Towards the Design of Affective Survival Horror Games: An Investigation on Player Affect

Cinematic virtual reality: Evaluating the effect of display type on the viewing experience for panoramic video

“…The question, "I felt afraid while watching this video", was designed to measure fear directly. The question, "I felt nervous while watching this video", was designed to measure anxiety, a state associated with fear during horror media [46]. Participants responded to both questions on a five-point Likert scale of agreement.…”

Electroencephalogram-based emotion assessment system using ontology and data mining techniques

“…It can work with noisy data and missing data in dataset. C4.5 is one of the preeminent inductive inference algorithms and has been successfully applied to affective computing tasks [4,22,56]. In our research, the multiple EEG features are classified into low/high arousal (or low/high valence) by performing a gender-specific classification task as follows:…”

A taxonomy and state of the art revision on affective games

“…Scheirer et al [14] Skin conductivity, blood pressure and mouse patterns for affective analysis Sakurazawa et al [15] Skin conductance response as emotional state detector Mandryk et al [16][17][18][19] Efficiency of several physiological measures Hazlett and L. [20] Facial electromyography Nacke and Lindley [21], Nacke et al [22,23] Multiple measures and flow between affective states Perez Martínez et al [24] Generality of physiological features Ravaja et al [25], Drachen et al [26], Levillain et al [27], Wu and Lin [28], Gualeni et al [29], Vachiratamporn et al [30], Martey et al [31], Abhishek and Suma [32], Landowska and Wróbel [33], Li et al [34] Applications of physiological measures Giakoumis et al [35] Automated boredom detection Chanel et al [36,37], Nogueira et al [38] Machine-learning classifiers for emotional states Jones and Sutherland [39] Emotion detection from player's voice Garner and Grimshaw [40], Nacke et al [41], Nacke and Grimshaw [42] Effect of the sound in players' fear level Christy and Kuncheva [43] Computer mouse with affective detection Going a step further, Nacke and Lindley [21], Nacke et al [22,23] studied how to measure the global player experience while playing a game analysing the same physiological metrics as before: electromyography, electrodermal activity and so on. Regarding the player experience, the authors measured the flow between varied affective states, such as anxiety, apathy and boredom.…”

“…There are many other papers that use and study those physiological factors as well, although they describe primarily the application of these factor to varied games and/or learning methods: Ravaja et al [25], Drachen et al [26], Levillain et al [27], Wu and Lin [28], Gualeni et al [29], Vachiratamporn et al [30], Martey et al [31], Abhishek and Suma [32], Landowska and Wróbel [33], Li et al [34], Giakoumis et al [35] with the automated boredom detection, Chanel et al [36,37] presenting a classifier for emotional classes, and Nogueira et al [38] with a classifier of different levels of arousal and valence.…”