Results 11 to 20 of about 816,396 (376)
EEG Based Emotion Recognition: A Tutorial and Review [PDF]
ACM Computing Surveys, 2022 Emotion recognition technology through analyzing the EEG signal is currently an essential concept in Artificial Intelligence and holds great potential in emotional health care, human-computer interaction, multimedia content recommendation, etc.
Xiang Li +8 more
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EEG Conformer: Convolutional Transformer for EEG Decoding and Visualization
IEEE transactions on neural systems and rehabilitation engineering, 2022 Due to the limited perceptual field, convolutional neural networks (CNN) only extract local temporal features and may fail to capture long-term dependencies for EEG decoding.
Yonghao Song +3 more
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EEGNet: a compact convolutional neural network for EEG-based brain–computer interfaces [PDF]
Journal of Neural Engineering, 2016 Objective. Brain–computer interfaces (BCI) enable direct communication with a computer, using neural activity as the control signal. This neural signal is generally chosen from a variety of well-studied electroencephalogram (EEG) signals. For a given BCI
Vernon J. Lawhern +5 more
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Neural computing & applications (Print), 2022 Affective computing, a subcategory of artificial intelligence, detects, processes, interprets, and mimics human emotions. Thanks to the continued advancement of portable non-invasive human sensor technologies, like brain–computer interfaces (BCI ...
Essam H. Houssein +2 more
semanticscholar +1 more source
Dramaturgies of reality – shaping and being shaped by things
Nordic Journal of Art and Research, 2021 New dramaturgy expands beyond the theatre and stage, working on the ways in which things in each time and space are organised and produce meaning. I link this to object-oriented ontology (Morton, 2013; 2016; 2018) and the ethics of relating to things ...
Camilla Eeg-Tverbakk
doaj +1 more source
Computational Intelligence and Neuroscience, 2010 This paper describes FieldTrip, an open source software package that we developed for the analysis of MEG, EEG, and other electrophysiological data.
R. Oostenveld +3 more
semanticscholar +1 more source
BENDR: Using Transformers and a Contrastive Self-Supervised Learning Task to Learn From Massive Amounts of EEG Data [PDF]
Frontiers in Human Neuroscience, 2021 Deep neural networks (DNNs) used for brain–computer interface (BCI) classification are commonly expected to learn general features when trained across a variety of contexts, such that these features could be fine-tuned to specific contexts.
Demetres Kostas +2 more
semanticscholar +1 more source
Energies, 2021 In this work, the main research question is how a high penetration of energy communities (ECs) affects the national electricity demand in the residential sector.
Sebastian Zwickl-Bernhard, Hans Auer
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Energies, 2021 Battery-powered electric mobility is currently the most promising technology for the decarbonisation of the transport sector, alongside hydrogen-powered vehicles, provided that the electricity used comes 100% from renewable energy sources.
Albert Hiesl +2 more
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DeepSleepNet: A Model for Automatic Sleep Stage Scoring Based on Raw Single-Channel EEG [PDF]
IEEE transactions on neural systems and rehabilitation engineering, 2017 This paper proposes a deep learning model, named DeepSleepNet, for automatic sleep stage scoring based on raw single-channel EEG. Most of the existing methods rely on hand-engineered features, which require prior knowledge of sleep analysis.
A. Supratak, Hao Dong, Chao Wu, Yike Guo
semanticscholar +1 more source