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

2024-07-20

arXiv.org

AI Summary

This paper addresses critical challenges hindering the practical deployment of single-channel electroencephalography (EEG): the absence of standardized acquisition protocols, fragmented methodological approaches, and persistent performance limitations. We present the first comprehensive, end-to-end systematic review spanning the entire technical stack—unifying definitions of monopolar and bipolar configurations, cataloging mainstream hardware platforms (e.g., Muse, OpenBCI), benchmarking publicly available datasets (e.g., SEED-IV, Sleep-EDF), and analyzing signal processing paradigms (time-frequency analysis, deep learning, transfer learning, and generative modeling). Crucially, we propose a novel AI-driven framework for synthetic single-channel EEG generation and empirically demonstrate its capacity to match—or even surpass—the performance of conventional multi-channel systems. Our work establishes the first structured, taxonomy-based review framework for single-channel EEG, providing both theoretical foundations and actionable guidelines to accelerate the scalable adoption of low-cost brain–computer interfaces in consumer applications and clinical screening.

Technology Category

• Humans and AI: Brain-Sensing and Analysis
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language

Application Category

• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• User Modeling, Personalization and Recommendation: Practical large-scale studies of user experience

Abstract

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.

Problem

• Review single-channel EEG devices and datasets.
• Explore signal processing methods and applications.
• Discuss future directions and AI-based advancements.

Innovation

• Single-channel EEG for brain activity monitoring
• AI-based EEG generation techniques advancement
• Applications in sleep, emotion, education, diagnosis