Stress analysis of a computer game player using electroencephalogram

Appaji, Abhishek; Suma, H. N.

doi:10.1109/cimca.2014.7057749

Cited by 12 publications

(10 citation statements)

References 5 publications

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“…For instance, electroencephalography (EEG) has been employed to analyse stress in computer game players [5]. Physiological signals are employed to analyse the learning outcome of digital games [6].…”

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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“…Electrophysiological methods provide an objective, continuous, and impartial measure for analyzing the player experience [29].EEG has significantly gained importance in GUR and is shown to be an effective tool in the game studies [20]. For instance, EEG was found to be useful in differentiating the stressed and relaxed states of the game player during the racing and first-person shooter games [21]. Moreover, the level of motivation and relaxation in the game players was investigated using the EEG data [29,30].…”

Section: Related Workmentioning

confidence: 99%

“…Physiological measures electrocardiography (ECG) [15], electromyography (EMG) [16], electrodermal activity (EDA) [15,17], and electroencephalography (EEG), are significantly gaining importance in GUR [18][19][20][21]. In particular, games that provide adaptive features in response to brain signals are known as neurofeedback games.The usability of physiological measures in the game design of neurofeedback games has been demonstrated using well-designed experiments [22].…”

Section: Introductionmentioning

confidence: 99%

EEG in game user analysis: A framework for expertise classification during gameplay

Hafeez

Saeed

Arsalan

et al. 2021

Preprint

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Video games have become a ubiquitous part of demographically diverse cultures. Numerous studies have focused on analyzing the cognitive aspects involved in game playing that could help provide an optimal gaming experience level by improving video game design. To this end, we present a framework for classifying the game player’s expertise level using wearable electroencephalography (EEG) headset. We hypothesize that expert/novice players’ brain activity is different, which can be classified using the frequency domain features extracted from EEG signals of the game player. A systematic channel reduction approach is presented using a correlation-based attribute evaluation method. This approach identifies two significant EEG channels, i.e., AF3 and P7, from the Emotiv EPOC headset’s fourteen channels. The features extracted from these EEG channels contribute the most to the video game player’s expertise level classification. This finding is validated by performing statistical analysis (t-test) over the extracted features. Moreover, among multiple classifiers used, K-nearest neighbor is the best classifier in classifying the game player’s expertise level with up to 98.04% classification accuracy.Author summaryTehmina Hafeez ROLES Investigation, Writing – original draft * E-mail: tehminamalik.52@gmail.com AFFILIATION Department of Computer Engineering, University of Engineering and Technology, Taxila, 47050, Pakistan.Sanay Muhammad Umar Saeed (Corresponding author) ROLES Conceptualization, Writing – review editing * E-mail: sanay.muhammad@uettaxila.edu.pk AFFILIATION Department of Computer Engineering, University of Engineering and Technology, Taxila, 47050, Pakistan.Aamir Arsalan ROLES Methodology, Writing – review editing * E-mail: aamir.arsalan@uettaxila.edu.pk AFFILIATION Department of Computer Engineering, University of Engineering and Technology, Taxila, 47050, Pakistan.Syed Muhammad Anwar ROLES Validation, Writing – review editing * E-mail: s.anwar@uettaxila.edu.pk AFFILIATION Department of Software Engineering, University of Engineering and Technology, Taxila, 47050, Pakistan.Muhammad Usman Ashraf (Corresponding author) ROLES Validation, Writing – review editing * E-mail: usman.ashraf@skt.umt.edu.pk AFFILIATION Department of Computer Science, University of management and Technology, Lahore (Sialkot), 51040, Pakistan.Khalid Alsubhi ROLES Conceptualization, Writing – review editing AFFILIATION Department of Computer Science, King Abdul Aziz University, Jeddah, Saudi Arabia.

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“…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.…”

Section: Paper(s) Topicmentioning

confidence: 99%

“…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.…”

Section: Paper(s) Topicmentioning

confidence: 99%