LEDPatNet19: Automated Emotion Recognition Model based on Nonlinear LED Pattern Feature Extraction Function using EEG Signals

Abstract

Electroencephalography (EEG) signals collected from human brains have generally been used to diagnose diseases. Moreover, EEG signals can be used in several areas such as emotion recognition, driving fatigue detection. This work presents a new emotion recognition model by using EEG signals. The primary aim of this model is to present a highly accurate emotion recognition framework by using both a hand-crafted feature generation and a deep classifier. The presented framework uses a multilevel fused feature generation network. This network has three primary phases, which are tunable Q-factor wavelet transform (TQWT), statistical feature generation, and nonlinear textural feature generation phases. TQWT is applied to the EEG data for decomposing signals into different sub-bands and create a multilevel feature generation network. In the nonlinear feature generation, an S-box of the LED block cipher is utilized to create a pattern, which is named as Led-Pattern. Moreover, statistical feature extraction is processed using the widely used statistical moments. The proposed LED pattern and statistical feature extraction functions are applied to 18 TQWT sub-bands and an original EEG signal. Therefore, the proposed hand-crafted learning model is named LEDPatNet19. To select the most informative features, ReliefF and iterative Chi2 (RFIChi2) feature selector is deployed. The proposed model has been developed on the two EEG emotion datasets, which are GAMEEMO and DREAMER datasets. Our proposed hand-crafted learning network achieved 94.58%, 92.86%, and 94.44% classification accuracies for arousal, dominance, and valance cases of the DREAMER dataset. Furthermore, the best classification accuracy of the proposed model for the GAMEEMO dataset is equal to 99.29%. These results clearly illustrate the success of the proposed LEDPatNet19.

Emotional Speaker Verification Using Novel Modified Capsule Neural Network

“…The experimental analysis demonstrated that the accuracy is increased with the use of fuzzy logic. On the other hand, many articles did not use data fuzzification in the preprocessing phase and obtained outstanding classification results which outperformed the state-of-the-art methods such as [24][25][26]. In fact, Zhang et al [24] demonstrated that the experimental results showed the excellent performance of the proposed model in comparison to the existing state-of-the-art models.…”

“…In fact, Zhang et al [24] demonstrated that the experimental results showed the excellent performance of the proposed model in comparison to the existing state-of-the-art models. Furthermore, Tuncer et al [25] stated that the best classification accuracy of the proposed Led-Pattern model named: LEDPatNet19 for the GAMEEMO dataset attained 99.29%. Moreover, Mohebbanaaz et al [26] showed the superior performance of their proposed classifier with classification results equivalent to 98.77%.…”

FF-BTP Model for Novel Sound-Based Community Emotion Detection

“…• Emotion classification, where the real moods of the subject(s) are automatically classified using machine learning techniques [37][38][39], is a growing research field. Many emotion classification models in the literature use various input data: facial images, speech, biophysical signals (e.g., electroencephalography), or functional magnetic resonance images [17,40,41], [42].…”

Deep Neural Network for EEG Signal-Based Subject-Independent Imaginary Mental Task Classification

“…These cross-subject problems with large and complex data can be handled in a much better way by employing deep learning techniques [10,21]. The studies reported in [22][23][24][25][26] quantified EEG features to recognize neurological deteriorations according to the task because of stroke and estimate the biomarkers to differentiate between healthy adults and ischemic stroke patients.…”