A Game Player Expertise Level Classification System Using Electroencephalography (EEG)
Abstract
:1. Introduction
- EEG-based data are recorded from multiple participants during the play of a mobile game to automatically classify the player as expert or novice on the basis of brain activity.
- Those significant brain areas are highlighted and selected as being affected during the game play after a careful statistical analysis.
- Thirteen morphological features are extracted in the time domain for classification purposes.
2. Proposed Methodology
2.1. EEG Data Acquisition
2.1.1. Stimuli
2.1.2. Participants
2.1.3. Apparatus
2.1.4. Procedure
2.2. Preprocessing
2.3. Feature Extraction
- Maximum value ():
- Maximum value time ():
- Minimum value ():
- Minimum value time ():
- Maximum absolute value ():
- Peak to peak signal value ():
- Latency to maximum value ratio ():
- Latency to minimum value ratio ():
- Peak to peak time window ():
- Sum of values (S):
- Mean ():
- Signal power (P):
- Signal energy (E):
2.4. Classification
2.4.1. Support Vector Machine
2.4.2. Naive Bayes Classifier
2.4.3. Multilayer Perceptron
3. Experimental Results
3.1. Configuration and Parameter Settings
3.2. Channel Selection
3.3. Performance Analysis
4. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
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Player | T1 | R1 | T2 | R2 | T3 | R3 | T4 | R4 | T5 | Total Time (Minutes) |
---|---|---|---|---|---|---|---|---|---|---|
1 | 0.70 | 1 | 0.90 | 1 | 0.60 | 1 | 0.50 | 1 | 1.30 | 8 |
2 | 1.60 | 1 | 1.80 | 1 | 2.20 | 1 | 2.05 | 1 | 2.42 | 14.07 |
3 | 7.60 | 1 | 5.40 | 1 | 6.00 | 1 | 5.95 | 1 | 6.80 | 35.75 |
4 | 1.20 | 1 | 1.40 | 1 | 1.20 | 1 | 1.70 | 1 | 1.80 | 11.30 |
5 | 2.45 | 1 | 2.68 | 1 | 2.20 | 1 | 2.90 | 1 | 2.10 | 16.33 |
6 | 0.84 | 1 | 0.64 | 1 | 0.76 | 1 | 0.90 | 1 | 0.54 | 7.68 |
7 | 2.60 | 1 | 3.30 | 1 | 1.10 | 1 | 2.47 | 1 | 2.33 | 15.83 |
8 | 0.61 | 1 | 0.72 | 1 | 0.90 | 1 | 0.40 | 1 | 0.80 | 7.44 |
9 | 0.95 | 1 | 0.89 | 1 | 0.84 | 1 | 0.87 | 1 | 0.80 | 8.35 |
10 | 0.50 | 1 | 0.70 | 1 | 0.70 | 1 | 0.80 | 1 | 0.30 | 7.00 |
11 | 7.30 | 1 | 6.70 | 1 | 3.20 | 1 | 8.10 | 1 | 7.70 | 37.00 |
12 | 0.40 | 1 | 0.80 | 1 | 0.60 | 1 | 0.25 | 1 | 1.10 | 7.15 |
13 | 0.25 | 1 | 0.45 | 1 | 0.70 | 1 | 0.40 | 1 | 0.80 | 6.60 |
14 | 2.10 | 1 | 0.60 | 1 | 1.50 | 1 | 1.02 | 1 | 0.90 | 10.12 |
15 | 3.20 | 1 | 2.20 | 1 | 1.50 | 1 | 2.65 | 1 | 2.10 | 15.65 |
16 | 0.80 | 1 | 0.70 | 1 | 0.50 | 1 | 0.40 | 1 | 1.20 | 7.60 |
17 | 6.40 | 1 | 3.00 | 1 | 6.80 | 1 | 5.20 | 1 | 5.90 | 31.30 |
18 | 0.50 | 1 | 0.60 | 1 | 0.80 | 1 | 0.90 | 1 | 0.65 | 7.45 |
19 | 0.30 | 1 | 0.45 | 1 | 0.60 | 1 | 0.45 | 1 | 0.89 | 6.69 |
20 | 3.30 | 1 | 2.40 | 1 | 5.80 | 1 | 4.80 | 1 | 4.40 | 24.70 |
Number of Channels | Classification Algorithm | Correctly Classified | Incorrectly Classified | Time Taken (s) | kappa Statistics | Precision | Recall | ROC |
---|---|---|---|---|---|---|---|---|
4 channels | Naive Bayes | 84 | 16 | |||||
SVM | 86 | 14 | ||||||
MLP | 84 | 16 | ||||||
14- channels | Naive Bayes | 88 | 12 | |||||
SVM | 82 | 18 | ||||||
MLP | 82 | 18 |
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Anwar, S.M.; Saeed, S.M.U.; Majid, M.; Usman, S.; Mehmood, C.A.; Liu, W. A Game Player Expertise Level Classification System Using Electroencephalography (EEG). Appl. Sci. 2018, 8, 18. https://doi.org/10.3390/app8010018
Anwar SM, Saeed SMU, Majid M, Usman S, Mehmood CA, Liu W. A Game Player Expertise Level Classification System Using Electroencephalography (EEG). Applied Sciences. 2018; 8(1):18. https://doi.org/10.3390/app8010018
Chicago/Turabian StyleAnwar, Syed Muhammad, Sanay Muhammad Umar Saeed, Muhammad Majid, Saeeda Usman, Chaudhry Arshad Mehmood, and Wei Liu. 2018. "A Game Player Expertise Level Classification System Using Electroencephalography (EEG)" Applied Sciences 8, no. 1: 18. https://doi.org/10.3390/app8010018
APA StyleAnwar, S. M., Saeed, S. M. U., Majid, M., Usman, S., Mehmood, C. A., & Liu, W. (2018). A Game Player Expertise Level Classification System Using Electroencephalography (EEG). Applied Sciences, 8(1), 18. https://doi.org/10.3390/app8010018