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Cited by 12EEG and Brain-Computer InterfacesMuscle activation and electromyography studiesEmotion and Mood Recognition

The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.

Cross-Subject EEG Signal Classification with Deep Neural Networks Applied to Motor Imagery

Riyad, Mouad, Khalil, Mohammed, Adib, Abdellah·2019·Lecture Notes in Computer Science
DOI 10.1007/978-3-030-22885-9_12
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Research works printed but not linked: 23

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Metadata

Authors
Riyad, M., Khalil, M., & Adib, A.
Corresponding author
Riyad, Mouad
ORCID
Venue
Lecture Notes in Computer Science
Publisher
Springer International Publishing
Published
2019
Pages
124-139
ISSN
  • 0302-9743 (ISSN-L)
  • 1611-3349
DOI
10.1007/978-3-030-22885-9_12
OpenAlex
W2951451556
Cited by
12
References
5
Citations by year
2024: 2 · 2023: 3 · 2022: 1 · 2021: 2 · 2020: 4
FWCI
3.02
Type
conference paper
Language
en
Open access
closed
Licence
www.springer.com/tdm (tdm)
Locations
Lecture notes in computer science (publishedVersion)
Topics
  • Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
  • Physical Sciences › Engineering › Biomedical Engineering › Muscle activation and electromyography studies
  • Physical Sciences › Computer Science › Artificial Intelligence › Emotion and Mood Recognition
Keywords
motor imagery, sensorimotor rhythm, EEG classification, cross-subject classification, brain-computer interface, deep neural network, temporal convolution, spatial convolution, recurrent layers, dropout regularization, EEGNet

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CoChat. (2026). Citation check: Cross-Subject EEG Signal Classification with Deep Neural Networks Applied to Motor Imagery. https://app.cochat.ai/papers/10.1007/978-3-030-22885-9_12
The paper
Riyad, M., Khalil, M., & Adib, A. (2019). Cross-Subject EEG Signal Classification with Deep Neural Networks Applied to Motor Imagery. Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-030-22885-9_12