Identifying Complex Emotions in Alexithymia Affected Adolescents Using Machine Learning Techniques

Abstract

Many scientific researchers’ study focuses on enhancing automated systems to identify emotions and thus relies on brain signals. This study focuses on how brain wave signals can be used to classify many emotional states of humans. Electroencephalography (EEG)-based affective computing predominantly focuses on emotion classification based on facial expression, speech recognition, and text-based recognition through multimodality stimuli. The proposed work aims to implement a methodology to identify and codify discrete complex emotions such as pleasure and grief in a rare psychological disorder known as alexithymia. This type of disorder is highly elicited in unstable, fragile countries such as South Sudan, Lebanon, and Mauritius. These countries are continuously affected by civil wars and disaster and politically unstable, leading to a very poor economy and education system. This study focuses on an adolescent age group dataset by recording physiological data when emotion is exhibited in a multimodal virtual environment. We decocted time frequency analysis and amplitude time series correlates including frontal alpha symmetry using a complex Morlet wavelet. For data visualization, we used the UMAP technique to obtain a clear district view of emotions. We performed 5-fold cross validation along with 1 s window subjective classification on the dataset. We opted for traditional machine learning techniques to identify complex emotion labeling.

Multi-Input Speech Emotion Recognition Model Using Mel Spectrogram and GeMAPS

“…Automatic emotion recognition is expected to be an effective technology for effortless communication and forms a major research hotspot in the field of artificial intelligence. Emotions can be identified from various sources of information, such as speech (voice), utterance transcript, facial expressions, and brain waves [ 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 ]. Among these sources, speech can be used for emotion recognition in face-to-face mode as well as in remote communication via telephone or video calls.…”