Learning-based classification of valence emotion from electroencephalography.
The neuroimaging research field has been revolutionized with the development of human cognitive functions without the use of brain pathways. To assist such systems, electroencephalography (EEG) based measures play an important role. In this study, the publicly available database of emotion analysis using physiological signals, has been used to identify the human emotions such as valence (positive/negative) from the given recorded EEG signals. With the identification of such emotion, the feeling of goodness or badness related individual experiences with the situation can be identified from his/her brain signals. The different machine learning classifiers such as random forest, decisions trees, K-nearest neighbor, support vector machines, naive Bayes and neural network have been used to identify and evaluate such emotions. The previous work done by the other authors on the same dataset using various quantitative approaches are compared with the approaches used in this study yields higher accuracy rates with the random forest and decision tree. The effectiveness of each classifier in terms of statistical measures such as accuracy, F-score, etc. has been evaluated. The random forest classifier was found to outperform with an accuracy of 98%, closely followed by the Decision tree at 94% are the most effective classifiers in classifying the valence emotions of the EEG data for 6 subjects.
['Ramzan|Munaza|M|', 'Dawn|Suma|S|']
D000328:Adult D001921:Brain D058256:Brain Waves D066264:Datasets as Topic D004569:Electroencephalography D004644:Emotions D006801:Humans D000069550:Machine Learning D008962:Models, Theoretical D016571:Neural Networks, Computer D021641:Recognition, Psychology D012815:Signal Processing, Computer-Assisted D055815:Young Adult
Electroencephalography database of emotion analysis using physiological signals random forest decisions trees support vector machines naive Bayes neural network -nearest neighbor brain–computer interface