaller segments and doing the Fourier Transform for each segment, a method called short-time Fourier Transform (STFT) is introduced. Wavelet analysis is then discussed as a much better alternative to or replacement for STFT. It involves scaled and translated convolution with a short base function (short in the sense that it is essentially non-zero only in a finite interval). Wavelet analysis uses different base functions than the Fourier Transform. They are limited in time (unlike the infinitely long sinusoidal functions) and can be stretched or compressed to represent different scales (equivalent to frequencies). This method will allow the resolution of events at different times and different scales.