Adaptable Game Experience Based on Player's Performance and EEG

Fernandez, Don; Mikami, Koji; Kondo, Kunio

doi:10.1109/nicoint.2017.11

Cited by 11 publications

(4 citation statements)

References 22 publications

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“…E.g. in [6,15], the authors monitored biological changes in a player's organism in order to adjust the level of difficulty during gameplay. Based on the results of EEG, the system may adjust requirements of a game to maintain constant level of player's focus.…”

Section: Methodsmentioning

confidence: 99%

Impression Curve as a New Tool in the Study of Visual Diversity of Computer Game Levels for Individual Phases of the Design Process

Andrzejczak

Osowicz

Szrajber

2020

Lecture Notes in Computer Science

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Impression curve is a widely used method in urban and landscape design to assess visual diversity of the space. In these studies, the method is applied for game level design. The goal of conducted research was the analysis of space perception in successive design phases related to the process of game environment formation. Next steps of the design process define the space burdened with more and more information. It aims to evaluate if initial assumptions, made by a designer at the beginning of the designing process, are maintained with the increase in the number of details and the content of locations. These studies are also a background for research in automation of visual diversity assessment. This, in turn, is related to making a player focused and interested during a gameplay, by the means of space defining an action scene. By applying a method from domain of urban planning and architecture in humancomputer interaction (HCI) studies related to virtual space, we show that both -defining the surroundings and its impact on recipient -are subject to the same rules in either case.

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Section: Methodsmentioning

confidence: 99%

Impression Curve as a New Tool in the Study of Visual Diversity of Computer Game Levels for Individual Phases of the Design Process

Andrzejczak

Osowicz

Szrajber

2020

Lecture Notes in Computer Science

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“…Conversely, affective-based DDA focuses on adjusting game difficulty in response to a player's emotional state, utilizing real-time physiological feedback from sensors. This method has been explored using various tools, including Electroencephalography (EEG) devices for detecting anxiety or stress [18,19] and the affective metric of focus in two-dimensional (2D) platform games [20]. Stein et al [21] implemented affective-based DDA in a multiplayer third-person shooter, targeting excitement as the key emotional driver.…”

Section: Related Workmentioning

confidence: 99%

Exploring Dynamic Difficulty Adjustment Methods for Video Games

Fisher,

Kulshreshth

2024

Virtual Worlds

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Maintaining player engagement is pivotal for video game success, yet achieving the optimal difficulty level that adapts to diverse player skills remains a significant challenge. Initial difficulty settings in games often fail to accommodate the evolving abilities of players, necessitating adaptive difficulty mechanisms to keep the gaming experience engaging. This study introduces a custom first-person-shooter (FPS) game to explore Dynamic Difficulty Adjustment (DDA) techniques, leveraging both performance metrics and emotional responses gathered from physiological sensors. Through a within-subjects experiment involving casual and experienced gamers, we scrutinized the effects of various DDA methods on player performance and self-reported game perceptions. Contrary to expectations, our research did not identify a singular, most effective DDA strategy. Instead, findings suggest a complex landscape where no one approach—be it performance-based, emotion-based, or a hybrid—demonstrably surpasses static difficulty settings in enhancing player engagement or game experience. Noteworthy is the data’s alignment with Flow Theory, suggesting potential for the Emotion DDA technique to foster engagement by matching challenges to player skill levels. However, the overall modest impact of DDA on performance metrics and emotional responses highlights the intricate challenge of designing adaptive difficulty that resonates with both the mechanical and emotional facets of gameplay. Our investigation contributes to the broader dialogue on adaptive game design, emphasizing the need for further research to refine DDA approaches. By advancing our understanding and methodologies, especially in emotion recognition, we aim to develop more sophisticated DDA strategies. These strategies aspire to dynamically align game challenges with individual player states, making games more accessible, engaging, and enjoyable for a wider audience.

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“…For a computer game to reliably mitigate the experience of pain, the game must adapt the level of demand to the skill level of the individual player, e.g., dynamic difficulty adjustment (DDA) (Zohaib, 2018 ). This type of personalised gaming experience can be achieved by a closed-loop approach to neuroadaptive gaming, wherein neurophysiological measures are collected (Liu et al, 2009 ; Ewing et al, 2016 ; Fernández et al, 2017 ) and analysed to create a real-time model of player state, which is subsequently used to dynamically adjust game demand to match the skills of the individual player.…”

Section: Introductionmentioning

confidence: 99%

Classification of Game Demand and the Presence of Experimental Pain Using Functional Near-Infrared Spectroscopy

Fairclough

Dobbins

Stamp

2021

Front. Neuroergonomics

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Pain tolerance can be increased by the introduction of an active distraction, such as a computer game. This effect has been found to be moderated by game demand, i.e., increased game demand = higher pain tolerance. A study was performed to classify the level of game demand and the presence of pain using implicit measures from functional Near-InfraRed Spectroscopy (fNIRS) and heart rate features from an electrocardiogram (ECG). Twenty participants played a racing game that was configured to induce low (Easy) or high (Hard) levels of demand. Both Easy and Hard levels of game demand were played with or without the presence of experimental pain using the cold pressor test protocol. Eight channels of fNIRS data were recorded from a montage of frontal and central-parietal sites located on the midline. Features were generated from these data, a subset of which were selected for classification using the RELIEFF method. Classifiers for game demand (Easy vs. Hard) and pain (pain vs. no-pain) were developed using five methods: Support Vector Machine (SVM), k-Nearest Neighbour (kNN), Naive Bayes (NB) and Random Forest (RF). These models were validated using a ten fold cross-validation procedure. The SVM approach using features derived from fNIRS was the only method that classified game demand at higher than chance levels (accuracy = 0.66, F1 = 0.68). It was not possible to classify pain vs. no-pain at higher than chance level. The results demonstrate the viability of utilising fNIRS data to classify levels of game demand and the difficulty of classifying pain when another task is present.

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Adaptable Game Experience Based on Player's Performance and EEG

Cited by 11 publications

References 22 publications

Impression Curve as a New Tool in the Study of Visual Diversity of Computer Game Levels for Individual Phases of the Design Process

Impression Curve as a New Tool in the Study of Visual Diversity of Computer Game Levels for Individual Phases of the Design Process

Exploring Dynamic Difficulty Adjustment Methods for Video Games

Classification of Game Demand and the Presence of Experimental Pain Using Functional Near-Infrared Spectroscopy

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