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.
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http://dx.doi.org/10.3390/bios13100930 | DOI Listing |
Bioengineering (Basel)
December 2024
Faculty of Electronics, Communication and Computers, Pitești University Center, National University of Science and Technology POLITEHNICA Bucharest, 110040 Pitesti, Romania.
Anxiety is a widespread mental health issue, and binaural beats have been explored as a potential non-invasive treatment. EEG data reveal changes in neural oscillation and connectivity linked to anxiety reduction; however, harmonics introduced during signal acquisition and processing often distort these findings. Existing methods struggle to effectively reduce harmonics and capture the fine-grained temporal dynamics of EEG signals, leading to inaccurate feature extraction.
View Article and Find Full Text PDFHum Brain Mapp
January 2025
FinnBrain Birth Cohort Study, Turku Brain and Mind Center, Department of Clinical Medicine, University of Turku, Turku, Finland.
The brain develops most rapidly during pregnancy and early neonatal months. While prior electrophysiological studies have shown that aperiodic brain activity undergoes changes across infancy to adulthood, the role of gestational duration in aperiodic and periodic activity remains unknown. In this study, we aimed to bridge this gap by examining the associations between gestational duration and aperiodic and periodic activity in the EEG power spectrum in both neonates and toddlers.
View Article and Find Full Text PDFeNeuro
January 2025
Neurophysiology of Everyday Life Group, Department of Psychology, Carl von Ossietzky Universität Oldenburg, Oldenburg, Germany
A comprehensive analysis of everyday sound perception can be achieved using Electroencephalography (EEG) with the concurrent acquisition of information about the environment. While extensive research has been dedicated to speech perception, the complexities of auditory perception within everyday environments, specifically the types of information and the key features to extract, remain less explored. Our study aims to systematically investigate the relevance of different feature categories: discrete sound-identity markers, general cognitive state information, and acoustic representations, including discrete sound onset, the envelope, and mel-spectrogram.
View Article and Find Full Text PDFClin EEG Neurosci
December 2024
Behavioral and Social Neuroscience Research Group, CEITEC - Central European Institute of Technology, Masaryk University, Brno, Czech Republic.
Vagal nerve stimulation (VNS) is a therapeutical option for the treatment of drug-resistant epileptic patients. The response to VNS varies from patient to patient and is difficult to predict. The proposed study is based on our previous work, identifying relative mean power in pre-implantation EEG as a reliable marker for VNS efficacy prediction in adult patients.
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