Abstract
The electroencephalograph (EEG) signal is one of the most widely used signal in the field of computer science to analyze the electrical brain waves from software developers and students. In this paper we present initial research results of an empirical study related to application of EEG in measurement of software development activities. We discuss existing methods and problems of running such experiments in future. In particular, we focus on the different kinds of limitations implied by modern EEG devices as well as the issues related to evaluation of the collected data set.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Pizzagalli, D.A.: Electroencephalography and high-density electrophysiological source localization. Handb. Psychophysiol. 3, 56–84 (2007)
Klimesch, W.: Memory processes, brain oscillations and EEG synchronization. Int. J. Psychophysiol. 24(1–2), 61–100 (1996)
Sillitti, A., Succi, G., Vlasenko, J.: Understanding the impact of pair programming on developers attention: a case study on a large industrial experimentation. In: 2012 34th International Conference on Software Engineering (ICSE), pp. 1094–1101. IEEE, June 2012
Raziq, A., Maulabakhsh, R.: Impact of working environment on job satisfaction. Proc. Econ. Finance 23, 717–725 (2015)
Doborjeh, Z.G., Kasabov, N., Doborjeh, M.G., Sumich, A.: Modelling peri-perceptual brain processes in a deep learning spiking neural network architecture. Sci. Rep. 8(1), 8912 (2018)
Keil, A., et al.: Committee report: publication guidelines and recommendations for studies using electroencephalography and magnetoencephalography. Psychophysiology 51(1), 1–21 (2014)
Xu, J., Mitra, S., Van Hoof, C., Yazicioglu, R.F., Makinwa, K.A.: Active electrodes for wearable EEG acquisition: review and electronics design methodology. IEEE Rev. Biomed. Eng. 10, 187–198 (2017)
Delorey, D.P., Knutson, C.D., Chun, S.: Do programming languages affect productivity? A case study using data from open source projects. In: First International Workshop on Emerging Trends in FLOSS Research and Development (FLOSS 2007: ICSE Workshops 2007), p. 8. IEEE, May 2007
Bell, M.A., Cuevas, K.: Using EEG to study cognitive development: issues and practices. J. Cogn. Dev. 13(3), 281–294 (2012)
Wendel, K., et al.: EEG/MEG source imaging: methods, challenges, and open issues. Comput. Intell. Neurosci. 2009, 13 (2009)
Puce, A., Hämäläinen, M.: A review of issues related to data acquisition and analysis in EEG/MEG studies. Brain Sci. 7(6), 58 (2017)
Jiang, X., Bian, G.B., Tian, Z.: Removal of artifacts from EEG signals: a review. Sensors 19(5), 987 (2019)
Das, S., Tripathy, D., Raheja, J.L.: An insight to the human brain and EEG. In: Real-Time BCI System Design to Control Arduino Based Speed Controllable Robot Using EEG. BRIEFSAPPLSCIENCES, pp. 13–24. Springer, Singapore (2019). https://doi.org/10.1007/978-981-13-3098-8_2
Bigdely-Shamlo, N., et al.: Hierarchical Event Descriptors (HED): semi-structured tagging for real-world events in large-scale EEG. Front. Neuroinform. 10, 42 (2016)
Züger, M., Fritz, T.: Interruptibility of software developers and its prediction using psycho-physiological sensors. In: Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems, pp. 2981–2990. ACM, April 2015
Kosmyna, N., Lécuyer, A.: A conceptual space for EEG-based brain-computer interfaces. PLoS ONE 14(1), e0210145 (2019)
Müller, S.C., Fritz, T.: Stuck and frustrated or in flow and happy: sensing developers’ emotions and progress. In: 2015 IEEE/ACM 37th IEEE International Conference on Software Engineering, vol. 1, pp. 688–699. IEEE, May 2015
Chaparro, E.A., Yuksel, A., Romero, P., Bryant, S.: Factors affecting the perceived effectiveness of pair programming in higher education. In: PPIG, p. 2, June 2005
Lesiuk, T.: The effect of music listening on work performance. Psychol. Music 33(2), 173–191 (2005)
Nanz, S., Furia, C.A.: A comparative study of programming languages in Rosetta code. In: 2015 IEEE/ACM 37th IEEE International Conference on Software Engineering, vol. 1, pp. 778–788. IEEE, May 2015
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Tarasau, H., Thapaliya, A., Zufarova, O. (2019). Problems in Experiment with Biological Signals in Software Engineering: The Case of the EEG. In: Mazzara, M., Bruel, JM., Meyer, B., Petrenko, A. (eds) Software Technology: Methods and Tools. TOOLS 2019. Lecture Notes in Computer Science(), vol 11771. Springer, Cham. https://doi.org/10.1007/978-3-030-29852-4_6
Download citation
DOI: https://doi.org/10.1007/978-3-030-29852-4_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-29851-7
Online ISBN: 978-3-030-29852-4
eBook Packages: Computer ScienceComputer Science (R0)