# Inter- and Intra-Subject Variability in EEG: A Systematic Survey

arXiv: https://arxiv.org/abs/2602.01019 · HTML: https://mlsanity.com/paper/2602.01019/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 69 references that are in the map.
- First posted on arXiv 1 Feb 2026.

## Builds on (most cited first, 3 of 3)

- [Variability of model-free and model-based quantitative measures of EEG](https://mlsanity.com/paper/1801.01711/) (2018)
- [Cross-subject Brain Functional Connectivity Analysis for Multi-task Cognitive State Evaluation](https://mlsanity.com/paper/2408.15018/) (2024)
- [Design and Quantitative Evaluation of an Embedded EEG Instrumentation Platform for Real-Time SSVEP Decoding](https://mlsanity.com/paper/2601.01772/) (2026)

## Built on by (most cited first, 4 of 4)

- [NeuroSonic: Conditional Flow Matching for EEG-to-Speech Reconstruction](https://mlsanity.com/paper/2606.24087/) (2026)
- [Cross-Subject Intracranial EEG Reconstruction from Scalp Recordings Using Multi-Scale Cross-Attention Transformers](https://mlsanity.com/paper/2605.18897/) (2026)
- [Temporal Feature Extractors in EEG Foundation Models: A Controlled Comparison Including a Pretrained Time-Series Model](https://mlsanity.com/paper/2606.30104/) (2026)
- [EEG-to-Report: An Annotation and Feature-Text Framework for Training Language Models on Clinical EEG](https://mlsanity.com/paper/2608.26153/) (2026)

## Abstract (arXiv)

Electroencephalography (EEG) underpins neuroscience, clinical neurophysiology, and brain-computer interfaces (BCIs), yet pronounced inter- and intra-subject variability limits reliability, reproducibility, and translation. This systematic review studies that quantified or modeled EEG variability across resting-state, event-related potentials (ERPs), and task-related/BCI paradigms (including motor imagery and SSVEP) in healthy and clinical cohorts. Across paradigms, inter-subject differences are typically larger than within-subject fluctuations, but both affect inference and model generalization. Stability is feature-dependent: alpha-band measures and individual alpha peak frequency are often relatively reliable, whereas higher-frequency and many connectivity-derived metrics show more heterogeneous reliability; ERP reliability varies by component, with P300 measures frequently showing moderate-to-good stability. We summarize major sources of variability (biological, state-related, technical, and analytical), review common quantification and modeling approaches (e.g., ICC, CV, SNR, generalizability theory, and multivariate/learning-based methods), and provide recommendations for study design, reporting, and harmonization. Overall, EEG variability should be treated as both a practical constraint to manage and a meaningful signal to leverage for precision neuroscience and robust neurotechnology.