title: Emotion Recognition with Eigen Features of Frequency Band Activities Embedded in Induced Brain Oscillations Mediated by Affective Pictures.
abstract: 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 Hz), theta (4-8 Hz), alpha (8-16 Hz), beta (16-32 Hz), gamma (32-64 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.
authors.affiliation: 1 Biomedical Engineering Department, Bahçeşehir University, Beşiktaş Istanbul 34353, Turkey.
authors.forename: Serap
authors.initials: S
authors.lastname: Aydin
authors.affiliation: 2 Department of Biophysics, Gülhane Military Medical Academy, Ankara, Turkey.
authors.forename: Serdar
authors.initials: S
authors.lastname: Demirtaş
authors.affiliation: 2 Department of Biophysics, Gülhane Military Medical Academy, Ankara, Turkey.
authors.forename: Kahraman
authors.initials: K
authors.lastname: Ateş
authors.affiliation: 3 Software Engineering Department, Bahçeşehir University, Beşiktaş Istanbul 34353, Turkey.
authors.forename: M Alper
authors.initials: MA
authors.lastname: Tunga
mesh_terms: D000328:Adult; D001921:Brain / Q000502:physiology*; D001931:Brain Mapping / Q000379:methods; D058256:Brain Waves / Q000502:physiology*; D057225:Data Mining / Q000379:methods; D004569:Electroencephalography / Q000379:methods; D004644:Emotions / Q000502:physiology*; D000066499:Facial Recognition / Q000502:physiology*; D006801:Humans; D008875:Middle Aged; D009483:Neuropsychological Tests; D010775:Photic Stimulation; D025341:Principal Component Analysis; D058067:Wavelet Analysis; D055815:Young Adult
keywords: Brain oscillations; data mining; emotional state; gamma band activity