Emotion Recognition with Eigen Features of Frequency Band Activities Embedded in Induced Brain Oscillations Mediated by Affective Pictures
| العنوان: | Emotion Recognition with Eigen Features of Frequency Band Activities Embedded in Induced Brain Oscillations Mediated by Affective Pictures |
|---|---|
| المؤلفون: | Kahraman Ates, Serap Aydin, Serdar Demirtaş, M. Alper Tunga |
| المصدر: | International Journal of Neural Systems. 26:1650013 |
| بيانات النشر: | World Scientific Pub Co Pte Lt, 2016. |
| سنة النشر: | 2016 |
| مصطلحات موضوعية: | 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 |
| الوصف: | 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. |
| تدمد: | 1793-6462 0129-0657 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::10e1ebcf96238d30f534a8529c3a4285 https://doi.org/10.1142/s0129065716500131 |
| رقم الأكسشن: | edsair.doi.dedup.....10e1ebcf96238d30f534a8529c3a4285 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 17936462 01290657 |
|---|