Name
Abstract | ||
|---|---|---|
Recognition of user felt emotion is an exciting field because visual, verbal and facial communications can be falsified more easily than 'inner' emotions. Non-invasive EEG-based human emotion recognition entails the classification of discrete emotions using EEG data. These emotions can be defined by the arousal-valence dimensions. We performed real-time emotion classification for four categories of emotional states, namely: pleasant, sad, happy and frustrated. Higuchi's Fractal Dimension was applied on EEG data and used as a feature extraction method and Support Vector Machine was used for classification. This paper documents a comparative study of classification accuracy achieved by collecting raw EEG data from 3 electrode locations vs. collection from 8 electrode locations. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1007/978-3-319-26561-2_22 | Lecture Notes in Computer Science |
Keywords | Field | DocType |
EEG,Emotion recognition,Arousal-valence,SVM | Pattern recognition,Fractal dimension,Emotion recognition,Computer science,Support vector machine,Emotion classification,Feature extraction,Artificial intelligence,Eeg data,Electroencephalography | Conference |
Volume | ISSN | Citations |
9492 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mair Muteeb Javaid | 1 | 0 | 0.34 |
| Muhammad Yousaf | 2 | 81 | 15.98 |
| Quratulain Zahid Sheikh | 3 | 0 | 0.34 |
| Mian Awais | 4 | 59 | 11.53 |
| Sameera Saleem | 5 | 0 | 0.34 |
| Maryam Khalid | 6 | 0 | 0.68 |