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.

Review and Classification of Emotion Recognition Based on EEG Brain-Computer Interface System Research: A Systematic Review

“…Moreover, finding key emotional states to be recognized is mandatory; for example, six emotions were detected in [42,45,106,120,122,192], whereas in [108], a real-time EEG signal to classify happy and unhappy emotions was proposed, and in [113], a fear evaluation system was proposed. In our review, we found that most of the articles aim to detect unpleasant, pleasant, and neutral emotions, such as in [105,217], or positive, negative, and neutral emotions that are based on the valence-arousal dimensional emotion model, as in [159,206].…”

“…These proposed systems aim to explore or improve EEG-based emotion recognition systems. [2,39,41,42,49,50,57,61,63,92,104,108,109,117,131,136,149,152,157,173,174,185,186,189,191,[195][196][197][198][199][200][201][202][203][204][205][206][207][208][209]217,219,[223][224][225]229,[262][263][264][265][266]<...>…”

MUSIC-Expected maximization gaussian mixture methodology for clustering and detection of task-related neuronal firing rates

“…This methodology is general and could be used in any situation where multiple neurons are recorded, whether there be a single type of trial or multiple trial types, as considered here. For example, this application could be used in the study of motor commands for gaze control (Takemura et al, 2001), neuronal oscillators (Schwemmer and Lewis, 2011; Geertsema et al, 2015; Aydin et al, 2016) or other brain studies such as deep brain stimulation (Florin et al, 2013). Further, it could be used for research in brain-computer interfaces (Carmena et al, 2003;Ortiz-Rosario and Adeli, 2013).…”

Geometry based three-dimensional image processing method for electronic cluster eye

“…and the FOV θ is In our eCley system, the pixel pitch is 3.2µm; the channel image has a size of 55 × 55 pixels; and the focal length f is 778µm. According to (2) and 3, the angular resolution ∆θ =0.235 • and the FOV θ =6.345 • are obtained.…”

“…Some recently proposed examples include inspiration by music [41,40], physics [43,42,44], swarm behavior [34,37,36]. Artificial Intelligence is often integrated within robot controllers [45,28] and combined to neural systems for Machine Intelligence [19,2,13].…”