Using AI-Based Classification Techniques to Process EEG Data Collected during the Visual Short-Term Memory Assessment
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 wh...
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doaj-c4d2e6efd6d040669c827caeb2b278192020-11-25T02:50:25ZengHindawi LimitedJournal of Sensors1687-725X1687-72682020-01-01202010.1155/2020/87678658767865Using AI-Based Classification Techniques to Process EEG Data Collected during the Visual Short-Term Memory AssessmentMilos Antonijevic0Miodrag Zivkovic1Sladjana Arsic2Aleksandar Jevremovic3Informatics and Computing Department, Singidunum University, Belgrade, SerbiaInformatics and Computing Department, Singidunum University, Belgrade, SerbiaDepartment Cupria, Academy of Educational Medical Professional Studies, Krusevac, SerbiaInformatics and Computing Department, Singidunum University, Belgrade, SerbiaVisual 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.http://dx.doi.org/10.1155/2020/8767865 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Milos Antonijevic Miodrag Zivkovic Sladjana Arsic Aleksandar Jevremovic |
spellingShingle |
Milos Antonijevic Miodrag Zivkovic Sladjana Arsic Aleksandar Jevremovic Using AI-Based Classification Techniques to Process EEG Data Collected during the Visual Short-Term Memory Assessment Journal of Sensors |
author_facet |
Milos Antonijevic Miodrag Zivkovic Sladjana Arsic Aleksandar Jevremovic |
author_sort |
Milos Antonijevic |
title |
Using AI-Based Classification Techniques to Process EEG Data Collected during the Visual Short-Term Memory Assessment |
title_short |
Using AI-Based Classification Techniques to Process EEG Data Collected during the Visual Short-Term Memory Assessment |
title_full |
Using AI-Based Classification Techniques to Process EEG Data Collected during the Visual Short-Term Memory Assessment |
title_fullStr |
Using AI-Based Classification Techniques to Process EEG Data Collected during the Visual Short-Term Memory Assessment |
title_full_unstemmed |
Using AI-Based Classification Techniques to Process EEG Data Collected during the Visual Short-Term Memory Assessment |
title_sort |
using ai-based classification techniques to process eeg data collected during the visual short-term memory assessment |
publisher |
Hindawi Limited |
series |
Journal of Sensors |
issn |
1687-725X 1687-7268 |
publishDate |
2020-01-01 |
description |
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. |
url |
http://dx.doi.org/10.1155/2020/8767865 |
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