EEGparameters

EEG parameters refer to quantitative measures derived from electroencephalography (EEG) data that summarize brain electrical activity and its dynamics. They span acquisition settings, time-domain features, spectral properties, functional connectivity, and event-related measures.

Acquisition and quality: Data collection uses a standard or high-density electrode montage, with common sampling rates from 250 Hz to 1000 Hz. Consistent electrode impedance, appropriate referencing (such as Cz, average, or linked mastoids), and careful preprocessing are essential. Filtering often includes a bandpass around 0.5–70 Hz and a notch filter at 50 or 60 Hz to reduce line noise; the exact settings depend on the study and equipment. The choice of montage and reference can influence many downstream measures.

Time-domain parameters: Amplitude measures include peak-to-peak and root-mean-square values. Event-related potentials (ERPs) describe time-locked responses, with metrics such as component latency and amplitude (for example, P300). Power in specific epochs is often analyzed after baseline correction and segmentation into trials or tasks.

Spectral parameters: Power spectral density is estimated using methods like FFT or Welch and expressed as absolute or relative power across standard bands (delta, theta, alpha, beta, gamma). Changes in band power over time or in response to tasks can reflect cognitive states, sleep stages, or pathology.

Connectivity and network measures: Synchronization between channels is quantified with coherence, phase-locking value, and related metrics. More advanced analyses examine functional and effective connectivity, network topology, and coupling between rhythms, such as phase-amplitude coupling.

Artifact handling and reporting: Artifact rejection or correction (e.g., independent component analysis) is critical. Reporting should specify sampling rate, montage, reference, filtering, epoching, artifact criteria, and the specific parameters or metrics used, to enable reproducibility.

Applications: EEG parameters support clinical diagnosis, sleep research, and cognitive neuroscience studies, with interpretation dependent on data quality and methodological choices.