Prediction of Players Motivational States Using Electrophysiological Measures during Serious Game Play

Derbali, Lotfi; Frasson, Claude

doi:10.1109/icalt.2010.143

Cited by 23 publications

(11 citation statements)

References 17 publications

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“…In the third step, classification is performed using the extracted features. The motivational states have been predicted based on brain activity for game play [19]. A non-EEG-based method to classify expert and novice levels of a game player is presented in [20], where objective-based action sequences are used.…”

Section: Introductionmentioning

confidence: 99%

A Game Player Expertise Level Classification System Using Electroencephalography (EEG)

et al. 2017

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Abstract:The success and wider adaptability of smart phones has given a new dimension to the gaming industry. Due to the wide spectrum of video games, the success of a particular game depends on how efficiently it is able to capture the end users' attention. This leads to the need to analyse the cognitive aspects of the end user, that is the game player, during game play. A direct window to see how an end user responds to a stimuli is to look at their brain activity. In this study, electroencephalography (EEG) is used to record human brain activity during game play. A commercially available EEG headset is used for this purpose giving fourteen channels of recorded EEG brain activity. The aim is to classify a player as expert or novice using the brain activity as the player indulges in the game play. Three different machine learning classifiers have been used to train and test the system. Among the classifiers, naive Bayes has outperformed others with an accuracy of 88%, when data from all fourteen EEG channels are used. Furthermore, the activity observed on electrodes is statistically analysed and mapped for brain visualizations. The analysis has shown that out of the available fourteen channels, only four channels in the frontal and occipital brain regions show significant activity. Features of these four channels are then used, and the performance parameters of the four-channel classification are compared to the results of the fourteen-channel classification. It has been observed that support vector machine and the naive Bayes give good classification accuracy and processing time, well suited for real-time applications.

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Section: Introductionmentioning

confidence: 99%

A Game Player Expertise Level Classification System Using Electroencephalography (EEG)

et al. 2017

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“…One promising way is the use of physiological sensors. This is notably explained by the significant results of recent studies involving physiological sensors to assess motivational learners' states as well as emotional and cognitive systems strategies [10,[17][18][19].…”

Section: Introductionmentioning

confidence: 99%

“…Some researchers have analyzed log files and have established correlations between learners' actions in log files Advances in Human-Computer Interaction 3 and their motivational states (e.g., [24]). Other researchers have used physiological sensors to assess learners' motivation and correlate physiological learners' responses to some dimensions of motivation such as attention and confidence (e.g., [17,25] [19] has used biometric sensors (HR, SC, EMG, and RESP) and facial expression analysis to develop a probabilistic model of detecting students' affective states within an educational game. Arroyo and colleagues [26] have used four different sensors (camera, mouse, chair, and wrist) in a multimedia adaptive tutoring system to recognize students' affective states and embed emotional support.…”

Section: Related Researchmentioning

confidence: 99%

Assessment of Learners’ Motivation during Interactions with Serious Games: A Study of Some Motivational Strategies in Food-Force

Derbali

Frasson

2012

Advances in Human-Computer Interaction

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This study investigated motivational strategies and the assessment of learners' motivation during serious gameplay. Identifying and intelligently assessing the effects that these strategies may have on learners are particularly relevant for educational computer-based systems. We proposed, therefore, the use of physiological sensors, namely, heart rate, skin conductance, and electroencephalogram (EEG), as well as a theoretical model of motivation (Keller's ARCS model) to evaluate six motivational strategies selected from a serious game called Food-Force. Results from nonparametric tests and logistic regressions supported the hypothesis that physiological patterns and their evolution are suitable tools to directly and reliably assess the effects of selected strategies on learners' motivation. They showed that specific EEG "attention ratio" was a significant predictor of learners' motivation and could relevantly evaluate motivational strategies, especially those associated with the Attention and Confidence categories of the ARCS model of motivation. Serious games and intelligent systems can greatly benefit from using these results to enhance and adapt their interventions.

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“…The conclusion was that the EEG waves corresponding to Delta, Sigma and Theta bands had strong correlation with heart rate at different sleep stages. Derbali et al [21] did a study on the prediction of motivation of players in a serious game using the EEG waves and any correlation with heart rate. The conclusion of the study was that the theta waves were positively correlated with motivation.…”

Section: Correlation Of Heart Rate and Beta Waves During Exercisementioning

confidence: 99%

Quantifying cognitive impairment due to physical or mental stress

Gupta

Dantu

2013

Proceedings of the 6th International Conference on PErvasive Technologies Related to Assistive Environments

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In this paper, we study impairment caused by physical stress and impairment caused by alcohol consumption. We first base line the EEG waves in a meditation state. Then we measure the EEG waves during the physical activities such as walking, climbing stairs and doing sit-ups. We also measure the effect on brain as a person spins while sitting in a revolving chair (simulating a mental stress). Finally, we measure the EEG waves after consumption of alcohol. Our study shows that EEG waves do capture the physical activities that cause impairment. The magnitude of EEG waves increases with increased intensity of stress as measured by physical exercise or alcohol consumption. The magnitude is highest for sit-ups as compared to other activities and the magnitude for sit-ups after alcohol is even higher when compared with magnitude before alcohol consumption. A higher magnitude means lower level of alertness. The results could be used to improve the care of elderly and plan the intensity of their physical activity.

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Prediction of Players Motivational States Using Electrophysiological Measures during Serious Game Play

Cited by 23 publications

References 17 publications

A Game Player Expertise Level Classification System Using Electroencephalography (EEG)

A Game Player Expertise Level Classification System Using Electroencephalography (EEG)

Assessment of Learners’ Motivation during Interactions with Serious Games: A Study of Some Motivational Strategies in Food-Force

Quantifying cognitive impairment due to physical or mental stress

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