# A Tale of Single-channel Electroencephalogram: Devices, Datasets, Signal Processing, Applications, and Future Directions

arXiv: https://arxiv.org/abs/2407.14850 · HTML: https://mlsanity.com/paper/2407.14850/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 222 references that are in the map.
- First posted on arXiv 20 Jul 2024.

## Builds on (most cited first, 10 of 10)

- [DeepSleepNet: a Model for Automatic Sleep Stage Scoring based on Raw Single-Channel EEG](https://mlsanity.com/paper/1703.04046/) (2017)
- [SleepEEGNet: Automated Sleep Stage Scoring with Sequence to Sequence Deep Learning Approach](https://mlsanity.com/paper/1903.02108/) (2019)
- [EEG-GAN: Generative adversarial networks for electroencephalograhic (EEG) brain signals](https://mlsanity.com/paper/1806.01875/) (2018)
- [K-EmoCon, a multimodal sensor dataset for continuous emotion recognition in naturalistic conversations](https://mlsanity.com/paper/2005.04120/) (2020)
- [EEG-based Brain-Computer Interfaces (BCIs): A Survey of Recent Studies on Signal Sensing Technologies and Computational Intelligence Approaches and their Applications](https://mlsanity.com/paper/2001.11337/) (2020)
- [Modeling EEG data distribution with a Wasserstein Generative Adversarial Network to predict RSVP Events](https://mlsanity.com/paper/1911.04379/) (2019)
- [You Only Acquire Sparse-channel (YOAS): A Unified Framework for Dense-channel EEG Generation](https://mlsanity.com/paper/2406.15269/) (2024)
- [Light-Weight 1-D Convolutional Neural Network Architecture for Mental Task Identification and Classification Based on Single-Channel EEG](https://mlsanity.com/paper/2012.06782/) (2020)
- [Topological EEG Nonlinear Dynamics Analysis for Emotion Recognition](https://mlsanity.com/paper/2203.06895/) (2022)
- [Alpha Wavelet Power as a Biomarker of Antidepressant Treatment Response in Bipolar Depression](https://mlsanity.com/paper/1702.04972/) (2017)

## Built on by (most cited first, 13 of 13)

- [Neural-MCRL: Neural Multimodal Contrastive Representation Learning for EEG-based Visual Decoding](https://mlsanity.com/paper/2412.17337/) (2024)
- [EEG Emotion Copilot: Optimizing Lightweight LLMs for Emotional EEG Interpretation with Assisted Medical Record Generation](https://mlsanity.com/paper/2410.00166/) (2024)
- [Linguistics and Human Brain: A Perspective of Computational Neuroscience](https://mlsanity.com/paper/2602.08275/) (2026)
- [Non-Invasive Reconstruction of Intracranial EEG Across the Deep Temporal Lobe from Scalp EEG based on Conditional Normalizing Flow](https://mlsanity.com/paper/2603.03354/) (2026)
- [LEL: Lipschitz Continuity Constrained Ensemble Learning for Efficient EEG-Based Intra-subject Emotion Recognition](https://mlsanity.com/paper/2504.09156/) (2025)
- [PA-TCNet: Pathology-Aware Temporal Calibration with Physiology-Guided Target Refinement for Cross-Subject Motor Imagery EEG Decoding in Stroke Patients](https://mlsanity.com/paper/2604.16554/) (2026)
- [CFSPMNet: Cross-subject Fourier-guided Spatial-Patch Mamba Network for EEG Motor Imagery Decoding in Stroke Patients](https://mlsanity.com/paper/2605.10111/) (2026)
- [STAMBRIDGE: Spectral-Temporal Amplitude-aware Mid-Feature Bridge for EEG Visual Decoding](https://mlsanity.com/paper/2605.23137/) (2026)
- [A Graph Neural Network based on a Functional Topology Model: Unveiling the Dynamic Mechanisms of Non-Suicidal Self-Injury in Single-Channel EEG](https://mlsanity.com/paper/2508.11684/) (2025)
- [Region-aware Spatiotemporal Modeling with Collaborative Domain Generalization for Cross-Subject EEG Emotion Recognition](https://mlsanity.com/paper/2601.15615/) (2026)
- [TGSD: Topology-Guided State-Space Diffusion Framework for EEG Spatial Super-Resolution](https://mlsanity.com/paper/2606.03998/) (2026)
- [SUP-MCRL: Subject-aware Unified Pseudo-feature Coded Multimodal Contrastive Representation Learning for EEG Visual Decoding](https://mlsanity.com/paper/2606.16615/) (2026)
- [Virtual iEEG from Scalp EEG: Charting the Landscape of Source Imaging, Intracranial Inference and Reconstruction](https://mlsanity.com/paper/2608.26998/) (2026)

## Abstract (arXiv)

Single-channel electroencephalogram (EEG) is a cost-effective, comfortable, and non-invasive method for monitoring brain activity, widely adopted by researchers, consumers, and clinicians. The increasing number and proportion of articles on single-channel EEG underscore its growing potential. This paper provides a comprehensive review of single-channel EEG, focusing on development trends, devices, datasets, signal processing methods, recent applications, and future directions. Definitions of bipolar and unipolar configurations in single-channel EEG are clarified to guide future advancements. Applications mainly span sleep staging, emotion recognition, educational research, and clinical diagnosis. Ongoing advancements of single-channel EEG in AI-based EEG generation techniques suggest potential parity or superiority over multichannel EEG performance.