Open AccessDeep Learning Based on CNN for Emotion Recognition Using EEG Signal
Author(s) -
Isah Salim Ahmad,
Shuai Zhang,
Sani Saminu,
Lingyue Wang,
Isselmou Abd El Kader,
Ziliang Cai,
Imran Javaid,
Souha Kamhi,
Ummay Kulsum
Publication year - 2021
Publication title -
wseas transactions on signal processing
Language(s) - English
Resource type - Journals
eISSN - 2224-3488
pISSN - 1790-5052
DOI - 10.37394/232014.2021.17.4
Subject(s) - electroencephalography , convolutional neural network , emotion recognition , computer science , emotion classification , artificial intelligence , feature (linguistics) , speech recognition , deep learning , brain–computer interface , pattern recognition (psychology) , cognitive psychology , psychology , neuroscience , linguistics , philosophy
Emotion 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.