Convolutional Neural Networks Model for Emotion Recognition Using EEG Signal

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dc.contributor.authorAhmad, Isah Salim
dc.contributor.authorZhang, Shuai
dc.contributor.authorWANG, LINGYUE
dc.contributor.authorSaminu, Sani
dc.contributor.authorIsselmou, Abd El Kader
dc.contributor.authorCAI, ZILIANG
dc.contributor.authorJavaid, Imran
dc.contributor.authorKAMHI, SOUHA
dc.contributor.authorKULSUM, UMMAY
dc.date.accessioned2022-01-10T11:24:42Z
dc.date.available2022-01-10T11:24:42Z
dc.date.issued2021-04-29
dc.description.abstractA Brain-computer interface (BCI) using an electroencephalogram (EEG) signal has a great attraction in emotion recognition studies due to its resistance to humans’ deceptive actions. This is the most significant advantage of brain signals over speech or visual signals in the emotion recognition context. A major challenge in EEG-based emotion recognition is that a lot of effort is required for manually feature extractor, EEG recordings show varying distributions for different people and the same person at different time instances. The Poor generalization ability of the network model as well as low robustness of the recognition system. Improving algorithms and machine learning technology helps researchers to recognize emotion easily. In recent years, deep learning (DL) techniques, specifically convolutional neural networks (CNNs) have made excellent progress in many applications. This study aims to reduce the manual effort on features extraction and improve the EEG signal single model’s emotion recognition using convolutional neural network (CNN) architecture with residue block. The dataset is shuffle, divided into training and testing, and then fed to the model. DEAP dataset has class 1, class 2, class 3, and class 4 for both valence and arousal with an accuracy of 90.69%, 91.21%, 89.66%, 93.64% respectively, with a mean accuracy of 91.3%. 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 a mean accuracy of 94.13% on the SEED dataset. The experimental results indicated that CNN Based on residual networks can achieve an excellent result with high recognition accuracy, which is superior to most recent approaches.en_US
dc.identifier.issn1998-4464
dc.identifier.urihttps://uilspace.unilorin.edu.ng/handle/20.500.12484/7305
dc.language.isoenen_US
dc.publisherNorth Atlantic University Unionen_US
dc.subjectAdam optimizeren_US
dc.subjectBCIen_US
dc.subjectConvolutional neural network (CNN)en_US
dc.subjectDeep learning (DL)en_US
dc.subjectEEGen_US
dc.subjectEmotion recognitionen_US
dc.subjectSingle model emotion recognitionen_US
dc.subjectResidual blocken_US
dc.titleConvolutional Neural Networks Model for Emotion Recognition Using EEG Signalen_US
dc.typeArticleen_US

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