Curriculum

The EEG is, at root, the macroscopic shadow of microscopic ion movements. This module builds the electrochemical engine from first principles: the resting potential, the Nernst and Goldman equations, channel gating, the action potential, and the synaptic currents that ultimately sum into the scalp signal. It then pushes into the biophysics an expert must own - cable theory, the chloride-gradient logic that decides whether GABA inhibits or excites, the slow conductances that pace rhythms, and the energetic and pharmacologic levers that move the trace at the bedside.

How the orderly architecture of cortical pyramidal neurons converts microscopic synaptic currents into measurable scalp potentials. This module develops the dipole model, the summation requirement, volume conduction through the head, and the rules that map a cortical generator to a scalp field - then confronts the inverse problem, the gyral geometry that hides whole populations from the scalp, and the forward-modeling tools that define the state of the art in 2026.

The instrumentation chain that turns a microvolt-scale scalp field into an interpretable trace: electrode electrochemistry and impedance, the international 10-20 measurement system, the logic of bipolar, referential, and average montages, and the differential amplifier whose common-mode rejection makes recording in a noisy world possible. The module then develops the reference problem in depth, the localization rules an expert applies reflexively, and the dry-electrode and high-density developments reshaping practice in 2026.

How the continuous analog scalp signal becomes a digital record without distortion: sampling and the Nyquist theorem, aliasing and the anti-alias filter, the high-pass/low-pass/notch filter chain, artifact rejection, and the disciplined pursuit of signal-to-noise ratio. The module then develops the phase distortion and filter-artifact pitfalls that mislead even experienced readers, the source-separation methods that define modern artifact handling, and the cognitive biases - including misplaced trust in 2026-era automated tools - that corrupt interpretation.

The five frequency bands are not arbitrary bins but distinct neurophysiological states, each with its own generators, topography, and clinical meaning. This module dissects delta through gamma at the molecular and network level, anchors each band to quantitative descriptors, and builds the posterior dominant rhythm into the central organizing feature of the waking record - with explicit attention to the artifacts, cognitive biases, and edge cases that derail interpretation.

Brain state is the hidden variable behind every EEG, and the same waveform can be normal in one state and ominous in another. This module traces the orderly march from alert wakefulness through the NREM stages to REM at the level of the neuromodulatory switches that drive it, shows how state and circadian phase reshape interpretation, and details the graphoelements, edge cases, and biomarkers that make state the lens through which all abnormality is judged.

Beneath every rhythm lies a network. This module builds the systems-level account of how excitatory-inhibitory loops and the thalamocortical-reticular circuit generate oscillations, how the biophysics of summed dipoles and synchrony set scalp amplitude, and how connectivity turns local activity into the large-scale patterns that EEG actually measures - then carries that framework into the 2026 network view of epilepsy, consciousness, and degeneration, with its real controversies and limits.

Physiologic artifacts are biological signals from outside the cortex that intrude on the EEG. Recognizing their stereotyped fields, polarity, and timing is the foundation of clean interpretation and the first defense against overreading.

Technical artifacts arise from the recording apparatus and its environment rather than from the patient. Understanding the electronics that create them turns each one into a solvable engineering problem at the source rather than a filtering problem after the fact.

A family of benign normal variants and sleep transients carries sharp, spiky, or rhythmic morphology that can be mistaken for true epileptiform activity. Knowing their stereotyped features, and the cognitive biases that inflate them, prevents the overreading that leads to a misdiagnosis of epilepsy.

Interictal epileptiform discharges are the EEG fingerprint of a hyperexcitable cortical network. This module dissects their morphologic criteria, the paroxysmal depolarizing shift and microcircuit failures that generate them, how their field localizes the irritative zone, and how modern quantitative markers such as high-frequency oscillations and spike-detection algorithms refine an inherently Bayesian read.

The electrographic seizure is defined not by a single waveform but by evolution. This module develops the law of ictal evolution, contrasts focal and generalized onsets and their network mechanisms, traces propagation dynamics and the quantitative tools that measure them, and reads the post-ictal state as a window on cortical recovery and risk.

Status epilepticus is a neurologic emergency in which the seizure-terminating machinery fails. This module covers convulsive and nonconvulsive forms, the elusive subtle status, the receptor pharmacology of self-perpetuation, the Salzburg criteria and ACNS standardized terminology for EEG diagnosis, and the ictal-interictal continuum that defies binary categorization.

Epilepsy syndromes integrate seizure type, EEG signature, age of onset, and etiology into actionable diagnoses. This module maps the electrographic hallmarks of the major focal, generalized, pediatric, and genetic syndromes onto their underlying networks, incorporates the 2022 ILAE syndrome framework and the molecular genetics now reshaping classification, and frames the whole enterprise as Bayesian pattern integration.

The EEG signatures of diffuse cerebral dysfunction - graded slowing, triphasic waves, and pharmacologic fast activity - and the Bayesian reasoning that separates a metabolic encephalopathy from nonconvulsive status epilepticus.

How the EEG after cardiac arrest evolves from suppression toward recovery or toward malignant periodicity, the neurophysiology of burst-suppression and identical bursts, and the multimodal, self-fulfilling-prophecy-aware framework for using it in prognosis.

Why continuous EEG transformed the diagnosis of nonconvulsive seizures in the ICU, how electrographic seizures and the ictal-interictal continuum are defined, how to read color density spectral arrays and rhythmicity trends, and the limits of quantitative EEG as a screening tool.

A consolidated framework for the EEG variables that carry prognostic weight in coma - continuity, reactivity, the burst-suppression ratio, and the special coma patterns - and how to map each to outcome probabilistically without overreaching.

The graphoelements and staging rules of NREM and REM sleep, the thalamocortical machinery that builds spindles and slow oscillations, the cortical UP and DOWN states of slow-wave sleep, the spindle-slow-oscillation-ripple coupling that consolidates memory, and the cyclic hypnogram that organizes a night into ultradian epochs.

How the electrophysiology of sleep breaks down in narcolepsy, NREM and REM parasomnias, obstructive sleep apnea, and REM sleep behavior disorder, the neurophysiology of state-boundary failure that unifies them, and the EEG and polysomnographic markers - SOREMPs, arousals, REM-without-atonia - that distinguish them.

The thalamocortical loop is the master oscillator of the EEG. Its intrinsic membrane currents and reciprocal circuitry generate spindles, alpha, and the spike-wave complexes of generalized epilepsy.

The brain is a network, and EEG lets us estimate how its regions coordinate. Functional and effective connectivity metrics, recast as graphs, expose the topological signatures of health and disease.

Oscillations are not epiphenomena but the syntax of cognition. Alpha gates information by inhibition, gamma binds features into percepts, and cross-frequency coupling orchestrates the two.

How an expert reads a routine EEG: a fixed sequence that characterizes the background before hunting abnormalities, then localizes and lateralizes findings, separates true epileptiform activity from the benign variants that mimic it, and translates electrography into a structured, defensible clinical report.

How prolonged video-EEG captures habitual events, the controlled risks of medication withdrawal, the concordance logic by which an epilepsy surgery candidate is built from semiology, ictal onset, and imaging, and where candidate biomarkers such as high-frequency oscillations actually stand after their first randomized trial.

How continuous EEG in the critically ill grades coma by reactivity and continuity, detects the nonconvulsive seizures that bedside exam misses, informs prognosis after cardiac arrest without becoming a self-fulfilling prophecy, and is read through the pervasive confounder of sedation.

How the neonatal EEG is read against postmenstrual age, why neonatal seizures are subtle and electroclinically dissociated, how hypoxic injury writes itself onto the background under the confounders of cooling and sedation, and how the brain's electrical maturation unfolds week by week.

Quantitative EEG converts the raw waveform into spectral and connectivity numbers, opening powerful analyses but also a wide door to artifact-driven and statistical misuse. This module builds the mathematics, distinguishes the validated from the investigational, and dwells on the failure modes that make qEEG dangerous when applied uncritically.

The mathematics behind every spectrum and spectrogram - the Fourier transform, sampling and aliasing, its stationarity assumption, windowing and leakage, the multitaper estimator, and the wavelet transform that trades frequency resolution for time resolution under an unbreakable uncertainty bound.

How algorithms detect seizures, classify states, and denoise EEG; how self-supervised foundation models and transformers are reshaping the field in 2026; and why generalization failure, dataset shift, label noise, and weak validation make most published performance numbers optimistic.

How EEG-based brain-computer interfaces decode intention - through motor-imagery rhythms, the P300 response, and steady-state visual evoked potentials - the decoding pipeline and its modern deep-learning refinements, and the practical limits of throughput, calibration, and reliability that constrain clinical use.

How experts convert local waveform morphology into a spatial field, a temporal trajectory, and a diagnosis - why the underlying task is an ill-posed inverse problem, and how rapid gestalt recognition and slow analytic verification combine, fail, and recombine into a calibrated read.

The three dominant failure modes of EEG interpretation - over-calling epileptiform discharges, missing subtle seizures and nonconvulsive status, and misclassifying artifact - their mechanisms, the empirical evidence on how often they occur, and why their downstream costs are profoundly and asymmetrically distributed.

How clinical history and prior reports distort EEG reads through anchoring, confirmation, availability, framing, and satisfaction of search - framed rigorously in Bayesian terms with worked likelihood-ratio arithmetic - and the debiasing strategies, from blinded review to structured terminology, that demonstrably help and where they reach their limits.

How to appraise the EEG evidence base critically - the metrics and pitfalls of diagnostic accuracy and interrater reliability, the appraisal of prognostic studies and the self-fulfilling prophecy, the design issues specific to epilepsy and seizure-monitoring trials, and the 2026 reckoning with AI-assisted reading and automation bias.

A unifying framework that converts the preceding ten pillars into a single, disciplined interpretive act — from background characterization to quantified uncertainty and the next clinical step.