A Game Player Expertise Level Classification System Using Electroencephalography (EEG)

Abstract

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.

EEG Based Classification of Long-Term Stress Using Psychological Labeling

“…The Welch method was used to extract power spectral densities with a window length of 128 samples with 50 percent overlap. For feature extraction, power spectral densities of different neural oscillations namely, delta (1-3 Hz), theta (4-7 Hz), alpha (8-12 Hz), beta (13-30 Hz), gamma (25-43 Hz), slow (4-13 Hz), and low beta (13)(14)(15)(16)(17) were computed from each channel. Relative gamma waves were computed by taking the ratio of slow and gamma waves.…”

“…Recently, wearable systems were developed that can record electro-physiological signals (such as EEG and heart rate variability) to detect acute stress [12]. EEG is one of the most common source of information for studying brain function [13][14][15][16][17]. The oscillations generated by the variation of electric potential in the brain are recorded using low resistance electrodes placed on the human scalp [18].…”

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

“…While [40], found that the player's expertise and competency can be better classified using features from the January 25, 2021 3/21 EEG signals and regression-based ridge estimator classifier. In [41,42], mobile game players' expertise was classified using EEG signals and machine learning algorithms,…”

Design of a Brain-Controlled Video Game based on a BCI System

“…As far as brain-controlled video game technology is concerned, it can be used for both medical and non-medical purposes: to recover limb motricity in case of brain level trauma or in case of suffering by a neurologic disease such as stroke or severe neurodegenerative disease using generation of motor imagery based on BCI commands; as assistive technology for persons with mobility impairment such as spinal cord injuries who otherwise are not able to perform game controls; for relaxation and entertainment, in many cases used to measure the concentration level of a player [5,6,7].…”