Introduction
Electroencephalography In Neurodegeneration is an important component in the neurobiology of neurodegenerative diseases. This page provides detailed information about its structure, function, and role in disease processes.
Overview
Electroencephalography (EEG) is a non-invasive neurophysiological technique that records electrical activity of the brain through electrodes placed on the scalp. In neurodegenerative diseases, EEG has emerged as a valuable diagnostic and prognostic tool, capable of detecting abnormalities in neural oscillatory patterns, cortical connectivity, and event-related brain responses that reflect underlying neuronal dysfunction ([Babiloni et al., 2020](https://doi.org/10.1016/j.neubiorev.2020.09.020)). Unlike structural neuroimaging modalities such as [MRI](/diagnostics/neuroimaging) or [PET](/diagnostics/pet-imaging), EEG captures real-time neural dynamics with millisecond temporal resolution, making it uniquely suited for detecting transient electrophysiological abnormalities such as the periodic sharp wave complexes (PSWCs) pathognomonic of Creutzfeldt-Jakob disease or the posterior dominant rhythm slowing characteristic of Lewy body dementia. [@ref]
The advent of quantitative EEG (qEEG) analysis — involving spectral power analysis, coherence mapping, and source localization — has transformed EEG from a primarily qualitative clinical tool into a quantitative biomarker platform. Recent studies demonstrate that qEEG measures can differentiate Alzheimer's disease from frontotemporal dementia, predict cognitive decline in Parkinson's disease, and serve as pharmacodynamic markers in clinical trials of disease-modifying therapies ([Simmatis et al., 2025](https://doi.org/10.1177/13872877251327754)). EEG's low cost, portability, and non-invasive nature position it as an accessible complement to more expensive imaging and fluid biomarker approaches. [@ref2004]
Principles of Clinical EEG
Recording Technique
Standard clinical EEG uses the International 10-20 system for electrode placement, with 19-21 scalp electrodes providing coverage of frontal, temporal, parietal, and occipital regions. High-density EEG systems with 64-256 channels enable more precise source localization and spatial resolution. Key technical parameters include: [@refa]
| Parameter | Standard Clinical EEG | High-Density Research EEG | [@refb]
|-----------|----------------------|--------------------------| [@refc]
| Channels | 19-21 | 64-256 | [@ref2005]
| Sampling rate | 256-512 Hz | 512-2048 Hz | [@ref2004a]
| Duration | 20-30 minutes | 30-60+ minutes | [@refd]
| Electrode type | Cup electrodes, gel-based | Geodesic nets, dry electrodes | [@refe]
| Montage | Bipolar, referential | Source-space reconstruction | [@ref1998]
Frequency Bands
EEG oscillations are categorized into canonical frequency bands, each reflecting distinct neural processes: [@ref2020a]
- Delta (0.5-4 Hz): Predominant in deep sleep[@ref2020]; pathological excess in waking EEG indicates cortical dysfunction or deafferentation[@ref]
- Theta (4-8 Hz): Associated with drowsiness, memory encoding, and hippocampal function[@ref2004]; increased theta power is an early marker of Alzheimer's disease[@refa]
- Alpha (8-13 Hz): Dominant posterior rhythm in relaxed wakefulness[@refb]; slowing below 8 Hz is a hallmark of Lewy body dementia[@refc]
- Beta (13-30 Hz): Associated with active cortical processing[@ref2005]; decreased beta power occurs in mild cognitive impairment[@ref2004a]
- Gamma (>30 Hz): Linked to cortical binding and higher cognitive functions[@refd]; disrupted gamma oscillations are reported in Alzheimer's disease[@refe]
EEG in Specific Neurodegenerative Diseases
Alzheimer's Disease
EEG abnormalities in Alzheimer's disease reflect progressive cortical disconnection and cholinergic deficit. The characteristic EEG signature includes: [@ref2016]
Spectral changes: Increased delta and theta power with decreased alpha and beta power, yielding a characteristic "slowing" pattern. The alpha/theta ratio and the delta-theta/alpha-beta ratio (DTABR) serve as quantitative markers, with DTABR values increasing progressively from normal aging through mild cognitive impairment to AD dementia ([Babiloni et al., 2020](https://doi.org/10.1016/j.neubiorev.2020.09.020)). [@ref2017]
Coherence reduction: Interhemispheric and intrahemispheric EEG coherence — a measure of functional connectivity between brain regions — is reduced in AD, particularly in the alpha frequency band. Decreased coherence in the right centro-parietal region may serve as an early biomarker for mild cognitive impairment ([Jeong, 2004](https://doi.org/10.1016/j.clinph.2004.01.001)). [@ref2024]
Posterior dominant rhythm (PDR) slowing: The normal alpha-range PDR progressively slows with disease severity, though this finding is less specific than in Lewy body dementia.
Diagnostic performance: qEEG-based classifiers using spectral and connectivity features achieve 85-95% accuracy in distinguishing AD from healthy controls, and 80-90% accuracy in distinguishing AD from other dementias ([Simmatis et al., 2025](https://doi.org/10.1177/13872877251327754)).
Creutzfeldt-Jakob Disease
EEG is a cornerstone diagnostic tool for Creutzfeldt-Jakob disease, with characteristic periodic sharp wave complexes (PSWCs) serving as a WHO diagnostic criterion for sporadic CJD (sCJD). These PSWCs are:
- Morphology: Bi- or triphasic sharp waves, 100-600 ms in duration, recurring at 0.5-2 Hz intervals
- Distribution: Typically generalized and synchronous, though lateralized PSWCs may occur early in the disease
- Sensitivity: Present in approximately 64-67% of sCJD cases, with sensitivity increasing to >90% in advanced stages ([Steinhoff et al., 2004](https://doi.org/10.1016/j.clinph.2004.05.021))
- Specificity: 91% specificity; PSWCs occur in only 5-7% of patients with rapidly progressive cognitive decline from other causes
- Positive predictive value: 95% when PSWCs meet standard criteria
The timing and morphology of PSWCs vary by prion strain and disease subtype. The MM1 subtype (most common, ~70% of sCJD) typically shows classic bilateral synchronous PSWCs, while the MM2-cortical form may show focal sharp waves as an early-stage marker ([Bizzi et al., 2020](https://doi.org/10.1080/19336896.2020.1803516)). In familial forms such as fatal familial insomnia and Gerstmann-Sträussler-Scheinker syndrome, PSWCs are absent, but progressive loss of sleep architecture and spindle activity provide diagnostic clues.
Early disease stages may show frontal intermittent rhythmic delta activity (FIRDA) before PSWCs develop, and this finding should be regarded as compatible with CJD when clinical suspicion is high ([Hansen et al., 1998](https://doi.org/10.1111/j.1600-0404.1998.tb00617.x)).
Dementia with Lewy Bodies and Parkinson's Disease Dementia
EEG is particularly useful in the Lewy body spectrum disorders, where posterior dominant rhythm slowing below 8 Hz is recognized as a supportive biomarker in the 2017 diagnostic criteria ([McKeith et al., 2017](https://pubmed.ncbi.nlm.nih.gov/28592453/)):
- Posterior EEG slowing: The dominant rhythm slows to the pre-alpha/theta range (<8 Hz) in approximately 90% of DLB patients compared to only ~10% of AD patients, making this a valuable differentiating feature ([Bonanni et al., 2008](https://doi.org/10.1212/01.wnl.0000324257.48553.28))
- Cognitive fluctuations: Periodic fluctuations in background EEG frequency correlate with the hallmark cognitive fluctuations of DLB, and qEEG can quantify these fluctuations objectively
- Predictive value: In Parkinson's disease, qEEG measures — specifically the background rhythm frequency and relative theta band power — predict the development of dementia ([Klassen et al., 2011](https://doi.org/10.1212/WNL.0b013e318224af8d))
- Differential diagnosis: A classifier using Granger causality and theta/beta1 power ratio achieved 100% accuracy in differentiating DLB/PDD from AD and frontotemporal dementia ([Garn et al., 2017](https://doi.org/10.1016/j.jns.2017.02.037))
Frontotemporal Dementia
EEG changes in frontotemporal dementia are generally less pronounced than in AD or DLB, which itself serves as a differential diagnostic clue:
- Preserved posterior rhythm: The alpha rhythm is often preserved in behavioral variant FTD until later stages, unlike AD
- Frontal abnormalities: When present, EEG changes tend to localize to the prefrontal and anterior temporal regions, reflecting the characteristic frontotemporal atrophy pattern
- Spectral differences: FTD patients show less theta increase and less alpha decrease than AD patients, enabling qEEG-based differentiation
- Parietal changes: Despite the frontal predilection of the disease, recent studies show that the parietal lobe exhibits the most significant EEG changes, achieving 95.7% accuracy for FTD detection ([Kumar et al., 2025](https://doi.org/10.1007/s12559-025-10447-9))
Epilepsy and Neurodegenerative Disease
Many neurodegenerative diseases increase seizure risk, and EEG is essential for detecting subclinical epileptiform activity:
- AD and seizures: Alzheimer's disease patients have a 6-10 fold increased risk of seizures, particularly those with early-onset familial AD due to PSEN1 or APP mutations. Subclinical epileptiform activity detected on overnight EEG monitoring occurs in up to 42% of AD patients and may contribute to cognitive fluctuations ([Vossel et al., 2016](https://doi.org/10.1001/jamaneurol.2016.0006))
- Huntington's disease: Seizures occur in 3-8% of HD patients; juvenile-onset HD has higher seizure prevalence
- Prion diseases: CJD may present with myoclonic seizures alongside PSWCs
Event-related potentials (ERPs) are averaged EEG responses time-locked to specific sensory, cognitive, or motor events. Several ERP components serve as biomarkers for cognitive dysfunction in neurodegeneration[@ref1998]:
- P300 (P3b): A positive deflection occurring ~300-600 ms after stimulus onset; latency prolongation correlates with cognitive decline in Alzheimer's disease and is used as a marker of stimulus evaluation time[@ref2020a]
- N200 (N2): A negative component at ~200 ms associated with stimulus discrimination and mismatch negativity; reduced amplitude in mild cognitive impairment predicts progression to dementia[@ref2016]
- Mismatch Negativity (MMN): An automatic auditory change-detection response; impaired MMN in Alzheimer's disease reflects dysfunction in pre-attentive sensory memory[@ref2017]
- N400: A language-related component sensitive to semantic processing; N400 abnormalities occur in primary progressive aphasia variants[@ref2024]
P300 Component
The P300 (positive waveform ~300 ms post-stimulus) reflects attention allocation and working memory updating. In neurodegenerative disease:
- Amplitude reduction: P300 amplitude is decreased in AD, MCI, PD, and DLB
- Latency prolongation: P300 latency increases with cognitive decline, prolonging by approximately 1-2 ms/year in normal aging but 5-10 ms/year in AD
- Source shift: Normal P300 generators localize to frontal cortex, but in AD they shift to temporal lobe, reflecting frontal dysfunction ([Polich & Corey-Bloom, 2005](https://doi.org/10.1016/j.clinph.2005.05.012))
Mismatch Negativity (MMN)
MMN (negative waveform ~100-250 ms) reflects automatic auditory change detection and is generated primarily in the superior temporal gyrus and prefrontal cortex:
- Prolonged MMN latency in MCI and AD
- Reduced MMN amplitude correlates with disease severity
- May detect pre-symptomatic changes in at-risk individuals
N400 Component
The N400 (negative waveform ~400 ms) indexes semantic memory processing:
- Significantly reduced N400 effect in AD, reflecting breakdown of semantic networks
- Potential early biomarker as semantic deficits precede episodic memory loss in some AD variants
- Preserved in many patients with FTD behavioral variant
Emerging Applications
Machine Learning and Deep Learning
Recent advances in computational EEG analysis have dramatically improved diagnostic accuracy:
- Convolutional neural networks (CNNs): Deep learning models utilizing dynamic EEG connectivity patterns achieve 96-98% accuracy in differentiating AD and FTD from healthy controls ([Liu et al., 2025](https://doi.org/10.1038/s41598-025-02018-7))
- EEG connectome analysis: Graph-theoretical analysis of EEG functional networks reveals disrupted small-world properties and hub reorganization in neurodegenerative diseases
- Automated pipelines: Novel automated EEG processing pipelines enable standardized biomarker extraction suitable for large-scale clinical trials ([Simmatis et al., 2025](https://doi.org/10.1177/13872877251327754))
Portable and Wearable EEG
Miniaturized EEG devices are expanding access to neurophysiological monitoring:
- Consumer-grade EEG headsets: Devices with 4-14 channels can perform screening-level qEEG assessment
- Home monitoring: Long-term ambulatory EEG enables detection of intermittent abnormalities and nocturnal seizures
- Integration with [digital biomarkers](/diagnostics/digital-biomarkers): EEG data combined with gait, speech, and eye-tracking measures may improve early detection of neurodegeneration
Pharmacodynamic Biomarker
EEG serves as a real-time pharmacodynamic marker in clinical trials:
- Cholinesterase inhibitors partially restore alpha power and coherence in AD
- Anti-seizure medications normalize epileptiform activity detected on EEG
- Lecanemab and other anti-amyloid therapies may modulate EEG connectivity patterns
Normative Modeling
Patient-specific EEG biomarkers derived from normative models — comparing individual EEG profiles against age- and sex-matched healthy populations — enable personalized assessment of deviation patterns, constituting a preliminary step toward precision diagnostics for neurodegenerative diseases ([Puttaert et al., 2024](https://doi.org/10.1101/2024.03.01.582909)).
Advantages and Limitations
Advantages
- Non-invasive: No radiation exposure, minimal discomfort
- Low cost: Substantially less expensive than [PET imaging](/diagnostics/pet-imaging) or [MRI](/diagnostics/neuroimaging)
- High temporal resolution: Millisecond-level capture of neural dynamics
- Portability: Bedside and home monitoring possible
- Repeatability: Can be performed serially for disease monitoring
- Real-time assessment: Captures fluctuating neurological states (e.g., cognitive fluctuations in DLB)
Limitations
- Low spatial resolution: Cannot localize pathology as precisely as [neuroimaging](/diagnostics/neuroimaging)
- Artifact susceptibility: Eye movements, muscle activity, and electrode impedance can contaminate recordings
- Operator dependence: Visual EEG interpretation requires trained neurophysiologists
- Lack of standardization: Variable protocols across centers limit cross-site comparisons
- Non-specific findings: EEG slowing can result from many conditions (metabolic encephalopathy, medication effects, sleep deprivation)
Clinical Guidelines and Recommendations
- The International Federation of Clinical Neurophysiology (IFCN) recommends qEEG analysis as a supportive diagnostic tool in the evaluation of cognitive disorders
- The 2017 DLB diagnostic criteria include abnormally prominent posterior slow-wave activity with periodic fluctuations as a supportive biomarker ([McKeith et al., 2017](https://pubmed.ncbi.nlm.nih.gov/28592453/))
- WHO diagnostic criteria for probable sCJD include typical EEG (generalized periodic sharp wave complexes)
- EEG is recommended for all patients with rapidly progressive dementia to evaluate for CJD and subclinical seizures
See Also
- [Cognitive Assessment Tools for Neurodegenerative Diseases](/diagnostics/cognitive-assessments)
- [Cerebrospinal Fluid (CSF) Biomarkers in Neurodegeneration](/diagnostics/csf-biomarkers)
- [Digital Biomarkers for Neurodegeneration](/diagnostics/digital-biomarkers)
- [Neuroimaging in Neurodegenerative Diseases](/diagnostics/neuroimaging)
- [PET Imaging in Neurodegeneration](/diagnostics/pet-imaging)
- [Plasma Biomarkers in Neurodegeneration](/diagnostics/plasma-biomarkers)
External Links
- [American Clinical Neurophysiology Society — EEG Guidelines](https://www.acns.org/practice/guidelines)
- [IFCN Recommendations for EEG in Dementia](https://doi.org/10.1016/j.clinph.2020.01.002)
- [CDC Prion Disease Diagnostic Criteria](https://www.cdc.gov/prion-disease/hcp/diagnostic-criteria/)
Background
The study of Electroencephalography In Neurodegeneration has evolved significantly over the past decades. Research in this area has revealed important insights into the underlying mechanisms of neurodegeneration and continues to drive therapeutic development.
Historical context and key discoveries in this field have shaped our current understanding and will continue to guide future research directions.
EEG Abnormalities in Neurodegenerative Diseases
Mermaid diagram (expand to render)
References
[Unknown, (2020)](https://doi.org/10.1016/j.neubiorev.2020.09.020)
[Unknown, (n.d.)](https://doi.org/10.1177/13872877251327754)
[Unknown, (2004)](https://doi.org/10.1016/j.clinph.2004.05.021)
[Unknown, (n.d.)](https://pubmed.ncbi.nlm.nih.gov/28592453/)
[Unknown, (n.d.)](https://doi.org/10.1212/WNL.0b013e318224af8d)
[Unknown, (n.d.)](https://doi.org/10.1212/01.wnl.0000324257.48553.28)
[Unknown, (2005)](https://doi.org/10.1016/j.clinph.2005.05.012)
[Unknown, (2004)](https://doi.org/10.1016/j.clinph.2004.01.001)
[Unknown, (n.d.)](https://doi.org/10.1007/s12559-025-10447-9)
[Unknown, (n.d.)](https://doi.org/10.1038/s41598-025-02018-7)
[Unknown, (1998)](https://doi.org/10.1111/j.1600-0404.1998.tb00617.x)
[Unknown, (2020)](https://doi.org/10.1080/19336896.2020.1803516)
[Unknown, (2016)](https://doi.org/10.1001/jamaneurol.2016.0006)
[Unknown, (2017)](https://doi.org/10.1016/j.jns.2017.02.037)
[Unknown, (2024)](https://doi.org/10.1101/2024.03.01.582909)