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An EEG Database and Its Initial Benchmark Emotion Classification Performance
المؤلفون المشاركون
Krejcar, Ondrej
Reddy, Puthi Prem Nivesh
Chaithanya, Pingali
Meghana, Arramada
Jahnavi, Kamireddy
Hudak, Radovan
Seal, Ayan
المصدر
Computational and Mathematical Methods in Medicine
العدد
المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-14، 14ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2020-08-03
دولة النشر
مصر
عدد الصفحات
14
التخصصات الرئيسية
الملخص EN
Human emotion recognition has been a major field of research in the last decades owing to its noteworthy academic and industrial applications.
However, most of the state-of-the-art methods identified emotions after analyzing facial images.
Emotion recognition using electroencephalogram (EEG) signals has got less attention.
However, the advantage of using EEG signals is that it can capture real emotion.
However, very few EEG signals databases are publicly available for affective computing.
In this work, we present a database consisting of EEG signals of 44 volunteers.
Twenty-three out of forty-four are females.
A 32 channels CLARITY EEG traveler sensor is used to record four emotional states namely, happy, fear, sad, and neutral of subjects by showing 12 videos.
So, 3 video files are devoted to each emotion.
Participants are mapped with the emotion that they had felt after watching each video.
The recorded EEG signals are considered further to classify four types of emotions based on discrete wavelet transform and extreme learning machine (ELM) for reporting the initial benchmark classification performance.
The ELM algorithm is used for channel selection followed by subband selection.
The proposed method performs the best when features are captured from the gamma subband of the FP1-F7 channel with 94.72% accuracy.
The presented database would be available to the researchers for affective recognition applications.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Seal, Ayan& Reddy, Puthi Prem Nivesh& Chaithanya, Pingali& Meghana, Arramada& Jahnavi, Kamireddy& Krejcar, Ondrej…[et al.]. 2020. An EEG Database and Its Initial Benchmark Emotion Classification Performance. Computational and Mathematical Methods in Medicine،Vol. 2020, no. 2020, pp.1-14.
https://search.emarefa.net/detail/BIM-1139608
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Seal, Ayan…[et al.]. An EEG Database and Its Initial Benchmark Emotion Classification Performance. Computational and Mathematical Methods in Medicine No. 2020 (2020), pp.1-14.
https://search.emarefa.net/detail/BIM-1139608
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Seal, Ayan& Reddy, Puthi Prem Nivesh& Chaithanya, Pingali& Meghana, Arramada& Jahnavi, Kamireddy& Krejcar, Ondrej…[et al.]. An EEG Database and Its Initial Benchmark Emotion Classification Performance. Computational and Mathematical Methods in Medicine. 2020. Vol. 2020, no. 2020, pp.1-14.
https://search.emarefa.net/detail/BIM-1139608
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1139608
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
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