Deep Learning Based on CNN for Emotion Recognition Using EEG Signal

Simple item page
dc.contributor.authorAhmad, Isah Salim
dc.contributor.authorZhang, Shuai
dc.contributor.authorSaminu, Sani
dc.contributor.authorWANG, LINGYUE
dc.contributor.authorIsselmou, Abd El Kader
dc.contributor.authorCAI, ZILIANG
dc.contributor.authorJavaid, Imran
dc.contributor.authorKAMHI, SOUHA
dc.contributor.authorKULSUM, UMMAY
dc.date.accessioned2022-01-10T11:22:48Z
dc.date.available2022-01-10T11:22:48Z
dc.date.issued2021-04-14
dc.description.abstractEmotion recognition based on brain-computer interface (BCI) has attracted important research attention despite its difficulty. It plays a vital role in human cognition and helps in making the decision. Many researchers use electroencephalograms (EEG) signals to study emotion because of its easy and convenient. Deep learning has been employed for the emotion recognition system. It recognizes emotion into single or multi-models, with visual or music stimuli shown on a screen. In this article, the convolutional neural network (CNN) model is introduced to simultaneously learn the feature and recognize the emotion of positive, neutral, and negative states of pure EEG signals single model based on the SJTU emotion EEG dataset (SEED) with ResNet50 and Adam optimizer. The dataset is shuffle, divided into training and testing, and then fed to the CNN model. The negative emotion has the highest accuracy of 94.86% fellow by neutral emotion with 94.29% and positive emotion with 93.25% respectively. With average accuracy of 94.13%. The results showed excellent classification ability of the model and can improve emotion recognition.en_US
dc.identifier.issn2224-3488
dc.identifier.urihttps://uilspace.unilorin.edu.ng/handle/20.500.12484/7304
dc.language.isoenen_US
dc.publisherWSEASen_US
dc.subjectBCIen_US
dc.subjectEEGen_US
dc.subjectDeep learning (DL)en_US
dc.subjectCNNen_US
dc.subjectResNet50en_US
dc.subjectEmotion recognitionen_US
dc.subjectAdam optimizeren_US
dc.subjectSEEDen_US
dc.titleDeep Learning Based on CNN for Emotion Recognition Using EEG Signalen_US
dc.typeArticleen_US

Files

Original bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
20J_ISA_WSEAS_TSP2021_Deep Learning based on CNN for Emotion recognition using EEG.pdf
Size:
1.97 MB
Format:
Adobe Portable Document Format
Description:
Main article
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections