In 1969 Zetterberg presented a method for describing the spectral properties of an EEG signal, starting from the assumption that the signal is essentially stationary during the analysis epoch. The method involves determination of the parameters for a model consisting of a lenear filter with the ability to produce a signal with the same spectral properties as the EEG signal. Zetterberg and Ahlin described in 1975 an analogue simulator based on this model theory. With this simulator it is possible to reproduce practically all types of stationary, as well as a number of non-stationary, EEG signals. We have used this simulator to demonstrate in an illustrative manner the relation between the properties of a signal in the spectral domain and in the time domain. We have also endeavoured to anser the question: if one starts from the frequency spectrum, what does the EEG signal corresponding to this spectrum look like? We also draw attention to the usefulness of the simulator in connection with training and as an instrument for testing computer and other systems of EEG analysis.
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http://dx.doi.org/10.1016/0013-4694(75)90094-2 | DOI Listing |
Cancer is a condition in which cells in the body grow uncontrollably, often forming tumours and potentially spreading to various areas of the body. Cancer is a hazardous medical case in medical history analysis. Every year, many people die of cancer at an early stage.
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January 2025
Interdisciplinary Research Center for Finance and Digital Economy, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia.
Marketing plays a vital role in the success of a business, driving customer engagement, brand recognition, and revenue growth. Neuromarketing adds depth to this by employing insights into consumer behavior through brain activity and emotional responses to create more effective marketing strategies. Electroencephalogram (EEG) has typically been utilized by researchers for neuromarketing, whereas Eye Tracking (ET) has remained unexplored.
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January 2025
School of Data Science, Lingnan University, Hong Kong SAR, China.
Accurate monitoring of drowsy driving through electroencephalography (EEG) can effectively reduce traffic accidents. Developing a calibration-free drowsiness detection system with single-channel EEG alone is very challenging due to the non-stationarity of EEG signals, the heterogeneity among different individuals, and the relatively parsimonious compared to multi-channel EEG. Although deep learning-based approaches can effectively decode EEG signals, most deep learning models lack interpretability due to their black-box nature.
View Article and Find Full Text PDFAnn N Y Acad Sci
January 2025
Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China.
Deep learning has revolutionized electroencephalograph (EEG) decoding, with convolutional neural networks (CNNs) being a predominant tool. However, CNNs struggle with long-term dependencies in sequential EEG data. Models like long short-term memory and transformers improve performance but still face challenges of computational efficiency and long sequences.
View Article and Find Full Text PDFBMC Pregnancy Childbirth
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Department of Anesthesiology, Zhujiang Hospital, Southern Medical University, Guangzhou, China.
Background: Lack of motivation and behavioral abnormalities are the hallmarks of postpartum depression (PPD). Severe uterine contractions during labor are pain triggers for psychiatric disorders, including PPD in women during the puerperium. Creating biomarkers to monitor PPD may help in its early detection and treatment.
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