mlsanityData to 7 Oct 2026

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

arXiv 2407.14850 ↗First posted 20 Jul 2024cs.CVcited by 13

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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.

Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). Builds on 10 of its 222 references that are in the map. First posted on arXiv 20 Jul 2024.

Builds on

10 of 222 references.

References · 10

  1. 2017DeepSleepNet: a Model for Automatic Sleep Stage Scoring based on Raw Single-Channel EEGcited by 154
  2. 2019SleepEEGNet: Automated Sleep Stage Scoring with Sequence to Sequence Deep Learning Approachcited by 54
  3. 2018EEG-GAN: Generative adversarial networks for electroencephalograhic (EEG) brain signalscited by 46
  4. 2020K-EmoCon, a multimodal sensor dataset for continuous emotion recognition in naturalistic conversationscited by 30
  5. 2020EEG-based Brain-Computer Interfaces (BCIs): A Survey of Recent Studies on Signal Sensing Technologies and Computational Intelligence Approaches and their Applicationscited by 21
  6. 2019Modeling EEG data distribution with a Wasserstein Generative Adversarial Network to predict RSVP Eventscited by 5
  7. 2024You Only Acquire Sparse-channel (YOAS): A Unified Framework for Dense-channel EEG Generationcited by 5
  8. 2020Light-Weight 1-D Convolutional Neural Network Architecture for Mental Task Identification and Classification Based on Single-Channel EEGcited by 2
  9. 2022Topological EEG Nonlinear Dynamics Analysis for Emotion Recognitioncited by 2
  10. 2017Alpha Wavelet Power as a Biomarker of Antidepressant Treatment Response in Bipolar Depressioncited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 2; 2026: 9.

13 papers.

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  1. 2024Neural-MCRL: Neural Multimodal Contrastive Representation Learning for EEG-based Visual Decodingcited by 14
  2. 2024EEG Emotion Copilot: Optimizing Lightweight LLMs for Emotional EEG Interpretation with Assisted Medical Record Generationcited by 6
  3. 2026Linguistics and Human Brain: A Perspective of Computational Neurosciencecited by 3
  4. 2026Non-Invasive Reconstruction of Intracranial EEG Across the Deep Temporal Lobe from Scalp EEG based on Conditional Normalizing Flowcited by 2
  5. 2025LEL: Lipschitz Continuity Constrained Ensemble Learning for Efficient EEG-Based Intra-subject Emotion Recognitioncited by 1
  6. 2026PA-TCNet: Pathology-Aware Temporal Calibration with Physiology-Guided Target Refinement for Cross-Subject Motor Imagery EEG Decoding in Stroke Patientscited by 1
  7. 2026CFSPMNet: Cross-subject Fourier-guided Spatial-Patch Mamba Network for EEG Motor Imagery Decoding in Stroke Patientscited by 1
  8. 2026STAMBRIDGE: Spectral-Temporal Amplitude-aware Mid-Feature Bridge for EEG Visual Decodingcited by 1
  9. 2025A Graph Neural Network based on a Functional Topology Model: Unveiling the Dynamic Mechanisms of Non-Suicidal Self-Injury in Single-Channel EEGcited by 0
  10. 2026Region-aware Spatiotemporal Modeling with Collaborative Domain Generalization for Cross-Subject EEG Emotion Recognitioncited by 0
  11. 2026TGSD: Topology-Guided State-Space Diffusion Framework for EEG Spatial Super-Resolutioncited by 0
  12. 2026SUP-MCRL: Subject-aware Unified Pseudo-feature Coded Multimodal Contrastive Representation Learning for EEG Visual Decodingcited by 0
1 more
  1. 2026Virtual iEEG from Scalp EEG: Charting the Landscape of Source Imaging, Intracranial Inference and Reconstructioncited by 0