Cognitive training can improve mental abilities, and a novel method to apply it is trough video games. There is controversy about the effectiveness of commercial video games for brain training, therefore it is necessary to assess the utility of these kinds of games. One quantitative method to assess it is electroencephalography (EEG), a non-invasive technique to study brain activity. This paper explores the use of EEG and video games together to find what are the most used techniques when analyzing the signals by means of a systematic review. From the results of that review two partial contributions were obtained: a taxonomy of techniques to analyze EEG signals, and a ranking of these techniques based on their popularity. The partial contributions were the departure point for working in the main contribution of this paper: eeglib, a Python library for analyzing EEG. The library was tested technically and functionally. The technical test was oriented to assess the correct output of certain algorithms, while the functional one consisted in analyzing data from two different experiments to check the effectiveness of the library.
This is a preview of subscription content, access via your institution.
Buy single article
Instant access to the full article PDF.
Tax calculation will be finalised during checkout.
A Consensus on the Brain Training Industry from the Scientific Community (Summary) – Stanford Center on Longevity. http://longevity.stanford.edu/a-consensus-on-the-brain-training-industry-from-the-scientific-community Accessed 28 May 2019.
Aydore S, Pantazis D, Leahy RM (2013) A note on the phase locking value and its properties. Neuroimage 74:231–244. https://doi.org/10.1016/j.neuroimage.2013.02.008
Bai O, Lin P, Huang D, Fei D-Y, Floeter MK (2010) Towards a user-friendly brain-computer interface: initial tests in ALS and PLS patients. Clin Neurophysiol 121(8):1293–1303. https://doi.org/10.1016/j.clinph.2010.02.157
Ball K, Berch DB, Helmers KF, Jobe JB, Leveck MD et al (2002) Effects of Cognitive Training Interventions With Older Adults. JAMA 288(18):2271–2281
Berta R, Bellotti F, De G, Pranantha D, Schatten C (2013) Electroencephalogram and physiological signal analysis for assessing flow in games. IEEE Trans Computat Intell AI Games 5(2):164–175. https://doi.org/10.1109/tciaig.2013.2260340
Blackman RB, Tukey JW (1958) The measurement of power spectra from the point of view of communications engineering—part I. Bell Syst Tech J 37(1):185–282. https://doi.org/10.1002/j.1538-7305.1958.tb03874.x
Cabañero-Gómez L, Hervas R, Bravo J, Rodriguez-Benitez L (2018) Computational EEG analysis techniques when playing video games: a systematic review. In: Proceedings 2(19): 483. https://doi.org/10.3390/proceedings2190483
Chanel G, Rebetez C, Bétrancourt M, Pun T (2011) Emotion assessment from physiological signals for adaptation of game difficulty. IEEE Trans Syst Man Cybern Part A Syst Hum 41(6):1052–1063. https://doi.org/10.1109/tsmca.2011.2116000
ChatterjeeD, Sinharay A, Pal A (2014) Cognitive load detection on commercial eeg devices: an optimized signal processing chain
Cognitive Training Data Response Letter. Cognitive Training Data. https://www.cognitivetrainingdata.org/the-controversy-does-brain-training-work/response-letter/(accessed 28 May 2019
Comon P (1994) Independent component analysis, A new concept? Signal Process 36(3):287–314. https://doi.org/10.1016/0165-1684(94)90029-9
Cooley JW, Tukey JW (1965) An Algorithm for the Machine Calculation of Complex Fourier Series. Mathematics of Computation 19(90):297–301. https://doi.org/10.2307/2003354
Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273–297
Finke A, Lenhardt A, Ritter H (2009) The MindGame: a P300-based brain-computer interface game. Neural Networks 22(9):1329–1333. https://doi.org/10.1016/j.neunet.2009.07.003
Fisher RA (1936) The use of multiple measurements in taxonomic problems. Ann Eugenics 7(2):179–188. https://doi.org/10.1111/j.1469-1809.1936.tb02137.x
Grossmann A, Morlet J (1984) Decomposition of Hardy Functions into Square Integrable Wavelets of Constant Shape. SIAM J Math Anal 15(4):723–736. https://doi.org/10.1137/0515056
Herrmann B (2019a) Detrended fluctuation analysis. http://bjornherrmann.com/DetrendedFluctuationAnalysis.html Accessed 28 May 2019.
Herrmann B (2019b) Multi-scale entropy. http://bjornherrmann.com/MultiScaleEntropy.html Accessed 28 May 2019.
Hervás R, Ruiz-Carrasco D, Mondéjar T, Bravo J (2017) Gamification mechanics for behavioral change: a systematic review and proposed taxonomy. 11th EAI International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth 2017). Workshop on Health-i-coach intelligent technologies for coaching in health. Barcelona (Spain) 23-26 May, 2017. ACM
Higuchi T (1988) Approach to an irregular time series on the basis of the fractal theory. Physica D 31(2):277–283. https://doi.org/10.1016/0167-2789(88)90081-4
Huang D, Qian K, Fei D-Y, Jia W, Chen X et al (2012) Electroencephalography (EEG)-based brain-computer interface (BCI): a 2-D virtual wheelchair control based on event-related desynchronization/synchronization and state control. IEEE Trans Neural Syst Rehabil Eng 20(3):379–388. https://doi.org/10.1109/tnsre.2012.2190299
Johnny CL, Tan DS (2006) Using a low-cost electroencephalograph for task classification in HCI research. p. 81–90
Johnson E, Hervás R, Gutiérrez-López-Franca C, Mondéjar T, Ochoa SF, Favela J (2018) Assessing empathy and managing emotions through interactions with an affective avatar. J Health Inform 24(2):182–193. https://doi.org/10.1177/1460458216661864
Klimesch W (1999) EEG alpha and theta oscillations reflect cognitive and memory performance: a review and analysis. Brain Res Rev 29(2–3):169–195. https://doi.org/10.1016/s0165-0173(98)00056-3
Konstantinidis EN, Conci G, Bamparopoulos EA, Sidiropoulos F, De Natale et al. (2015) Introducing Neuroberry, a platform for pervasive EEG signaling in the IoT domain
Lalor EC, Kelly SP, Finucane C, Burke R, Smith R et al (2005) Steady-state VEP-based brain-computer interface control in an immersive 3D gaming environment. Eurasip J Appl Signal Process 2005(19):3156–3164. https://doi.org/10.1155/asp.2005.3156
Lempel A, Ziv J (1976) On the Complexity of Finite Sequences. IEEE Trans Inf Theory 22(1):75–81. https://doi.org/10.1109/tit.1976.1055501
Luck SJ (2005) An introduction to the event-related potential technique. MIT Press, Cambridge, MA
Mandelbrot BB (1982) The fractal geometry of nature. W. H. Freeman, San Francisco
Menezes MLR, Samara A, Galway L, Sant’Anna A, Verikas A et al (2017) Towards emotion recognition for virtual environments: an evaluation of eeg features on benchmark dataset. Pers Ubiquit Comput 21(6):1003–1013. https://doi.org/10.1007/s00779-017-1072-7
Millán JDR, Ferrez PW, Galán F, Lew E, Chavarriaga R (2008) Non-invasive brain-machine interaction. Int J Pattern Recognit Artif Intell 22(5):959–972. https://doi.org/10.1142/s0218001408006600
Mondéjar T, Hervás R, Johnson E, Gutierrez-López-Franca C, Latorre JM (2016) Correlation between videogame mechanics and executive functions through EEG analysis. J Biomed Inform 63:131–140. https://doi.org/10.1016/j.jbi.2016.08.006
Mondéjar T, Hervás R, Johnson E, Gutiérrez-López-Franca C, Latorre JM (2019) Analyzing EEG waves to support the design of serious games for cognitive training. J Ambient Intell Human Comput 10(6):2161–2174. https://doi.org/10.1007/s12652-018-0841-0
Mu Y, Guo C, Han S (2016) Oxytocin enhances inter-brain synchrony during social coordination in male adults. Soc Cognit Affect Neurosci 11(12):1882–1893
Müller M (2007) Dynamic time warping. information retrieval for music and motion. Springer, Berlin, Heidelberg pp: 69–84.
Pascual-Marqui RD (2002) Standardized low-resolution brain electromagnetic tomography (sLORETA): technical details. Methods Find Exp Clin Pharmacol 24:5–12
Pearson K (1901) LIII On lines and planes of closest fit to systems of points in space. The London, Edinburgh, and Dublin Philosophical Magazine. J Sci 2(11):559–572. https://doi.org/10.1080/14786440109462720
Peng CK, Buldyrev SV, Havlin S, Simons M, Stanley HE et al (1994) Mosaic organization of DNA nucleotides. Phys Rev E Stat Phys Plasmas Fluids Relat Interdiscip Topics 49(2):1685–1689
Pregenzer MD, Flotzinger, Pfurtscheller G (1994) Distinction Sensitive Learning Vector Quantisation-a new noise-insensitive classification method., 1994 IEEE International Conference on Neural Networks, 1994. IEEE World Congress on Computational Intelligence. p. 2890–2894.
Raghavendra BS, Dutt D (2010) Computing fractal dimension of signals using multiresolution box-counting method. World Acad Sci Eng Technol 37:1266–1281
Rao CR (1948) The utilization of multiple measurements in problems of biological classification. J R Stat Soc 10(2):159–203
Reuderink B, Nijholt A, Poel M (2009) Affective Pacman: a frustrating game for brain-computer interface experiments. Intelligent technologies for interactive entertainment. Springer, Berlin, pp 221–227
Richman JS, Moorman JR (2000) Physiological time-series analysis using approximate entropy and sample entropy. Am J Physiol-Heart Circul Physiol 278(6):H2039–H2049. https://doi.org/10.1152/ajpheart.2000.278.6.h2039
Russoniello CV, O’Brien K, Parks JM (2009) The effectiveness of casual video games in improving mood and decreasing stress. J Cyber Ther Rehabilit 2(1):53–66
Scherer R, Lee F, Schlögl A, Leeb R, Bischof H et al (2008) Toward self-paced brain-computer communication: navigation through virtual worlds. IEEE Trans Biomed Eng 55(2):675–682. https://doi.org/10.1109/tbme.2007.903709
Wang Q, Sourina O, Nguyen MK (2011) Fractal dimension based neurofeedback in serious games. Vis Comput 27(4):299–309. https://doi.org/10.1007/s00371-011-0551-5
Willis SL, Schaie KW (2009) Cognitive training and plasticity: theoretical perspective and methodological consequences. Restor Neurol Neurosci 27(5):375–389. https://doi.org/10.3233/rnn-2009-0527
Zhang H (2004) The Optimality of Naïve Bayes. In FLAIRS2004 conference
Zhang C, Wang H, Wu M-H (2013) EEG-based expert system using complexity measures and probability density function control in alpha sub-band. Integr Comput-Aided Eng 20(4):391–405. https://doi.org/10.3233/ica-130439
This research was funded by Ministry of Science, Innovation and Universities Grant No [RTI2018-098,780-B-I00].
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
About this article
Cite this article
Cabañero, L., Hervás, R., Bravo, J. et al. eeglib: computational analysis of cognitive performance during the use of video games. J Ambient Intell Human Comput (2019). https://doi.org/10.1007/s12652-019-01592-9
- Video games
- Cognitive performance
- Computation techniques
- Serious games