A game player expertise level classification system using electroencephalography (EEG)

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.

[1]  Thomas D. Parsons,et al.  Modality specific assessment of video game player's experience using the Emotiv , 2015, Entertain. Comput..

[2]  Roger W. Remington,et al.  Not so fast: Rethinking the effects of action video games on attentional capacity , 2011 .

[3]  Mohammad Hassan Moradi,et al.  A new approach for EEG feature extraction in P300-based lie detection , 2009, Comput. Methods Programs Biomed..

[4]  Syed Muhammad Anwar,et al.  Human emotion recognition and analysis in response to audio music using brain signals , 2016, Comput. Hum. Behav..

[5]  Syed Muhammad Anwar,et al.  Mapping Brain Activity Using Wearable EEG Sensors for Mobile Applications , 2014 .

[6]  Niklas Ravaja,et al.  Oscillatory Brain Responses Evoked by Video Game Events: The Case of Super Monkey Ball 2 , 2007, Cyberpsychology Behav. Soc. Netw..

[7]  Rabab K Ward,et al.  A survey of signal processing algorithms in brain–computer interfaces based on electrical brain signals , 2007, Journal of neural engineering.

[8]  Matthew S Cain,et al.  Action video game experience reduces the cost of switching tasks , 2012, Attention, Perception, & Psychophysics.

[9]  Arnaud Delorme,et al.  EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis , 2004, Journal of Neuroscience Methods.

[10]  U. Rajendra Acharya,et al.  Non-linear analysis of EEG signals at various sleep stages , 2005, Comput. Methods Programs Biomed..

[11]  Rajesh P. N. Rao,et al.  Dynamic Bayesian Networks for Brain-Computer Interfaces , 2004, NIPS.

[12]  Camarin E. Rolle,et al.  Video game training enhances cognitive control in older adults , 2013, Nature.

[13]  Chuan-Hoo Tan,et al.  Enhancing User-Game Engagement Through Software Gaming Elements , 2014, J. Manag. Inf. Syst..

[14]  Jukka Heikkonen,et al.  A local neural classifier for the recognition of EEG patterns associated to mental tasks , 2002, IEEE Trans. Neural Networks.

[15]  V. Rideout,et al.  Generation M2: Media in the Lives of 8- to 18-Year-Olds , 2010 .

[16]  Syed Muhammad Anwar,et al.  Classification of Expert-Novice Level of Mobile Game Players Using Electroencephalography , 2016, 2016 International Conference on Frontiers of Information Technology (FIT).

[17]  Johan A. K. Suykens,et al.  Least Squares Support Vector Machine Classifiers , 1999, Neural Processing Letters.

[18]  M. Thenmozhi,et al.  FORECASTING STOCK INDEX RETURNS USING NEURAL NETWORKS , 2022 .

[19]  Syed Muhammad Anwar,et al.  Quantification of Human Stress Using Commercially Available Single Channel EEG Headset , 2017, IEICE Trans. Inf. Syst..

[20]  E Donchin,et al.  Brain-computer interface technology: a review of the first international meeting. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[21]  D. Avery,et al.  EEG and the Variance of Motor Evoked Potential Amplitude , 2006, Clinical EEG and neuroscience.

[22]  Anton Nijholt,et al.  Turning Shortcomings into Challenges: Brain-Computer Interfaces for Games , 2009, INTETAIN.

[23]  Cuntai Guan,et al.  Enhancement of Attention and Cognitive Skills using EEG based Neurofeedback Game , 2013 .

[24]  Abdul Wahab Abdul Rahman,et al.  Lesson Learnt from an EEG-Based Experiment with ADHD Children in Malaysia , 2016, HCI.

[25]  T. Sejnowski,et al.  Removing electroencephalographic artifacts by blind source separation. , 2000, Psychophysiology.

[26]  Lennart E. Nacke,et al.  Affective Ludology: Scientific Measurement of User Experience in Interactive Entertainment , 2009 .

[27]  H. N. Suma,et al.  Stress analysis of a computer game player using electroencephalogram , 2014, International Conference on Circuits, Communication, Control and Computing.

[28]  M Congedo,et al.  A review of classification algorithms for EEG-based brain–computer interfaces , 2007, Journal of neural engineering.

[29]  Chih-Hung Wu,et al.  Understanding the relationship between physiological signals and digital game-based learning outcome , 2014 .

[30]  Anton Nijholt,et al.  Experiencing BCI Control in a Popular Computer Game , 2013, IEEE Transactions on Computational Intelligence and AI in Games.

[31]  Christian Sebastian Loh,et al.  Predicting expert-novice performance as serious games analytics with objective-oriented and navigational action sequences , 2015, Comput. Hum. Behav..

[32]  Ji-Young An,et al.  Subconscious Learning via Games and Social Media , 2015, Healthcare Informatics Research.

[33]  Claude Frasson,et al.  Prediction of Players Motivational States Using Electrophysiological Measures during Serious Game Play , 2010, 2010 10th IEEE International Conference on Advanced Learning Technologies.

[34]  B. He,et al.  A High Resolution EEG Study of Dynamic Brain Activity during Video Game Play , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.