National Repository of Grey Literature 2 records found  Search took 0.01 seconds. 
Emotion Recognition from Brain Electroencephalogram (EEG) Signals
Fritz, Karel ; Jawed, Soyiba (referee) ; Malik, Aamir Saeed (advisor)
Tato studie se zaměřuje na klasifikaci emocí z elektroencefalogramu (EEG). Kombinuje znalosti o fyziologii mozku (a emocí), s frekvenční analýzou, analýzou složitosti, zpracov- áním signálů a hlubokým strojovým učením (CNN, GNN). Cílem této práce je vytvořit model pro klasifikaci emocí a poskytnout nové náhledy do rozpoznávání emocí z EEG. Vytvořené modely stojí na principech CNN, GNN, multitask a self supervised tréninku. Jedním z výsledků bylo dosažení State of the Art výsledků na datasetu SEED. Proces porozumění této úloze sdílím na konci této práce.
High Level Analysis of the Psychotherapy Sessions
Polok, Alexander ; Karafiát, Martin (referee) ; Matějka, Pavel (advisor)
This work focuses on analyzing psychotherapy sessions within the DeePsy research project. This work aims to design and develop features that model the session dynamics, which can reveal seemingly subtle nuances. The mentioned features are automatically extracted from the source recording using neural networks. They are further processed, compared across sessions, and displayed graphically, creating a document that acts as a feedback document about the session for the therapist. Furthermore, this assistive tool can help therapists to professionally grow and to provide better psychotherapy in the future. A relative improvement in voice activity detection of 37.82% was achieved. The VBx diarization system was generalized to converge to two speakers with a minimum relative error rate degradation of 0.66%. An automatic speech recognition system has been trained with a 17.06% relative improvement over the best available hybrid model. Models for sentiment classification, type of therapeutic interventions, and overlapping speech detection were also trained.

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