Title Classification of cognitive load and emotional stress based on functional brain imaging techniques Title (croatian) Klasfikacija kognitivnoga opterećenja i emocionalnoga stresa utemeljena na tehnikama funkcionalnoga oslikavanja mozga Author Ivan Kesedžić Mentor Krešimir Ćosić (mentor)Mentor Siniša Popović (komentor)Committee member Krešimir Ćosić (član povjerenstva)Granter University of Zagreb Defense date and country 2022, Croatia Scientific / art field, TECHNICAL SCIENCES Universal decimal classificationUDC ) 621.3 - Electrical engineering Abstract Cognitive load and emotional stress affect people’s daily lives, so the adequate assessment of cognitive load and emotional stress is of special importance. Inadequate amounts of cognitive load and emotional stress negatively affect human performance and, in the long run, the health of the individual. Assessing cognitive load and emotional stress is especially important in highly stressful professions such as the military, pilots, and air traffic controllers.
Although cognitive load and emotional stress can be evaluated using different subjective questionnaires, their bias can lead to erroneous estimates. Objective methods based on neurophysiological signals collected using functional brain imaging techniques can be used to assess cognitive load and emotional stress. As part of this dissertation, two brain imaging techniques were used in cognitive load and emotional stress assessment: functional magnetic resonance imaging (fMRI) and functional near-infrared spectroscopy (fNIRS). These brain imaging techniques allow more precise and objective measurements of these mental states when compared to otherwise used approaches in cognitive load and stress estimation.
Within this doctoral thesis, a special interest is given to the computation methods and classification algorithms in estimating cognitive load and emotional stress levels. Experiments based on stimulation paradigms aimed at classifying these constructs are described. Methods for feature extraction, classification models selection, classification results, as well as discussion of results for all conducted experiments are presented. Classification methods in the assessment of cognitive load and emotional stress allow the better-than chance estimation of cognitive load and emotional stress levels.
The applications of such neurophysiological measurements enable more objective estimation of various mental states, such as emotional stress and cognitive load. The classification of the abovementioned mental states using various machine learning algorithms may make a significant improvement to the business-as-usual approach in many areas which may benefit from such objective emotional stress and cognitive load assessments. Developed methods and the obtained results are discussed in the context of prior work while considering limitations and proposing directions for future work.
Abstract (croatian) Kognitivno opterećenje i emocionalni stres utječu na svakodnevni život ljudi te je adekvatna procjena kognitivnog opterećenja i emocionalnog stresa od posebne važnosti. Neadekvatne količine kognitivnoga opterećenja i emocionalnoga stresa negativno utječu na ljudske performance i, dugoročno gledano, na zdravlje pojedinca. Procjena kognitivnoga opterećenja i emocionalnoga stresa posebno je važna u visoko stresnim profesijama kao što su vojska, piloti i kontrolori zračnog prometa.
Iako se kognitivno opterećenje i emocionalni stres mogu procijeniti korištenjem različitih subjektivnih upitnika samoprocjene, njihova pristranost može dovesti do pogrešnih procjena. Za procjenu kognitivnoga opterećenja i emocionalnoga stresa mogu se koristiti objektivne metode temeljene na neurofiziološkim signalima prikupljenima korištenjem funkcionalnih tehnika oslikavanja mozga. U sklopu ove doktorske disertacije korištene su dvije tehnike funkcionalnoga oslikavanja mozga u procjeni kognitivnoga opterećenja i emocionalnoga stresa: funkcionalna magnetska rezonancija (engl. functional magnetic resonance imaging, fMRI) i funkcionalna blisko-infracrvena spektroskopija (engl. functional near-infrared spectroscopy, fNIRS). Ove tehnike oslikavanja mozga omogućuju preciznija i objektivnija mjerenja opisanih mentalnih stanja u usporedbi s inače korištenim pristupima u procjeni kognitivnoga opterećenja i emocionalnoga stresa.
U okviru ove doktorske disertacije poseban je interes posvećen računarskim metodama i klasifikacijskim algoritmima u procjeni razina kognitivnoga opterećenja i emocionalnoga stresa. Opisani su eksperimenti utemeljeni na stimulacijskim paradigmama s ciljem klasifikacije ovih konstrukata. Prikazane su metode izlučivanja značajki, odabir modela klasifikacije, rezultati klasifikacije, kao i diskusija o rezultatima za sve provedene eksperimente. Metode klasifikacije u procjeni kognitivnoga opterećenja i emocionalnoga stresa omogućuju procjenu razina kognitivnoga opterećenja i emocionalnoga stresa.
Primjena takvih neurofizioloških mjerenja omogućuje objektivniju procjenu različitih psihičkih stanja, poput kognitivnoga opterećenja i emocionalnoga stresa. Klasifikacija navedenih mentalnih stanja korištenjem različitih algoritama strojnog učenja može značajno poboljšati procjenu stanja u mnogim područjima. Razvijene metode i dobiveni rezultati u doktorskoj disertaciji opisuju se u kontekstu dosadašnjeg rada uz razmatranje ograničenja i predlaganje smjerova za budući rad.
Keywords
classification of cognitive load
classification of emotional stress
neurophysiological signal processing
machine learning
functional magnetic resonance imaging
functional near-infrared spectroscopy
Keywords (croatian)
klasifikacija kognitivnoga opterećenja
klasifikacija emocionalnoga stresa
obrada neurofizioloških signala
strojno učenje
funkcionalna magnetska rezonancija
funkcionalna blisko-infracrvena spektroskopija
Language english URN:NBN urn:nbn:hr:168:088561 Promotion 2022 Study programme Title: Doctoral study programme "Electrical Engineering and Computing" Type of resource Text Extent x, 93 str. : graf. prikaz File origin Born digital Access conditions Closed access Terms of use Created on 2022-04-01 07:41:17
