Application and Development of EEG Acquisition and Feedback Technology: A Review

• Published in: Biosensors (Basel) Vol. 13; no. 10; p. 930
• Main Authors: Qin, Yong, Zhang, Yanpeng, Zhang, Yan, Liu, Sheng, Guo, Xiaogang
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.10.2023; MDPI
• Subjects: Accuracy; acquisition devices; Algorithms; Brain; Brain injury; Brain research; Classification; classification algorithm; Cognition; Cognitive ability; Cognitive tasks; Computer applications; Control systems; Data capture devices; Design; Design and construction; EEG; Electrodes; Electroencephalography; Emotional factors; Epilepsy; Equipment and supplies; Feedback; Feedback control systems; Head injuries; Health aspects; Human-computer interface; Implants; Medical innovations; Neurosciences; Physiology; Review; Scandals; Signal classification; Signal processing; Sleep disorders; China
• ISSN: 2079-6374; 2079-6374
• DOI: 10.3390/bios13100930

This review focuses on electroencephalogram (EEG) acquisition and feedback technology and its core elements, including the composition and principles of the acquisition devices, a wide range of applications, and commonly used EEG signal classification algorithms. First, we describe the construction of EEG acquisition and feedback devices encompassing EEG electrodes, signal processing, and control and feedback systems, which collaborate to measure faint EEG signals from the scalp, convert them into interpretable data, and accomplish practical applications using control feedback systems. Subsequently, we examine the diverse applications of EEG acquisition and feedback across various domains. In the medical field, EEG signals are employed for epilepsy diagnosis, brain injury monitoring, and sleep disorder research. EEG acquisition has revealed associations between brain functionality, cognition, and emotions, providing essential insights for psychologists and neuroscientists. Brain–computer interface technology utilizes EEG signals for human–computer interaction, driving innovation in the medical, engineering, and rehabilitation domains. Finally, we introduce commonly used EEG signal classification algorithms. These classification tasks can identify different cognitive states, emotional states, brain disorders, and brain–computer interface control and promote further development and application of EEG technology. In conclusion, EEG acquisition technology can deepen the understanding of EEG signals while simultaneously promoting developments across multiple domains, such as medicine, science, and engineering.