Using AI-Based Classification Techniques to Process EEG Data Collected during the Visual Short-Term Memory Assessment
المؤلفون المشاركون
Arsic, S.
Antonijevic, Milos
Zivkovic, Miodrag
Jevremovic, Aleksandar
المصدر
العدد
المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-12، 12ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2020-03-09
دولة النشر
مصر
عدد الصفحات
12
التخصصات الرئيسية
الملخص EN
Visual short-term memory (VSTM) is defined as the ability to remember a small amount of visual information, such as colors and shapes, during a short period of time.
VSTM is a part of short-term memory, which can hold information up to 30 seconds.
In this paper, we present the results of research where we classified the data gathered by using an electroencephalogram (EEG) during a VSTM experiment.
The experiment was performed with 12 participants that were required to remember as many details as possible from the two images, displayed for 1 minute.
The first assessment was done in an isolated environment, while the second assessment was done in front of the other participants, in order to increase the stress of the examinee.
The classification of the EEG data was done by using four algorithms: Naive Bayes, support vector, KNN, and random forest.
The results obtained show that AI-based classification could be successfully used in the proposed way, since we were able to correctly classify the order of the images presented 90.12% of the time and type of the displayed image 90.51% of the time.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Antonijevic, Milos& Zivkovic, Miodrag& Arsic, S.& Jevremovic, Aleksandar. 2020. Using AI-Based Classification Techniques to Process EEG Data Collected during the Visual Short-Term Memory Assessment. Journal of Sensors،Vol. 2020, no. 2020, pp.1-12.
https://search.emarefa.net/detail/BIM-1190559
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Antonijevic, Milos…[et al.]. Using AI-Based Classification Techniques to Process EEG Data Collected during the Visual Short-Term Memory Assessment. Journal of Sensors No. 2020 (2020), pp.1-12.
https://search.emarefa.net/detail/BIM-1190559
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Antonijevic, Milos& Zivkovic, Miodrag& Arsic, S.& Jevremovic, Aleksandar. Using AI-Based Classification Techniques to Process EEG Data Collected during the Visual Short-Term Memory Assessment. Journal of Sensors. 2020. Vol. 2020, no. 2020, pp.1-12.
https://search.emarefa.net/detail/BIM-1190559
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1190559
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر