Emo-Gem: An Impacted Affective Emotional Psychology Analysis through Gaussian Model using AMIGOS

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Bakkialakshmi V. S, T. Sudalaimuthu


Objectives: Analysis of human affect (feelings) that directly release on human emotions is mandatory to rival many psychological impacts. Human emotions are more precious and real. The history of Effect Theory implies on the idea of detecting the feelings and emotions seems needful to predict behaviour. The proposed research work is based on predicting the real emotion using a robust model with neurophysiological data. To design a robust affective computing model to determine the human emotions for an enhanced experience of understanding.

Methods: Machine learning-based existing research are helpful in deriving the idea of affect detection, in which the proposed system keeps the statistical analysis as a unique idea, Using AMIGOS Dataset, Gaussian distribution enabled analysis model is developed.

Findings: ECG, EEG signals are highly impacted by noise factors and motion artifacts. In spite of emotion detection, the Similarity of emotional results and multi-modality of results leads to neutral responses. In order to overcome the issue, a cross-modality approach is used here, which make the dual validation of the training data with the test inputs.

Novelty: The presented system utilizes the concept of Gaussian mixture models to create a novel prediction algorithm named the Gaussian expectation maximization technique (GEM) using the AMIGOS dataset. The dataset considered physiological signals such as Electrocardiography (ECG), Electroencephalography (EEG), Galvanic Skin Response (GSR). The statistical response after the processing of the data, measurable results on emotion labels those coincidence responses with training samples directly impacts the obtained results. The presented system is comparatively discussed with a state-of-the-art approach in terms of statistical parameters like standard deviation, the population mean etc. The comparative analysis on various participants and their unique covariate points are extracted for deep emotion analysis. The proposed system achieves the detection of emotional affects such as anger, contempt, disgust, happiness and sadness. Based on various iterative learning with improved expectations and maximization value extraction, the proposed system detects the emotion with minimum iterations of 5 with Mean=0.62, SD=0.88 etc.

Index Terms—Affective computing, Emotion analysis, Subject identification, Machine learning, Artificial intelligence, Emotional Psychology.

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