Emotion Recognition with Eigen Features of Frequency Band Activities Embedded in Induced Brain Oscillations Mediated by Affective Pictures
| Autor: | Kahraman Ates, Serap Aydin, Serdar Demirtaş, M. Alper Tunga |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Adult
medicine.medical_specialty Computer Networks and Communications Frequency band Emotions Wavelet Analysis 02 engineering and technology Neuropsychological Tests Electroencephalography Audiology Brain mapping Young Adult 03 medical and health sciences 0302 clinical medicine Wavelet decomposition 0202 electrical engineering electronic engineering information engineering medicine Data Mining Humans Emotion recognition International Affective Picture System Brain Mapping Principal Component Analysis medicine.diagnostic_test business.industry Emotional stimuli Brain Cognition General Medicine Middle Aged Brain Waves 020201 artificial intelligence & image processing Artificial intelligence business Psychology Facial Recognition Photic Stimulation 030217 neurology & neurosurgery |
| Zdroj: | International Journal of Neural Systems. 26:1650013 |
| ISSN: | 1793-6462 0129-0657 |
| DOI: | 10.1142/s0129065716500131 |
| Popis: | In this study, singular spectrum analysis (SSA) has been used for the first time in order to extract emotional features from well-defined electroencephalography (EEG) frequency band activities (BAs) so-called delta (0.5–4[Formula: see text]Hz), theta (4–8[Formula: see text]Hz), alpha (8–16[Formula: see text]Hz), beta (16–32[Formula: see text]Hz), gamma (32–64[Formula: see text]Hz). These five BAs were estimated by applying sixth-level multi-resolution wavelet decomposition (MRWD) with Daubechies wavelets (db-8) to single channel nonaveraged emotional EEG oscillations of 6 s for each scalp location over 16 recording sites (Fp1, Fp2, F3, F4, F7, F8, C3, C4, P3, P4, T3, T4, T5, T6, O1, O2). Every trial was mediated by different emotional stimuli which were selected from international affective picture system (IAPS) to induce emotional states such as pleasant (P), neutral (N), and unpleasant (UP). Largest principal components (PCs) of BAs were considered as emotional features and data mining approaches were used for the first time in order to classify both three different (P, N, UP) and two contrasting (P and UP) emotional states for 30 healthy controls. Emotional features extracted from gamma BAs (GBAs) for 16 recording sites provided the high classification accuracies of 87.1% and 100% for classification of three emotional states and two contrasting emotional states, respectively. In conclusion, we found the followings: (1) Eigenspectra of high frequency BAs in EEG are highly sensitive to emotional hemispheric activations, (2) emotional states are mostly mediated by GBA, (3) pleasant pictures induce the higher cortical activation in contrast to unpleasant pictures, (4) contrasting emotions induce opposite cortical activations, (5) cognitive activities are necessary for an emotion to occur. |
| Databáze: | OpenAIRE |
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