Emotion Elicitation with Stimuli Datasets in Automatic Affect Recognition Studies – Umbrella Review

Datasets for Automated Affect and Emotion Recognition from Cardiovascular Signals Using Artificial Intelligence— A Systematic Review

“…The topic is extensive, as it covers both data acquisition and computation. A typical experiment in AAER involves several steps [ 5 ]. Firstly, the researchers need to adopt a specific perspective on the field, as many exist that consider the universality [ 6 , 7 ] of emotions or their structure [ 8 ].…”

“…However, the general structure of elicitation experiments that are carried out to gather the data from human participants remains stable [ 9 ]. To evoke emotions, passive (e.g., video, music, or pictures presentation) or active stimulation (e.g., game playing, interaction with virtual reality, or conversation) is used [ 5 ]. Eliciting material may have different lengths, types, and quantities.…”

“…Next, the recognition phase begins. It involves data preprocessing, classification or regression, and finally, validation [ 5 ]. Due to its flexibility resulting in, e.g., reduced data preprocessing time [ 5 ], deep learning (DL) techniques are widely adopted [ 25 , 26 , 27 ], along with classical approaches in AAER [ 28 , 29 , 30 , 31 ].…”

“…The replicability crisis in both psychology and computer science also affects studies on AAER [ 5 , 33 , 34 ]. Poor methodological conduct often makes it impossible for existing research to be replicated or reproduced.…”

“…Each stage of an experiment leading to AAER should be repeatable and standardised among subjects. AAER concerns stimuli presentation, assessment of elicited emotions by the subject, collection of physiological parameters, and laboratory environment, including the presence of experimenter and individual factors [ 5 ].…”