Development of a Diagnostic Algorithm to Identify Psycho-Physiological Game Addiction Attributes Using Statistical Parameters

Hafeez, Maria; Idrees, Muhammad Dawood; Kim, Jung-Yong

doi:10.1109/access.2017.2753287

Cited by 14 publications

(2 citation statements)

References 42 publications

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“…Therefore, looking into the brain signal during the action of game playing was an inevitable step, despite the difficulty in controlling subjects during the experiment. Hafeez et al [ 12 ] and Kim et al [ 13 ] examined the bio-signals of IGD subjects during game playing and found significantly different EEG and ECG patterns in the IGD group compared to the healthy group. Moreover, the current study showed a 63.5%–73.1% accuracy in identifying the IGD subjects.…”

Section: Discussionmentioning

confidence: 99%

“…This is likely due to the variability of EEG response patterns among the IGD group in the resting state, and other studies were initiated to measure the EEG while subjects were playing real games. Hafeez et al [ 12 ] recorded EEG measurements during mobile gaming to identify the different wave patterns between game addicts and non-addicts. They used cluster analysis and cross-correlation analysis to mathematically define the significantly different signal patterns of θ and θ/σ in the right occipital lobe.…”

Section: Introductionmentioning

confidence: 99%

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EEG Parameter Selection Reflecting the Characteristics of Internet Gaming Disorder While Playing League of Legends

Kim

et al. 2023

Sensors

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Game playing is an accessible leisure activity. Recently, the World Health Organization officially included gaming disorder in the ICD-11, and studies using several bio-signals were conducted to quantitatively determine this. However, most EEG studies regarding internet gaming disorder (IGD) were conducted in the resting state, and the outcomes appeared to be too inconsistent to identify a general trend. Therefore, this study aimed to use a series of statistical processes with all the existing EEG parameters until the most effective ones to identify the difference between IGD subjects IGD and healthy subjects was determined. Thirty subjects were grouped into IGD (n = 15) and healthy (n = 15) subjects by using the Young’s internet addition test (IAT) and the compulsive internet use scale (CIUS). EEG data for 16 channels were collected while the subjects played League of Legends. For the exhaustive search of parameters, 240 parameters were tested in terms of t-test, factor analysis, Pearson correlation, and finally logistic regression analysis. After a series of statistical processes, the parameters from Alpha, sensory motor rhythm (SMR), and MidBeta ranging from the Fp1, C3, C4, and O1 channels were found to be best indicators of IGD symptoms. The accuracy of diagnosis was computed as 63.5–73.1% before cross-validation. The most interesting finding of the study was the dynamics of EEG relative power in the 10–20 Hz band. This EEG crossing phenomenon between IGD and healthy subjects may explain why previous research showed inconsistent outcomes. The outcome of this study could be the referential guide for further investigation to quantitatively assess IGD symptoms.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

EEG Parameter Selection Reflecting the Characteristics of Internet Gaming Disorder While Playing League of Legends

Kim

et al. 2023

Sensors

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Diagnostics by Statistical Characteristics of Different Deep Levels

Gusev

Eremin

Rumyantsev

2021

Lecture Notes in Networks and Systems

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Feature engineering of EEG applied to mental disorders: a systematic mapping study

García-Ponsoda,

García-Carrasco,

Teruel

et al. 2023

Appl Intell

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Around a third of the total population of Europe suffers from mental disorders. The use of electroencephalography (EEG) together with Machine Learning (ML) algorithms to diagnose mental disorders has recently been shown to be a prominent research area, as exposed by several reviews focused on the field. Nevertheless, previous to the application of ML algorithms, EEG data should be correctly preprocessed and prepared via Feature Engineering (FE). In fact, the choice of FE techniques can make the difference between an unusable ML model and a simple, effective model. In other words, it can be said that FE is crucial, especially when using complex, non-stationary data such as EEG. To this aim, in this paper we present a Systematic Mapping Study (SMS) focused on FE from EEG data used to identify mental disorders. Our SMS covers more than 900 papers, making it one of the most comprehensive to date, to the best of our knowledge. We gathered the mental disorder addressed, all the FE techniques used, and the Artificial Intelligence (AI) algorithm applied for classification from each paper. Our main contributions are: (i) we offer a starting point for new researchers on these topics, (ii) we extract the most used FE techniques to classify mental disorders, (iii) we show several graphical distributions of all used techniques, and (iv) we provide critical conclusions for detecting mental disorders. To provide a better overview of existing techniques, the FE process is divided into three parts: (i) signal transformation, (ii) feature extraction, and (iii) feature selection. Moreover, we classify and analyze the distribution of existing papers according to the mental disorder they treat, the FE processes used, and the ML techniques applied. As a result, we provide a valuable reference for the scientific community to identify which techniques have been proven and tested and where the gaps are located in the current state of the art. Graphical Abstract

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Development of a Diagnostic Algorithm to Identify Psycho-Physiological Game Addiction Attributes Using Statistical Parameters

Cited by 14 publications

References 42 publications

EEG Parameter Selection Reflecting the Characteristics of Internet Gaming Disorder While Playing League of Legends

EEG Parameter Selection Reflecting the Characteristics of Internet Gaming Disorder While Playing League of Legends

Diagnostics by Statistical Characteristics of Different Deep Levels

Feature engineering of EEG applied to mental disorders: a systematic mapping study

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