Towards the development of physiological models for emotions evaluation

Reali, Pierluigi; Cosentini, Claudia; Carvalho, Paulo; Traver, Vicente; Bianchi, Anna Maria

doi:10.1109/embc.2018.8512236

Cited by 5 publications

(1 citation statement)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition to emotion classification studies which are based on a limited number of pre-defined emotional categories, several studies have performed EEG-based or electrocorticography (ECoG)-based emotion or mood state regression/correlation analyses to recognize small-scale emotional changes [ 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 ]. McFarland and colleagues performed canonical correlation analysis (CCA) to predict participants’ emotional states from the sensor-level features of EEG recorded during affective picture viewing [ 22 ].…”

Section: Introductionmentioning

confidence: 99%

Independent Components of EEG Activity Correlating with Emotional State

et al. 2020

View full text Add to dashboard Cite

Among brain-computer interface studies, electroencephalography (EEG)-based emotion recognition is receiving attention and some studies have performed regression analyses to recognize small-scale emotional changes; however, effective brain regions in emotion regression analyses have not been identified yet. Accordingly, this study sought to identify neural activities correlating with emotional states in the source space. We employed independent component analysis, followed by a source localization method, to obtain distinct neural activities from EEG signals. After the identification of seven independent component (IC) clusters in a k-means clustering analysis, group-level regression analyses using frequency band power of the ICs were performed based on Russell’s valence–arousal model. As a result, in the regression of the valence level, an IC cluster located in the cuneus predicted both high- and low-valence states and two other IC clusters located in the left precentral gyrus and the precuneus predicted the low-valence state. In the regression of the arousal level, the IC cluster located in the cuneus predicted both high- and low-arousal states and two posterior IC clusters located in the cingulate gyrus and the precuneus predicted the high-arousal state. In this proof-of-concept study, we revealed neural activities correlating with specific emotional states across participants, despite individual differences in emotional processing.

show abstract

Section: Introductionmentioning

confidence: 99%

Independent Components of EEG Activity Correlating with Emotional State

et al. 2020

View full text Add to dashboard Cite

show abstract

Interaction Between L4 AVs and Human Drivers in Italian Take-over Scenarios

Boscaro,

De Guglielmo,

Fossati

et al. 2024

Lecture Notes in Mechanical Engineering

View full text Add to dashboard Cite

Automated Driving (AD) technologies are rapidly transforming road transportation, emphasizing the critical role of Human-Machine Interaction (HMI). In this regard, the paper examines the interaction between Level 4 Autonomous Vehicles (L4 AVs) and human drivers in take-over scenarios within Italian traffic environments. Employing the Dynamic Driving Simulator at Politecnico di Milano, the study presents two simulation environments: an urban roundabout and a Ligurian highway. The research aims to measure the driver response during take-over requests. Questionnaires are used to psychologically analyse the participants. Physiological signals, including ECG, EEG, and EDA, are acquired throughout the entire simulation.

show abstract