Quantitative electroencephalography (QEEG), often called brain mapping, is an advanced method of analyzing the brain’s electrical activity. It builds upon the traditional electroencephalogram (EEG), which measures the spontaneous electrical impulses generated by neurons. QEEG subjects this raw electrical signal to sophisticated computer analysis, transforming the visual trace into objective, statistical data about brain function. This process provides clinicians and researchers with a quantitative understanding of brain patterns.
Defining Quantitative Electroencephalography
The “quantitative” aspect of QEEG involves applying complex mathematical algorithms to digitized EEG data. Unlike a standard EEG, which offers a visual trace, QEEG uses digital signal processing (e.g., Fourier analysis) to break the continuous electrical signal into constituent frequency bands associated with different states of consciousness: Delta (0.5–4 Hz) for deep sleep, Theta (4–7 Hz) for drowsiness, Alpha (8–13 Hz) for relaxed wakefulness, and Beta (13–30 Hz) for active concentration.
QEEG analysis provides numerical metrics for each band, including absolute power (strength of activity) and relative power (percentage of total electrical activity). The method also calculates brain connectivity measures, such as coherence, which indicates synchronization between two regions, and phase, which measures the timing difference of signals. These parameters allow QEEG to identify subtle deviations in brain function.
The Data Acquisition and Processing Pipeline
The brain mapping process begins with data collection, using electrodes placed non-invasively on the scalp following the standardized International 10-20 System. These sensors record the raw electrical signals, capturing a continuous time series of activity. Specialized software then performs artifact removal, filtering out electrical noise from sources like eye movements or muscle tension, ensuring the initial EEG signal is clean.
The refined data is digitized and processed, transforming the time-series into spectral features like power and frequency. This data is statistically compared against a large, age-matched normative database of healthy individuals.
This comparison converts the individual’s brain metrics into Z-scores, which quantify how many standard deviations a measurement deviates from the mean population. Scores exceeding two are often considered atypical, allowing for the objective identification of areas with significantly higher or lower activity. These Z-scores are translated into the visual, color-coded “brain map,” highlighting areas of statistical deviation across the scalp.
Clinical and Research Applications
QEEG aids in the assessment of various neurological and psychological conditions. For individuals with Attention-Deficit/Hyperactivity Disorder (ADHD), QEEG often reveals increased power in slower frequency bands (Theta and Delta), resulting in an elevated Theta/Beta ratio. For Traumatic Brain Injury (TBI), coherence and phase analysis can quantify injury severity and associated diffuse axonal damage.
The technique also assesses mood disorders; for instance, reduced Beta wave production in the left prefrontal cortex has been linked to depression. The objective data guides treatment selection, such as developing specific protocols for neurofeedback, which trains individuals to self-regulate brain activity, or predicting responses to pharmacological treatments. QEEG is also a valuable research tool for identifying neural correlates of cognition and monitoring functional changes over time.
Interpretation and Context
Interpreting a QEEG brain map involves analyzing color-coded topographical images. Colors like red and blue indicate statistical deviations from the normative database; for example, red might represent a positive Z-score (excess activity), while blue signifies a negative Z-score (deficit of activity). This visual representation allows clinicians to quickly identify regions and types of atypical electrical activity.
QEEG is an assessment tool, not a standalone diagnostic test. The statistical findings must be integrated with a comprehensive clinical history, patient symptoms, and other diagnostic information by a qualified professional. Interpretation requires expertise to ensure the findings are placed within the correct clinical context.