Content area
Background
Video games are ubiquitous and play a significant role in our daily lives. They are utilized in waiting areas at hospitals, while waiting for buses, on airplanes, at home, while playing with friends, or when driving with children. Alterations in EEG brain waves are associated with responses to external stimuli i.e. video gaming. The cognitive states of users can be recognized by identifying distinct brain waves and tracking the various brain regions and their wave frequency ranges.
Objectives
To track the effect of video game playing on brain activity by quantitative analysis of EEG (QEEG) and to contrast the spectral analysis of dynamic brain activity while engaging in continuous visual mental tasks.
Methods
This is a cross sectional observational descriptive study conducted on young adults (15 to 26 years). A free endless runner game – Subway Surfers (SYPO games) was played on a smart phone by each participant during conventional EEG recording.
Results
There was a significant increase in power ratio index (PRI) in all regions during gaming compared to baseline readings. The younger subjects (< 20 years) had significantly higher PRI in the frontal, temporal and occipital regions compared to older subjects. Moreover, post gaming PRI showed significant negative correlation to age in the prefrontal and frontal regions.
Conclusion
Our results support the hypotheses that during gaming time, there is less creative thinking and alertness, problem solving, decision making, and more emotional strain, aggravation, and memories of past events, which is due to high levels of mental effort and attention to the game. The effect of playing video games is relatively more on younger people and its effect outlasts the gaming time, which could be used positively with further research.
Introduction
The popularity of video gaming is rising quickly and has developed into a significant social activity [1]. Considering the significant advancements in this field in recent years, it is currently estimated that 40% of people worldwide play video games, translating to about 3.1 billion users [2, 3].
Studies support that playing video games in moderation can bring great advantages, specially focused on improving cognitive skills [3, 4–5], such as increasing decision-making ability [3, 6], enhancing visual attention [3, 7] and improving attention control [3, 8].
Although there are some benefits to playing video games, studies have shown that playing them too often can have negative impacts as well, including stress, aggressive behavior, verbal memory loss, depression, decreased cognitive function, anxiety, sleep difficulties, and behavioral addiction [9].
It is not only about the frequency of gaming but the genre of the game as well. Bakaoukas and his colleagues, in 2016, used a brain computer interface to study brain activity while playing various computer game genres in an effort to gain a better understanding of end-user behavior patterns. Their analysis of the signals demonstrated that there are variations in brain activity when playing different types of games. Their findings pointed to several important elements, including the interaction process, the general gameplay, the surroundings, and the existence of rivals [10].
For instance, when playing racing or first-person shooter games, electroencephalogram (EEG) was found to be helpful in distinguishing between the player’s anxious and calm states [11, 12]. Additionally, the EEG data was used to examine the participants’ motivation and relaxation levels [10, 11, 13]. To determine the optimal classifier for different cognitive-affective states of the game player, the association between EEG frequency bands and valence, arousal, and engagement was examined [11, 14]. It was discovered that wearable EEG sensors provided a discrete, dependable, and portable method of monitoring and differentiating the gamer’s cognitive-affective states [11, 15].
Using multi-channel EEG recordings offers the advantage of allowing brain activity to be mapped over the scalp, which is a surface representation of the underlying brain activity. The three-dimensional topographic brain maps indicate how the activity of the brain changes with time, space, and frequency. These maps also show the main rhythm of brain activity in each location [16].
The major EEG brain waves for humans may be broken down into four distinct frequency patterns: alpha (ranging from 8 to 13 Hz), theta (ranging from 4 to 8 Hz), beta (ranging from 13 to 30 Hz), and gamma (beyond 30 Hz) [17].
Action video games are cognitively taxing because they need players to monitor several intricate visual cues at once, pay attention to the stimuli in their peripheral vision, make decisions and take appropriate action under time constraints [18, 19].
Playing video games was found to cause a theta peak, which was lessened during the rest period. It is believed that an increase in cortical activity brought on by intense mental effort and focus is reflected in an increase in theta band power [1, 20, 21].
Likewise, it has been observed that vigilant attentiveness and goal-directed behavior are linked to beta activity, especially in the frontal channels [1, 21, 22].
The aim of this work is to monitor and analyze brain activity of young adult Egyptian population while playing video games and to compare the spectral analysis of dynamic brain activity while performing ongoing visual mental tasks (i.e., video games), with those with no similar performance.
Methods
This is a cross sectional observational descriptive study conducted on 101 healthy young adults with ages ranging from 15 to 26 years. The study was approved by Research ethical committee (REC) of Cairo university, registration number (N-450-2023). Prior to participation, the study protocol was explained in detail and all participants filled in a written informed consent. The study was conducted in accordance with the Declaration of Helsinki (1964).
All included subjects filled in a questionnaire (appendix 1). This questionnaire focused on handedness, presence of any visual problems, presence of chronic illnesses, epilepsy or psychiatric troubles, as well as a few questions about video games. We exclude subjects with any visual complaints or suffering from chronic illnesses, with a history of epilepsy or psychiatric troubles.
The EEG examination was done using EBNeuro Galileo NT line EEG.NET, Galeleo NT Line 3.90 1.80(PM S USA). The quantitative analysis was done using the machine’s automated EEG.NET software tools.
The EEG recording was done according to the 10–20 system of electrode placement (Fig. 1), proposed by the International Federation of Societies for Electroencephalography and Clinical Neurophysiology in 1958, has been the international standard for recording routine scalp EEG for clinical use. This system provides a consistent and replicable method of recording EEG with 21 electrodes placed at relative distances (10% or 20%) between the cranial landmarks over the head [23].
A free endless runner game – Subway Surfers (SYPO games), with a reaction-based, visually stimulating and minimal narrative or problem-solving characters, was played on a smart phone by each participant during conventional EEG recording. The game was installed free of charge on a smart phone from google/apple store. We chose the Subway Surfers game because it is a popular game worldwide and downloaded massively [24, 25]. While achieving the research, most subjects in the studied age group were familiar with it. If, before the study, the subjects were found to be unfamiliar with the game, they were given instructions on how to play it.
[See PDF for image]
Fig. 1
The international 10–20 system of electrode placement of EEG. A Lateral view B Superior view of the scalp [26]
In accordance with the international 10–20 electrode placement scheme, a 21-channel headset was placed over the participant’s scalp to acquire EEG data.
The electrodes on the headset cover the scalp sufficiently to record the electrical activity of the brain, and it offers good temporal resolution. For example, the pre-frontal region is covered by electrodes FP1 and FP2, the frontal region by electrodes FZ, F3, F4, F7, and F8, the central region by electrodes CZ, C3, and C4, the temporal region by electrodes T3, T4, T5, and T6, the parietal region by electrodes PZ, P3, and P4 and the neural activity from the occipital region of the brain is captured by electrodes O1 and O2.
It is important to note that five primary brain rhythms, each with unique characteristics, are typically used to analyze EEG signals: During so-called deep or slow wave sleep (SWS), delta (δ, [0.5, 4] Hz) waves are prevalent, exhibit relatively large amplitudes, and are associated with participants’ attention to internal processing; Theta (θ [4, 8], Hz) band power selectively reflects the successful storing of new information by increasing in response to memory demands; Alpha (α [8, 10], Hz) is the most significant activity in normal subjects at rest is which decreases with eye opening or mental effort; Beta (β [10, 29], Hz) activity is concerned with the states of raised alertness and increased attention; Gamma (γ, over 30 Hz) components are challenging to be captured through electrodes due to the scalp’s low-pass filtering function, which typically limits frequencies to 45 Hz when working with resting-state acquisition protocols [27].
To obtain high-quality data during the experiment, saline liquid was absorbed into the EEG electrodes. A cozy bed and a smartphone with the subway game already installed were given to the participants. There was no acoustic noise in the room selected for the collection of EEG data.
The EEG recording was broken into two 5-minute recording segments evenly distributed throughout the recording. The participants were instructed to keep their eyes open as much as possible and to limit their muscular movements and eye blinks during each recording phase. A baseline recording of a 5-minute open-eye resting condition was made before starting the game.
After that, the participants played the video game nonstop for 5- minutes.
The participants were divided into two groups according to their age, where group 1 had an age below or equal to 20 years, and group 2 age above 20 years of age. This stratification was based on developmental and cognitive maturation differences observed during late adolescence and early adulthood.
A specialized EEG interpreter inspected the EEG visually for any abnormalities and artifact free epochs were chosen for quantitative analysis from each segment of recording (Fig. 2a, b).
[See PDF for image]
Fig. 2
A chosen EEG epoch. Artifact free epoch; raw EEG data a its spectral and b its quantitative analyses
The power ratio index (PRI) was calculated by dividing the relative power sum of slow bands (delta and theta) by fast bands (alpha and beta). The interhemispheric difference in PRI was obtained by subtracting left hemisphere values from corresponding right hemisphere values [28, 29].
The PRI is selected mainly for its capacity to clarify the balance of different frequency bands in the brain’s electrical activity. The PRI often includes ratios like delta/alpha or theta/alpha, which act as important indicators of brain states and functionality. It signifies brain functional states, where specific frequency bands are associated with cognitive and physiological circumstances. Elevated delta and theta activity may signify fatigue or cortical deceleration, while alpha activity is frequently linked to a state of relaxed awareness. Ratios such as delta/alpha enable the measurement of transitions between various states [29, 30].
The application of PRI enhances both sensitivity and specificity, as ratios can amplify subtle changes in EEG that may not be noticeable through the examination of individual power values, hence improving the ability to detect abnormal or clinically important patterns. Its application also reduces variability, as absolute power measurements can be influenced by individual differences, electrode placement, and environmental factors. Ratios often normalize this variability, resulting in more consistent reading between people and sessions [29, 30].
The selection of the PRI is additionally motivated by its capacity to depict neural oscillatory balance, provide sensitive indications of cerebral states, and facilitate standardized, comparative assessments across various populations or contexts [29, 30].
Statistical analysis was carried out using Jamovi software (2.3.28). Categorical data is expressed in terms of count and percent (%) and compared using Fisher’s exact test. Quantitative data is expressed in terms of mean and standard deviation or median, 25th and 75th percentiles. Comparisons between two states (pre and post playing) were done using Wilcoxon signed rank test. Comparison between the medians across the two groups was done using Kruskal-Wallis test. Correlation between variables is done using “Pearson correlation test” P < 0.05 was considered significant.
Results
This study included 101 right-handed healthy subjects, 21 (20.8%) were males and 80 (79.2%) were females. Only 1 subject wore glasses. 68% of the subjects had previous knowledge of the game. They reported that the time they usually spend playing video games per day ranged from 0 to 180 min with a median of 10 min, 94.9% of them stated that they spend less than 1 h per day playing, 3% spend from 1 to 2 h, 2% spend from 2 to 3 h. The highest score of the game recorded during the session ranged from 257 to 92,599 with a median of 11,675.
The subjects’ age ranged from 15 to 26 with mean of 16.97 ± 2.3 years categorized as Group 1 (< 20 years) that includes 90 subject and Group 2 (> 20 years) that includes 10 subjects. Given the nature of the data and the potential violations of parametric assumptions, the Kruskal-Wallis test was selected to compare results between the two groups. This non-parametric test is appropriate for analyzing differences in medians when the data are not normally distributed or are ordinal in nature. Although there is a notable discrepancy in group sizes; 90 participants in one group and only 10 in the other, the Kruskal-Wallis test remains valid under these conditions. However, it is important to consider that the smaller sample size in one group may reduce the statistical power and influence the robustness of the findings. Consequently, results were interpreted with caution, acknowledging the potential impact of unequal group sizes on the analysis [31].
There was a significant increase in PRI in all regions during gaming compared to pre-gaming (baseline) readings (Table 1).
Table 1. PRI during gaming compared to pre-gaming readings
Region | Pre gaming | During gaming | P value | ||
|---|---|---|---|---|---|
Median | (25th, 75th) | Median | (25th, 75th) | ||
Frontal | 13.92 | (6.67, 36.86) | 24.84 | (11.12, 52) | < 0.001 |
Central | 9.44 | (2.52, 29.21) | 15.33 | (6.56, 41.59) | < 0.001 |
Parietal | 5.97 | (2.44, 21.04) | 21.12 | (7.19, 72.94) | < 0.001 |
Temporal | 14.61 | (5.14, 31.54) | 36.6 | (16.17, 84.91) | < 0.001 |
Occipital | 3.45 | (1.38, 11.98) | 18.46 | (8.13, 42.64) | < 0.001 |
During playing the game, the younger age group subjects (< 20 years) showed significantly higher PRI in frontal, temporal and occipital regions compared to older subjects. The difference was not present in the pre-gaming readings (Table 2).
Table 2. PRI in both age groups (20 years old cutoff point)
Group | Region | Group 1 (< 20 years) | Group 2 (> 20 years) | P-value | ||
|---|---|---|---|---|---|---|
Median | (25th, 75th) | Median | (25th, 75th) | |||
Pre gaming | Frontal | 14.12 | (7.17, 34.76) | 10.07 | (5.42, 40.24) | 0.63 |
Temporal | 14.61 | (5.29, 31.77) | 13.67 | (4.9, 26.57) | 0.86 | |
Occipital | 3.69 | (1.38, 12.18) | 2.07 | (1.25, 7.76) | 0.42 | |
During gaming | Frontal | 25.41 | (12.69, 56.26) | 7.4 | (6.29, 26.32) | 0.04 |
Temporal | 41.88 | (18.12, 86.98) | 12.25 | (7.22, 24.93) | 0.04 | |
Occipital | 20.34 | (9.55, 44.92) | 8.78 | (3.52, 17.15) | 0.02 | |
Post gaming PRI showed significant negative correlation to age in prefrontal and frontal regions (r= -0.242, p = 0.016) and (r=-0.28, p = 0.004) respectively. Such relation wasn’t present in the pre gaming recordings (r=-0.035, p = 0.73*) and (r = 0.103, p = 0.31) (Fig. 3).
[See PDF for image]
Fig. 3
Correlation between PRI and age. a Post gaming prefrontal region, b post gaming frontal region
Discussion
Brain-wave activity that is collected by an electroencephalogram (EEG) offers a plethora of information regarding the ways in which players engage with the game and the cognitive processes that are associated with their behavior while playing [32].
For determining the dynamic EEG patterns in healthy human volunteers while they were playing a well-known endless runner game with a reaction-based, visually stimulating and minimal narrative or problem-solving characters, the current study was carried out on a total of one hundred and one healthy individuals.
Participants were divided into two age groups: below or equal to 20 years and above 20 years. Neurodevelopmental research indicates that the prefrontal cortex and other brain regions involved in executive functions, decision-making, and problem-solving continue to mature into the early twenties. Additionally, mobile gaming habits and engagement patterns tend to differ across these age ranges, potentially influencing cognitive outcomes [33].
By grouping participants this way, we aim to account for developmental and behavioral variations that may impact the effect of mobile games on cognition, thus enabling a more precise analysis of age-related differences in response to gaming interventions.
When compared to the baseline recordings, it demonstrated a significant increase in PRI (power ratio index) across the board in all regions of the brain during gaming, fundamental increase of slower waves.
According to Zhou and colleagues’ study in 2021, the relative power (RP) has been regarded as a crucial measure for the analysis of EEG during cognitive tasks. Previously, the alpha-band RP from the resting state was suggested as a way to measure cognitive performance.
Higher theta band RP was thought to be linked to memory consolidation or cognitive function. There was a substantial negative correlation between the inattention score and low gamma RP [34].
The frontal cortex and other related cortical regions carry out a variety of tasks together referred to as cognition. The frontal brain is particularly crucial for organizing suitable behavioral reactions to both internal and external inputs. These cognitive activities are carried out by the frontal cortex integrating complicated perceptual information from the parietal association cortex, sensory cortices, and motor cortices [35]. The brain activity of these regions varies, indicating that they contribute to the gamer’s level of expertise [11].
According to Saalmann and his colleagues, the alpha band frequency range is more prominent in the occipital areas. This frequency range has been associated with creative thinking and alertness [36]. On the other hand, larger levels of beta activity in the frontal lobe are associated with higher levels of intense mental activity, problem solving, decision making, and focused attention [37]. Based on research conducted by Georgiadis et al. [38] and Sood et al. [39], there is a correlation between gamma activity and increased levels of mental activity, motor function, and cognition while theta waves are largely located in the temporal lobe and are associated with feelings of emotional strain, aggravation, and memories of events that occurred in the past [40]. Of note, the current study studied no faster activities than beta waves.
Furthermore, Ravindran et al. [1] and Sheikholeslami et al. [41] both reported that theta levels reached their highest point during the act of playing video games, but these levels decreased when the subjects were in a resting condition. According to Naumann et al. [20] and Salminen et al. [42], an increase in the power of the theta band is assumed to indicate an increase in the activation of the cortex that occurs because of high levels of mental effort and attention.
We support conclusions of previous literature that during gaming, as related to the baseline of the player, less creative thinking and alertness, less problem solving, decision making, and more emotional strain, aggravation, and memories of past events were speculated. Also, an increase in the activation of the cortex that occurs because of high levels of mental effort and attention to the game.
Additionally, He and his colleagues [16], investigated the spectral brain maps of individuals who participated in both competitive and strategy games. The findings revealed that the theta predominance was significantly higher during the strategy game in comparison to the time spent playing the competitive game. Also, Ravindran et al. [1], found that beta activity is associated with aggressive behavior. Besides, Salminen and Ravaja’s research [42], demonstrated the activation of theta while people were playing First Person Shooter (FPS) games that featured violent events.
In a study that analyzed brain oscillations using frequency analysis during gaming, Salminen and Ravaja [11] found that beta activity, particularly in the frontal channels, has been shown to be related to alert attention and goal driven activities. Also, Malik and his colleagues [43] did a study in which they investigated the ways in which the brain is active when playing video games on large screens. The researchers employed absolute power, coherence, and phase lag to analyze this phenomenon. It was found that the absolute power in the occipital, parietal frontal, and motor regions was higher in the beta band (12–25 Hz) when playing video games as opposed to sitting still. This was determined when comparing the two activities. It was noticed that the regions described above exhibited high levels of coherence in beta, which strongly suggests that there is a high degree of connectivity between them respectively. The frontal, parietal, occipital, and motor regions of the brain were shown to communicate with one another more quickly when phase lag was present. The previously mentioned two studies do not align with our results regarding change in power they concluded. This could be related to a different approach to power analysis, as well as different screen sizes between studies. Also, we did not study further methodologies applied by Malik and his colleagues [43].
According to the findings of the current study, which investigated the age effect in relation to video gaming, those younger than 20 years old exhibited considerably higher PRI during playing in the frontal, temporal, and occipital areas; fundamentally relatively slower waves with same implication mentioned before.
Neural networks in the brains of younger adults are highly specialized, which means that they are more sparsely connected between other networks but densely connected inside certain subnetworks. As networks age, they become less specialized, as evidenced by increasing internetwork activity during specified tasks and while at rest, and decreasing within-network connectivity [44, 45–46]. Age-related variations in relative EEG power are associated with grey matter shrinkage, synaptic pruning, and the development of the gamma-aminobutyric acid (GABA) neurotransmitter system [47, 48, 49–50].
Researchers found that absolute power dropped with age across all frequency bands, but it was most noticeable in the slower frequency bands [51, 52–53]. Also, Clarke et al. [54] and Niemarkt et al. [55], found that the relative power of the delta and theta bands had a propensity to have a negative correlation with age. On the other hand, the relative power of the faster bands, such as alpha and beta, had a positive correlation with age. Giannakos et al. [32] agreed with their findings, indicated that when performance grew, it resulted in an increasing level of mental effort (and all the associated cognitive abilities: attention, focus, problem-solving etc.), with two exceptions (i.e. Occipital Alpha and Occipital Theta). Accordingly, performance had a negative effect only on occipital alpha and occipital theta, while the significant effect on all the other cases was positive (that is, high performers had significantly higher power in the respective bands). It is to be mentioned that our study only included adolescents and young adults, with no further age groups to compare with, however the differences were significant.
Post gaming PRI showed significant negative correlation to age in prefrontal and frontal regions, i.e. as age increased, post gaming regional PRI decreases, relatively faster waves with age increase. Evidence suggests that the pre-frontal cortex (PFC) is the key brain region in charge of cognitive regulation at the neuronal level [44, 56, 57]. The PFC has been recognized as a “warning system” for the aging effect on cognition and is among the first regions to exhibit decreased activity while under stress [44, 58]. We are suggesting prolonged engagement of younger age groups to the gaming process that outlasted the gaming time and faster recovery of relatively older ages.
Based on our very previous assumption of relatively more impact of video games on younger ages of our study group, this should have suggested to us a positive gaming aspect. Dye and colleagues in 2009, discussed that action video game training can help young adults to develop a generalized speeding pattern across activities since it improves their reaction time on tasks unrelated to the training. They recommended allowing the creation of games with a broad range of accessibility and suitability that may be applied to both educational and clinical settings [59]. However, our study was limited to one electrophysiologic modality and one video game.
Conclusion
Gaming affects EEG indices by altering the activity of different frequency bands, typically enhancing alertness and attention-related activity while diminishing relaxation-associated alpha waves. The particular alterations are contingent upon the game’s nature and intensity, the duration of play, and the individual’s experience. These EEG alterations offer insights into cognitive and emotional states during gaming and their relationship to learning, neuroplasticity, and cognitive.
Limitations
This study tests only one type of video gaming, for a limited age group.
Acknowledgements
The authors are grateful for all the participants who agreed to take part in this study.
Author contributions
Bodour Abdelkader: Writing – review & editing, Resources, Methodology, Formal analysis. Saly Hasan Elkholy: Visualization, Validation, Supervision, Formal analysis, Conceptualization, Project administration. Shahira Mostafa: Supervision, Project administration. Hala Rashad El Habashy: Visualization, Validation, Supervision, Conceptualization, Project administration. Mye Ali Basheer: Writing – original draft, Software, Data curation, Formal analysis, Conceptualization. Basma Bahgat El Sayed: Supervision, Software, Data curation, Formal analysis, Conceptualization.
Funding
This research did not receive any specific grant from funding agencies in the public, commercial, or non-profit sectors.
Data availability
The data that support the findings of this study are available from the corresponding author upon reasonable request.
Declarations
Human ethics and consent to participate
The study was approved by Research ethical committee (REC) of Cairo university, registration number (N-450-2023). Prior to participation, the study protocol was explained in detail and all participants filled in a written informed consent. The study was conducted in accordance with the Declaration of Helsinki (1964).
Consent to publish
Not applicable.
Competing Interests
The authors declare no competing interests.
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
References
1. Ravindran AS, Mobiny A, Cruz-Garza JG, Paek A, Kopteva A, Vidal JL. Assaying neural activity of children during video game play in public spaces: a deep learning approach. J Neural Eng. 2019;16(3):036028.
2. Intelligence DF. Global video game consumer segmentation.
3. Moreno-Calderón, S; Martínez-Cagigal, V; Santamaría-Vázquez, E; Pérez-Velasco, S; Marcos-Martínez, D; Hornero, R. Combining brain-computer interfaces and multiplayer video games: an application based on c-VEPs. Front Hum Neurosci; 2023; 17, 1227727. [DOI: https://dx.doi.org/10.3389/fnhum.2023.1227727]
4. Granic, I; Lobel, A; Engels, RC. The benefits of playing video games. Am Psychol; 2014; 69,
5. Reynaldo, C; Christian, R; Hosea, H; Gunawan, AA. Using video games to improve capabilities in decision making and cognitive skill: a literature review. Procedia Comput Sci; 2021; 179, pp. 211-21. [DOI: https://dx.doi.org/10.1016/j.procs.2020.12.027]
6. Jordan, T; Dhamala, M. Video game players have improved decision-making abilities and enhanced brain activities. Neuroimage: Rep; 2022; 2,
7. Gan, X; Yao, Y; Liu, H; Zong, X; Cui, R; Qiu, N; Xie, J; Jiang, D; Ying, S; Tang, X; Dong, L. Action real-time strategy gaming experience related to increased attentional resources: an attentional Blink study. Front Hum Neurosci; 2020; 14, 101. [DOI: https://dx.doi.org/10.3389/fnhum.2020.00101]
8. Anguera, JA; Boccanfuso, J; Rintoul, JL; Al-Hashimi, O; Faraji, F; Janowich, J; Kong, E; Larraburo, Y; Rolle, C; Johnston, E; Gazzaley, A. Video game training enhances cognitive control in older adults. Nature; 2013; 501,
9. Hosseini, Z; Delpazirian, R; Lanjanian, H; Salarifar, M; Hassani-Abharian, P. Computer gaming and physiological changes in the brain: an insight from QEEG complexity analysis. Appl Psychophysiol Biofeedback; 2021; 46, pp. 301-8. [DOI: https://dx.doi.org/10.1007/s10484-021-09518-y]
10. Bakaoukas, AG; Coada, F; Liarokapis, F. Examining brain activity while playing computer games. J Multimodal User Interfaces; 2016; 10,
11. Hafeez, T; Umar Saeed, SM; Arsalan, A; Anwar, SM; Ashraf, MU; Alsubhi, K. EEG in game user analysis: a framework for expertise classification during gameplay. PLoS ONE; 2021; 16,
12. Abhishek AM, Suma HN. Stress analysis of a computer game player using electroencephalogram. In: International conference on circuits, communication, control and computing 2014 Nov 21 (pp. 25–28). IEEE.
13. Derbali L, Frasson C. Prediction of players motivational states using electrophysiological measures during serious game play. In: 2010 10th IEEE international conference on advanced learning technologies 2010 Jul 5 (pp. 498–502). IEEE.
14. Parsons, TD; McMahan, T; Parberry, I. Classification of video game player experience using consumer-grade electroencephalography. IEEE Trans Affect Comput; 2020; 13,
15. Xu, J; Zhong, B. Review on portable EEG technology in educational research. Comput Hum Behav; 2018; 81, pp. 340-9. [DOI: https://dx.doi.org/10.1016/j.chb.2017.12.037]
16. He EJ, Yuan H, Yang L, Sheikholeslami C, He B. EEG spatio-spectral mapping during video game play. In: 2008 international conference on information technology and applications in biomedicine 2008 May 30 (pp. 346–348). IEEE.
17. Teplan, M. Fundamentals of EEG measurement. Meas Sci Rev; 2002; 2,
18. Cui, R; Jiang, J; Zeng, L; Jiang, L; Xia, Z; Dong, L; Gong, D; Yan, G; Ma, W; Yao, D. Action video gaming experience related to altered resting-state EEG Temporal and Spatial complexity. Front Hum Neurosci; 2021; 15, 640329. [DOI: https://dx.doi.org/10.3389/fnhum.2021.640329]
19. Dale, G; Joessel, A; Bavelier, D; Green, CS. A new look at the cognitive neuroscience of video game play. Ann N Y Acad Sci; 2020; 1464,
20. Naumann L, Schultze-Kraft M, Dähne S, Blankertz B. Prediction of difficulty levels in video games from ongoing EEG. In: Symbiotic interaction: 5th international workshop, symbiotic 2016, Padua, Italy, September 29–30, 2016, Revised Selected Papers 5 2017 (pp. 125–136). Springer International Publishing.
21. Salminen, M; Ravaja, N. Oscillatory brain responses evoked by video game events: the case of super monkey ball 2. CyberPsychology Behav; 2007; 10,
22. Takahashi, N; Shinomiya, S; Mori, D; Tachibana, S. Frontal midline theta rhythm in young healthy adults. Clin Electroencephalogr; 1997; 28,
23. Jasper, H. The 10–20 electrode system of the international federation. Electroencephalogr Clin Neuropysiol; 1958; 10, pp. 370-5.
24. Subway Surfers. Is the top mobile game of the decade by downloads. Gamesindustry Biz 16 December 2019.
25. Jump up. to:a b Meisenzahl, Mary. ‘Subway Surfers’ was the most downloaded mobile game of the decade. See the top 10 here. Business Insider. Archived from the original on 26 June 2020. Retrieved 24 June 2020.
26. Shriram, R; Sundhararajan, M; Daimiwal, N. EEG based cognitive workload assessment for maximum efficiency. Int Organ Sci Res IOSR; 2013; 7, pp. 34-8.
27. Maiorana, E; Solé-Casals, J; Campisi, P. EEG signal preprocessing for biometric recognition. Mach Vis Appl; 2016; 27, pp. 1351-60. [DOI: https://dx.doi.org/10.1007/s00138-016-0804-4]
28. Nagata, K; Gross, CE; Kindt, GW; Geier, MJ; Adey, GR. Topographic electroencephalographic study with power ratio index mapping in patients with malignant brain tumors. Neurosurgery; 1985; 17,
29. Marcantoni, I; Assogna, R; Del Borrello, G; Di Stefano, M; Morano, M; Romagnoli, S; Leoni, C; Bruschi, G; Sbrollini, A; Morettini, M; Burattini, L. Ratio indexes based on spectral electroencephalographic brainwaves for assessment of mental involvement: a systematic review. Sensors; 2023; 23,
30. Chang, J; Choi, Y. Depression diagnosis based on electroencephalography power ratios. Brain Behav; 2023; 13,
31. Ostertagova, E; Ostertag, O; Kováč, J. Methodology and application of the Kruskal-Wallis test. Appl Mech Mater; 2014; 611, pp. 115-20. [DOI: https://dx.doi.org/10.4028/www.scientific.net/AMM.611.115]
32. Giannakos MN, Sharma K, Niforatos E. Exploring EEG signals during the different phases of game-player interaction. In: 2019 11th international conference on virtual worlds and games for serious applications (VS-Games) 2019 Sep 4 (pp. 1–8). IEEE.
33. Arain M, Haque M, Johal L, Mathur P, Nel W, Rais A, Sandhu R, Sharma S. Maturation of the adolescent brain. Neuropsychiatr Dis Treat. 2013:449–61.
34. Zhou, Q; Lin, J; Yao, L; Wang, Y; Han, Y; Xu, K. Relative power correlates with the decoding performance of motor imagery both across time and subjects. Front Hum Neurosci; 2021; 15, 701091. [DOI: https://dx.doi.org/10.3389/fnhum.2021.701091]
35. Buchsbaum, MS. Frontal cortex function. Am J Psychiatry; 2004; 161,
36. Saalmann, YB; Pinsk, MA; Wang, L; Li, X; Kastner, S. The pulvinar regulates information transmission between cortical areas based on attention demands. Science; 2012; 337,
37. Zhang, Y; Chen, Y; Bressler, SL; Ding, M. Response Preparation and inhibition: the role of the cortical sensorimotor beta rhythm. Neuroscience; 2008; 156,
38. Georgiadis K, van Oostendorp H, van der Pal J. EEG assessment of surprise effects in serious games. In: Games and learning alliance: 4th international conference, GALA 2015, Rome, Italy, December 9–11, 2015, Revised Selected Papers 4 2016 (pp. 517–529). Springer International Publishing.
39. Sood, SK; Singh, KD. An Optical-Fog assisted EEG‐based virtual reality framework for enhancing E‐learning through educational games. Comput Appl Eng Educ; 2018; 26,
40. Mondéjar, T; Hervás, R; Johnson, E; Gutiérrez-López-Franca, C; Latorre, JM. Analyzing EEG waves to support the design of serious games for cognitive training. J Ambient Intell Humaniz Comput; 2019; 10, pp. 2161-74. [DOI: https://dx.doi.org/10.1007/s12652-018-0841-0]
41. Sheikholeslami C, Yuan H, He EJ, Bai X, Yang L, He B. A high resolution EEG study of dynamic brain activity during video game play. In: 2007 29th annual international conference of the ieee engineering in medicine and biology society 2007 Aug 22 (pp. 2489–2491). IEEE.
42. Salminen, M; Ravaja, N. Increased oscillatory theta activation evoked by violent digital game events. Neurosci Lett; 2008; 435,
43. Malik AS, Osman DA, Pauzi AA, Khairuddin RH. Investigating brain activation with respect to playing video games on large screens. In: 2012 4th international conference on intelligent and advanced systems (ICIAS2012) 2012 Jun 12 (Vol. 1, pp. 86–90). IEEE.
44. Dexter, M; Ossmy, O. The effects of typical ageing on cognitive control: recent advances and future directions. Front Aging Neurosci; 2023; 15, 1231410. [DOI: https://dx.doi.org/10.3389/fnagi.2023.1231410]
45. Chan MY, Park DC, Savalia NK, Petersen SE, Wig GS. Decreased segregation of brain systems across the healthy adult lifespan. Proc Natl Acad Sci. 2014;111(46):E4997-5006.
46. Geerligs, L; Renken, RJ; Saliasi, E; Maurits, NM; Lorist, MM. A brain-wide study of age-related changes in functional connectivity. Cereb Cortex; 2015; 25,
47. Feinberg, I; Campbell, IG. Sleep EEG changes during adolescence: an index of a fundamental brain reorganization. Brain Cogn; 2010; 72,
48. Hashimoto, T; Nguyen, QL; Rotaru, D; Keenan, T; Arion, D; Beneyto, M; Gonzalez-Burgos, G; Lewis, DA. Protracted developmental trajectories of GABAA receptor α1 and α2 subunit expression in primate prefrontal cortex. Biol Psychiatry; 2009; 65,
49. Tan, E; Troller-Renfree, SV; Morales, S; Buzzell, GA; McSweeney, M; Antúnez, M; Fox, NA. Theta activity and cognitive functioning: integrating evidence from resting-state and task-related developmental electroencephalography (EEG) research. Dev Cogn Neurosci; 2024; 67, 101404. [DOI: https://dx.doi.org/10.1016/j.dcn.2024.101404]
50. Whitford, TJ; Rennie, CJ; Grieve, SM; Clark, CR; Gordon, E; Williams, LM. Brain maturation in adolescence: concurrent changes in neuroanatomy and neurophysiology. Hum Brain Mapp; 2007; 28,
51. Pellouchoud, E; Smith, ME; McEvoy, L; Gevins, A. Mental effort-related EEG modulation during video‐game play: comparison between juvenile subjects with epilepsy and normal control subjects. Epilepsia; 1999; 40, pp. 38-43. [DOI: https://dx.doi.org/10.1111/j.1528-1157.1999.tb00905.x]
52. Barriga-Paulino CI, Flores AB, Gómez CM. Developmental changes in the Eeg rhythms of children and young adults. J Psychophysiol. 2011.
53. Gasser, T; Verleger, R; Bächer, P; Sroka, L. Development of the EEG of school-age children and adolescents. I. Analysis of band power. Electroencephalogr Clin Neurophysiol; 1988; 69,
54. Clarke, AR; Barry, RJ; McCarthy, R; Selikowitz, M. Age and sex effects in the EEG: development of the normal child. Clin Neurophysiol; 2001; 112,
55. Niemarkt, HJ; Jennekens, W; Pasman, JW; Katgert, T; Van Pul, C; Gavilanes, AW; Kramer, BW; Zimmermann, LJ; Bambang Oetomo, S; Andriessen, P. Maturational changes in automated EEG spectral power analysis in preterm infants. Pediatr Res; 2011; 70,
56. Vaughan, L; Giovanello, K. Executive function in daily life: age-related influences of executive processes on instrumental activities of daily living. Psychol Aging; 2010; 25,
57. Park, DC; McDonough, IM. The dynamic aging mind: revelations from functional neuroimaging research. Perspect Psychol Sci; 2013; 8,
58. Diamond, A. Executive functions. Ann Rev Psychol; 2013; 64,
59. Dye, MW; Green, CS; Bavelier, D. Increasing speed of processing with action video games. Curr Dir Psychol Sci; 2009; 18,
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