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EEG-Based Emotion Recognition Using an Improved Weighted Horizontal Visibility Graph
College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China; Key Laboratory of Underwater Acoustic Signal Processing, Ministry of Education, Southeast University, Nanjing 210096, China.
College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China; Key Laboratory of Underwater Acoustic Signal Processing, Ministry of Education, Southeast University, Nanjing 210096, China.
College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China; Key Laboratory of Underwater Acoustic Signal Processing, Ministry of Education, Southeast University, Nanjing 210096, China.
College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China; Key Laboratory of Underwater Acoustic Signal Processing, Ministry of Education, Southeast University, Nanjing 210096, China.
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2021 (Engelska)Ingår i: Sensors, E-ISSN 1424-8220, Vol. 21, nr 5, artikel-id 1870Artikel i tidskrift (Refereegranskat) Published
Abstract [en]

Emotion recognition, as a challenging and active research area, has received considerable awareness in recent years. In this study, an attempt was made to extract complex network features from electroencephalogram (EEG) signals for emotion recognition. We proposed a novel method of constructing forward weighted horizontal visibility graphs (FWHVG) and backward weighted horizontal visibility graphs (BWHVG) based on angle measurement. The two types of complex networks were used to extract network features. Then, the two feature matrices were fused into a single feature matrix to classify EEG signals. The average emotion recognition accuracies based on complex network features of proposed method in the valence and arousal dimension were 97.53% and 97.75%. The proposed method achieved classification accuracies of 98.12% and 98.06% for valence and arousal when combined with time-domain features.

Ort, förlag, år, upplaga, sidor
MDPI, 2021. Vol. 21, nr 5, artikel-id 1870
Nationell ämneskategori
Datavetenskap (datalogi)
Identifikatorer
URN: urn:nbn:se:mau:diva-41571DOI: 10.3390/s21051870ISI: 000628551400001PubMedID: 33800116Scopus ID: 2-s2.0-85102087461OAI: oai:DiVA.org:mau-41571DiVA, id: diva2:1541950
Tillgänglig från: 2021-04-06 Skapad: 2021-04-06 Senast uppdaterad: 2023-10-05Bibliografiskt granskad

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