Battle of minds: a new interaction approach in BCI games through competitive reinforcement

Sekhavat, Yoones A.

doi:10.1007/s11042-019-07963-w

“…Studies have demonstrated that a user can fully operate video games by SSVEP-BCI (van Vliet et al, 2012 ; Filiz and Arslan, 2020 ). Other studies have proposed how multiple users can participate in a collaborative game, in which joint decision making is required to control the gaming environment (Nijholt and Poel, 2016 ; Sekhavat, 2020 ). Another study previously suggested the aggregation of information from two intelligence analysts' brain signals may lead to better decision making than one's brain signals (Stoica, 2012 ).…”

Section: Neuroplasticity Sensors Signal Processing Modeling Anmentioning

confidence: 99%

Progress in Brain Computer Interface: Challenges and Opportunities

Saha

¹

,

Mamun

²

,

Ahmed

³

et al. 2021

Front. Syst. Neurosci.

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Brain computer interfaces (BCI) provide a direct communication link between the brain and a computer or other external devices. They offer an extended degree of freedom either by strengthening or by substituting human peripheral working capacity and have potential applications in various fields such as rehabilitation, affective computing, robotics, gaming, and neuroscience. Significant research efforts on a global scale have delivered common platforms for technology standardization and help tackle highly complex and non-linear brain dynamics and related feature extraction and classification challenges. Time-variant psycho-neurophysiological fluctuations and their impact on brain signals impose another challenge for BCI researchers to transform the technology from laboratory experiments to plug-and-play daily life. This review summarizes state-of-the-art progress in the BCI field over the last decades and highlights critical challenges.

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“…Thus, the time-domain EEG signal was represented by the power spectrum of its frequency bands [43]. The extracted PSD features from each channel included EEG frequency bands in the following ranges: delta (2-4 Hz), theta (5)(6)(7), alpha (8)(9)(10)(11)(12), beta (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29), and gamma (30)(31)(32)(33)(34)(35)(36)(37)(38)(39)(40)(41)(42)(43)(44)(45). These features were extracted for those EEG channels which resulted in a high correlation with the output class and hence were selected as discussed in Channel Selection section.…”

Section: Feature Extractionmentioning

confidence: 99%

“…Similarly, 2D and 3D EEG-based games such as brain chi , dancing robot , pipe , and escape are successfully deployed for concentration training purposes [ 26 ]. Recently, a multiplayer car racing game was proposed to improve attention level of a user [ 27 ]. In particular, it was shown that varying game difficulty in correspondence with the variation in the emotions of the game player helps in preserving their interest and maintains player engagement.…”

Section: Introductionmentioning

confidence: 99%

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

Hafeez

¹

,

Saeed

²

,

Arsalan

³

et al. 2021

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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 in providing an optimal gaming experience 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 and novice players’ brain activity is different, which can be classified using 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 lead us in identifying two significant EEG channels, i.e., AF3 and P7, among fourteen channels available in Emotiv EPOC headset. In particular, features extracted from these two EEG channels contributed 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 game player’s expertise level with a classification accuracy of up to 98.04% (without data balancing) and 98.33% (with data balancing).

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“…Similarly, 2D and 3D EEG-based games such as brain chi, dancing robot, pipe, and escape are successfully implemented for concentration training purposes [26]. Recently a multiplayer car racing game was proposed to improve the attention level of a user [27]. In particular, it was shown that varying game difficulty in correspondence with the variation in the emotions of the game player helps in preserving their interest and maintains player engagement.…”

Section: January 25 2021 2/21mentioning

confidence: 99%

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

Hafeez

¹

,

Saeed

²

,

Arsalan

³

et al. 2021

Preprint

4

0

View full text Add to dashboard Cite

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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Battle of minds: a new interaction approach in BCI games through competitive reinforcement

Cited by 8 publications

References 29 publications

Progress in Brain Computer Interface: Challenges and Opportunities

Progress in Brain Computer Interface: Challenges and Opportunities

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

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

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