Cited by 1,182EEG and Brain-Computer InterfacesEmotion and Mood RecognitionMachine Fault Diagnosis Techniques

EEG signal classification using wavelet feature extraction and a mixture of expert model.

SUBASI, A·2007·Expert Systems with Applications

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Crossref · 10.1016/j.eswa.2006.02.005

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1 of 1 references verified0 citation errors · 0 not checkable

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  • Feature matrix input can be extracted by wavelet, entropy, STFT, power spectral density, and so on.

    “Another form of input is the feature matrix that could be extracted by wavelet, entropy, STFT, power spectral density, and so on (Subasi, 2007; Filippo et al., 2009; Seitsonen et al., 2010; Lu et al., 2017; Lashgari et al., 2020).”

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Authors
SUBASI, A.
Venue
Expert Systems with Applications
Publisher
Elsevier BV
Published
2007-05
Volume
32
Issue
4
Pages
1084-1093
ISSN
0957-4174 (ISSN-L)
OpenAlex
W2081895431
Cited by
1,182
References
1
Citations by year
2026: 50 · 2025: 49 · 2024: 56 · 2023: 53 · 2022: 66 · 2021: 82 · 2020: 101 · 2019: 90 · 2018: 81 · 2017: 98 · 2016: 79 · 2015: 67 · 2014: 68 · 2013: 50 · 2012: 53
FWCI
7.73
Type
journal article
Language
en
Open access
closed
Locations
Expert Systems with Applications (publishedVersion)
Topics
  • Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
  • Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
  • Physical Sciences › Engineering › Control and Systems Engineering › Machine Fault Diagnosis Techniques
Keywords
EEG classification, seizure detection, mixture of experts, discrete wavelet transform, wavelet feature extraction, expectation-maximization algorithm, gating function, epilepsy diagnosis, epileptiform discharges

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