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Electroencephalography source localization [PDF]
Clinical and Experimental Pediatrics, 2023 Electroencephalography (EEG) has been and is still widely used in brain function research. EEG has advantages over other neuroimaging modalities. First, it not only directly images the electrical activity of neurons; it has a higher temporal resolution ...
Tae-Hoon Eom
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Handbook of Clinical Neurology, 2020 The electroencephalogram (EEG) was invented almost 100 years ago and is still a method of choice for many research questions, even applications-from functional brain imaging in neuroscientific investigations during movement to real-time applications like brain-computer interfacing. This chapter gives some background information on the establishment and
Gernot R. Müller-Putz
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Continuous heart rate variability and electroencephalography monitoring in severe acute brain injury: a preliminary study [PDF]
Acute and Critical Care, 2021 Background Decreases in heart rate variability have been shown to be associated with poor outcomes in severe acute brain injury. However, it is unknown whether the changes in heart rate variability precede neurological deterioration in such patients.
Hyunjo Lee +2 more
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Open Vocabulary Electroencephalography-To-Text Decoding and Zero-shot Sentiment Classification [PDF]
AAAI Conference on Artificial Intelligence, 2021 State-of-the-art brain-to-text systems have achieved great success in decoding language directly from brain signals using neural networks. However, current approaches are limited to small closed vocabularies which are far from enough for natural ...
Zhenhailong Wang, Heng Ji
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Italian National Conference on Sensors, 2023 The electroencephalography (EEG) signal is a noninvasive and complex signal that has numerous applications in biomedical fields, including sleep and the brain–computer interface.
A. Chaddad +3 more
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Considerate motion imagination classification method using deep learning.
PLoS ONE, 2022 In order to improve the classification accuracy of motion imagination, a considerate motion imagination classification method using deep learning is proposed.
Zhaokun Yan, Xiangquan Yang, Yu Jin
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Associations Between Infant Screen Use, Electroencephalography Markers, and Cognitive Outcomes
JAMA pediatrics, 2023 This cohort study analyzes data for children from the population-based study Growing Up in Singapore Toward Healthy Outcomes (GUSTO) to examine the associations between infant screen time, electroencephalography markers, and school-age cognitive outcomes
Law Ec +14 more
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Deep learning-based electroencephalography analysis: a systematic review [PDF]
Journal of Neural Engineering, 2019 Context. Electroencephalography (EEG) is a complex signal and can require several years of training, as well as advanced signal processing and feature extraction methodologies to be correctly interpreted.
Yannick Roy +5 more
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Electroencephalography-Based Depression Detection Using Multiple Machine Learning Techniques
Diagnostics, 2023 The growth of biomedical engineering has made depression diagnosis via electroencephalography (EEG) a trendy issue. The two significant challenges to this application are EEG signals’ complexity and non-stationarity.
Amel Ksibi +5 more
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The Future of Neuroscience: Flexible and Wireless Implantable Neural Electronics
Advanced Science, 2021 Neurological diseases are a prevalent cause of global mortality and are of growing concern when considering an ageing global population. Traditional treatments are accompanied by serious side effects including repeated treatment sessions, invasive ...
Eve McGlynn +6 more
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