Additionally, an optimal wavelet filter bank has been developed for automated diagnosis of Alzheimer’s disease and emotions classification using EEG signals, demonstrating its effectiveness for capturing relev
Currently, a large majority of emotion classification studies utilize Electroencephalograms (EEG) and facial expression recognition to perform emotion classification. Fewer studies have been conducted using the ECG and EDG to this end. These physiological signals will be reviewed to compare the ECG and...
35. Pereira, ET, Gomes, HM 2016, The role of data balancing for emotion classification using EEG signals. In: 2016 IEEE International Conference on Digital Signal Processing (DSP), 2016 IEEE: IEEE. pp. 555–9.10.1109/ICDSP.2016.7868619Suche in Google Scholar 36. Wichakam, I, Vateekul, P...
The result showed that, the classification accuracy in two emotion states was 73.25% using the support vector machine (SVM) classifier. The simulations showed that the classification accuracy is good and the proposed methods are effective. During an emotion, the EEG is less complex compared to ...
Moreover, a method for classifying EEG signals based on the state of mind of neural networks was constructed in the study. In addition, the EEG is denoised preprocessed by time-domain regression method, and features of the EEG signal parameters of different criminal psychological tasks are ...
Kumari, N., Anwar, S., Bhattacharjee, V.: Time series-dependent feature of EEG signals for improved visually evoked emotion classification using EmotionCapsNet. Neural Comput. Appl. 34(16), 13291–13303 (2022) Article Google Scholar Li, Y., Fu, B., Li, F., Shi, G., Zheng, W.: ...
In the area of affective computing technology, the classification of emotions can be used for a variety of things such as health, entertainment, education, etc. This study determined the classification of emotions based on EEG (Electroencephalography) signals, which is emotions are classified accordi...
@Article{s21051589, AUTHOR = {Nandi, Arijit and Xhafa, Fatos and Subirats, Laia and Fort, Santi}, TITLE = {Real-Time Emotion Classification Using EEG Data Stream in E-Learning Contexts}, JOURNAL = {Sensors}, VOLUME = {21}, YEAR = {2021}, NUMBER = {5}, ARTICLE-NUMBER = {1589}, ...
Generally, the proposed model consists of three parts: extraction, fusion, and classification of the features. Features from TOPO-FM and HOLO-FM are extracted by deep learning method using separately convolutional neural network per characteristic of the EEG signal. The feature matrix is constructed ...
The SEED-IV dataset, which includes additional emotion labels, also achieved high classification accuracy using the ACTNN method by Gong et al. (2023), with 91.90% accuracy for the four emotion categories. Show abstract ERTNet: an interpretable transformer-based framework for EEG emotion ...