Abstract
Electroencephalography is proposed as an alternative for evaluating user experience with two interactive systems. Traditionally, evaluation methods are applied either during the interaction, which can disturb the user, or at the end where the user does not usually remember all of their interactions. Therefore, using a BCI (Brain Computer Interface) device as OPEN-BCI as an alternative to evaluate the user experience of a subject while interacts with two interactive systems. In this evaluation were analyzed the sub-bands: alpha, theta and beta. The results show differences in workload and emotions. In addition, consistent analysis of the EEG data, were applied questionnaires, as: SUS, NASA-TLX y SAM, where data showed high consistency.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Frey, J., Mühl, C., Lotte, F., Hachet, M.: Review of the use of electroencephalography as an evaluation method for human-computer interaction, arXiv (2013)
Subha, D.P., Joseph, P.K., Acharya, U.R., et al.: EEG signal analysis: a survey. J. Med. Syst. 34, 195–212 (2010). https://doi.org/10.1007/s10916-008-9231-z
Kragel, P.A., LaBar, K.S.: Decoding the nature of emotion in the brain. Trends Cogn. Sci. 20(6), 444–455 (2016). https://doi.org/10.1016/j.tics.2016.03.011
Herrmann, C.S., Strüber, D., Helfrich, R.F., Engel, A.K.: EEG oscillations: from correlation to causality. Int. J. Psychophysiol. 103, 12–21 (2016)
Klimesch, W.: EEG alpha and theta oscillations reflect cognitive and memory performance: A review and analysis. Brain Res. Rev. 29(2–3), 169–195 (1999)
Sulthan, N., Mohan, N., Khan, K.A., Sofiya, S., Shanir, P.P.M.: Emotion recognition using brain signals. In: 2018 International Conference on Intelligent Circuits and Systems (ICICS), Phagwara, pp. 315–319 (2018). https://doi.org/10.1109/ICICS.2018.00071
Cano, S., Araujo, N., Guzman, C., Rusu, C., Albiol-Pérez, S.: Low-cost assessment of user eXperience through EEG signals. IEEE Access 8, 158475–158487 (2020). https://doi.org/10.1109/ACCESS.2020.3017685
Figueira, J.S.B., David, I., Lobo, I., et al.: Effects of load and emotional state on EEG alpha-band power and inter-site synchrony during a visual working memory task. Cogn. Affect Behav. Neurosci. 20, 1122–1132 (2020). https://doi.org/10.3758/s13415-020-00823-3
Xiao, D., Zhang, W.: Electroencephalogram based brain concentration and its human computer interface application. In: 2015 IEEE International Conference on Computer and Communications (ICCC), Chengdu, pp. 21–24 (2015). https://doi.org/10.1109/CompComm.2015.7387533
Lahane, P., Sangaiah, A.K.: An approach to EEG based emotion recognition and classification using kernal density estimation. Procedia Comput. Sci. 48, 574–581 (2015)
Sohaib, A.T., Qureshi, S., Hagelbäck, J., Hilborn, O., Jerčić, P.: Evaluating classifiers for emotion recognition using EEG. In: Schmorrow, D.D., Fidopiastis, C.M. (eds.) Foundations of Augmented Cognition. AC 2013. Lecture Notes in Computer Science, vol. 8027. Springer, Berlin (2013). https://doi.org/10.1007/978-3-642-39454-6_53
Fok, S., Schwartz, R., Wronkiewicz, M., Holmes, C., Zhang, J., Somers, T., Bundy, D., Leuthardt, E.: An EEG-based brain computer interface for rehabilitation and restoration of hand control following stroke using ipsilateral cortical physiology. In: Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, pp. 6277–6280 (2011). https://doi.org/10.1109/IEMBS.2011.6091549
Ergonomics of Human-System Interaction_Part 11: Usability: Definitions and Concepts, ISO Standard 9241-11, International Standardization Organization (ISO), Geneva, Switzerland (2018)
Nielsen, J.: Usability inspection methods. In: Conference Companion on Human Factors in Computing Systems, pp. 413–414. ACM (1994)
Beaudouin-Lafon, M.: Interaction instrumentale: de la manipulation directe à la réalité augmentée. In: Actes des Neuvièmes Journées sur l'Interaction Homme-Machine, IHM 1997 (1997)
Abiri, R., Borhani, S., Kilmarx, J., Esterwood, C., Jiang, Y., Zhao, X.: A usability study of low-cost wireless brain-computer interface for cursor control using online linear model. IEEE Trans. Hum.-Mach. Syst. 50(4), 287–297 (2020). https://doi.org/10.1109/THMS.2020.2983848
Herwig, U., Satrapi, P., Schönfeldt-Lecuona, C.: Using the international 10–20 EEG system for positioning of transcranial magnetic stimulation. Brain Topogr. 16, 95–99 (2003)
Niedermeyer, E., da Silva, F.L.: Electroencephalography: basic principles, clinical applications, and related fields. Lippincott Williams & Wilkins (2005). A New EEG Acquisition Protocol for Biometric Identification Using Eye Blinking Signals. https://www.researchgate.net/publication275830679_A_New_EEG_Acquisition_Protocol_for_Biometric_Identification_Using_Eye_Blinking_Signals. Accessed 03 Jan 2021
Saby, J.N., Marshall, P.J.: The utility of EEG band power analysis in the study of infancy and early childhood. Dev. Neuropsychol. 37(3), 253–273 (2012)
Lin, Y., Liu, Z., Gao, X.: Alpha-band oscillation during speech recognition under different sensory conditions. In: 2015 8th International Conference on Biomedical Engineering and Informatics (BMEI), pp. 153–157. IEEE (2015)
Gola, M., Magnuski, M., Szumska, I., Wrobel, A.: EEG beta band activity is related to attention and attentional deficits in the visual performance of elderly subjects. Int. J. Psychophysiol. 89(3), 334–341 (2013)
Miltner, W.H., Braun, C., Arnold, M., Witte, H., Taub, E.: Coherence of gamma-band EEG activity as a basis for associative learning. Nature 397(6718), 434 (1999)
Calcagno, A., et al.: EEG monitoring during software development. In: 2020 IEEE 20th Mediterranean Electrotechnical Conference (MELECON), Palermo, Italy, pp. 325–329 (2020). https://doi.org/10.1109/MELECON48756.2020.9140717
Negi, S., Mitra, R.: EEG metrics to determine cognitive load and affective states: a pilot study. In: Proceedings of the 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers (UbiComp 2018), pp. 182–185. Association for Computing Machinery, New York (2018). https://doi.org/10.1145/3267305.3267618
Lan, T., Erdogmus, D., Adami, A., Mathan, S., Pavel, M.: Channel selection and feature projection for cognitive load estimation using ambulatory EEG. Comput. Intell. Neurosci. 2007 (2007). https://doi.org/10.1155/2007/74895.Article no. 74895. PMID: 18364990, PMCID: PMC2267884
Roland, P.E.: Brain activation. Wiley-Liss, New York (1993)
Peng, H., Hu, B., Zheng, F., et al.: A method of identifying chronic stress by EEG. Pers. Ubiquit. Comput. 17, 1341–1347 (2013). https://doi.org/10.1007/s00779-012-0593-3
Kawala-Janik, A., Pelc, M., Podpora, M.: Method for EEG signals pattern recognition in embedded systems. Elektronika Ir Elektrotechnika 21(3), 3–9 (2015). https://doi.org/10.5755/j01.eee.21.3.9918
Herrmann, M.J., Huter, T., Plichta, M.M., Ehlis, A.-C., Alpers, G.W., Mühlberger, A., Fallgatter, A.J.: Enhancement of activity of the primary visual cortex during processing of emotional stimuli as measured with event-related functional near-infrared spectroscopy and event-related potentials. Hum. Brain Mapp. 29, 28–35 (2008). https://doi.org/10.1002/hbm.20368
Gevins, A., Smith, M.E.: Neurophysiological measures of cognitive workload during human-computer interactions. Theoret. Issues Ergon. Sci. 4, 113–131 (2003)
Klimesch, W., Schack, B., Sauseng, P.: The functional significance of theta and upper alphaoscillations for working memory: a review. Exp. Psychol. 52, 99–108 (2005)
Stelios, X., Aristea, T.: Studying student’s attitudes on using examples of game source code for learning programming. Inform. Educ. Int. J. 2, 265–277 (2014)
Bradley, M.M., Lang, P.J.: Measuring emotion: the self-assessment manikin and the semantic differential. J. Behav. Ther. Exp. Psychiatry 25(I), 49–59 (1994)
Hart, S.G., Staveland, L.E.: Development of NASA-TLX (Task Load Index): results of empirical and theoretical research. In: Hancock, P.A., Meshkati, N. (eds.) Human Mental Workload. North Holland Press, Amsterdam (1988)
Brooke, J.: SUS – a quick and dirty usability scale. In: Usability Evaluation in Industry, pp. 189, 194 (1996)
Lang, P.J., Greenwald, M.K., Bradley, M.M., Hamm, A.O.: Looking at pictures: evaluative, facial, visceral, and behavioral responses. Psychophysiology 30(3), 261–273 (1993)
Dhiman, R., Priyanka, Saini, J.S.: Wavelet analysis of electrical signals from brain: the electroencephalogram. In: Singh, K., Awasthi, A.K. (eds.) Quality, Reliability, Security and Robustness in Heterogeneous Networks. QShine 2013. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol. 115. Springer, Berlin (2013). https://doi.org/10.1007/978-3-642-37949-9_24
Omerhodzic, I., Avdakovic, S., Nuhanovic, A., Dizdarevic, K.: Energy distribution of EEG signals: EEG signal wavelet-neural network classifier. arxiv.org/abs/1307.7897 (2013)
Jacob, J.E., Nair, G.K., Iype, T., Cherian, A.: Diagnosis of encephalopathy based on energies of EEG subbands using discrete wavelet transform and support vector machine. Neurol. Res. Int. (2018). 1613456. https://doi.org/10.1155/2018/1613456
Klimesch, W., Schimke, H., Pfurtscheller, G.: Alpha frequency, cognitive load and memory performance. Brain Topogr. 5(3), 241–251 (1993)
Sanei, S., Chambers, J.A.: EEG Signal Processing. Wiley, Chichester (2007)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Cano, S., Soto, J., Acosta, L., Peñeñory, V., Moreira, F. (2021). Electroencephalography as an Alternative for Evaluating User eXperience in Interactive Systems. In: Rocha, Á., Adeli, H., Dzemyda, G., Moreira, F., Ramalho Correia, A.M. (eds) Trends and Applications in Information Systems and Technologies. WorldCIST 2021. Advances in Intelligent Systems and Computing, vol 1365. Springer, Cham. https://doi.org/10.1007/978-3-030-72657-7_42
Download citation
DOI: https://doi.org/10.1007/978-3-030-72657-7_42
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-72656-0
Online ISBN: 978-3-030-72657-7
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)