A Tale of Single-channel Electroencephalogram: Devices, Datasets, Signal Processing, Applications, and Future Directions
원문 초록
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.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Robust Emotion Recognition via Bi-Level Self-Supervised Continual Learning
EEG-based Emotion Recognition via Efficient Convolutional Neural Network and Contrastive Learning
EEG-Based Emotion Recognition Using Genetic Algorithm Optimized Multi-Layer Perceptron
FOIT: Fast Online Instance Transfer for Improved EEG Emotion Recognition
eegFloss: A Python package for refining sleep EEG recordings using machine learning models
SLEEPYLAND: trust begins with fair evaluation of automatic sleep staging models
토론
이 논문과 연결된 포럼 글이 아직 없습니다.