Skip to main content
SpringerLink
Log in

A Web Application for Characterizing Spontaneous Emotions Using Long EEG Recording Sessions

  • Chapter
  • First Online:
Innovations in Big Data Mining and Embedded Knowledge

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 159))

Abstract

Emotions are important in daily life and in several research fields, especially in Brain Computer Interface (BCI) and Affective Computing. Usually, emotions are studied by analyzing the brain activity of a subject, monitored by Electroencephalography (EEG), functional Magnetic Resonance Imaging (fMRI) or functional Near Infrared Spectroscopy (fNIRS), after some external stimulation. This approach could lead to characterization inaccuracies, due to the secondary activations produced by the artificial elicitation and to the subjective emotional response. In this work, we design a web application to support spontaneous emotions characterization. It is based on a database for EEG signals where a large amount of data from long recording sessions, collected from subjects during their daily life, are stored. In this way, EEG signals can be explored to characterize different spontaneous emotional states felt by several people. The application is also designed to extract features of specific emotions, and to compare different emotional states. Researchers all over the world could share both raw data and classification results. Since large datasets are treated, the application is based on strategies commonly used in big data managing. In particular, a column-oriented database is used to store a huge amount of raw EEG signals, while a relational database is employed to keep metadata information. A web application interface allows the user to communicate with the repository and a computational module performs the features extraction.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 159.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Wolpaw, J., Wolpaw, E.: Brain-Computer Interfaces. Oxford University Press, Oxford (2011)

    Google Scholar 

  2. Yoo, S., Fairneny, T., Chen, N., Choo, S., Panych, L., Park, H., Lee, S., Jolesz, F.: Brain–computer interface using fMRI: spatial navigation by thoughts. NeuroReport 15(10), 1591–1595 (2004)

    Article  Google Scholar 

  3. DeCharms, R., Maeda, F., Glover, G., Ludlow, D., Pauly, J., Soneji, D., Gabrieli, J., Mackey, S.: Control over brain activation and pain learned by using real-time functional MRI. Proc. Nat. Acad. Sci. 02(51), 18626–18631 (2005)

    Article  Google Scholar 

  4. Ruiz, S., Lee, S., Soekadar, S., Caria, A., Veit, R., Kircher, T., Birbaumer, N., Sitaram, R.: Acquired self-control of insula cortex modulates emotion recognition and brain network connectivity in schizophrenia. Hum. Brain Mapp. 34(1), 200–212 (2013)

    Article  Google Scholar 

  5. Sitaram, R., Zhang, H., Guan, C., Thulasidas, M., Hoshi, Y., Ishikawa, A., Shimizu, K., Birbaumer, N.: Temporal classification of multichannel near-infrared spectroscopy signals of motor imagery for developing a brain–computer interface. NeuroImage 34(4), 1416–1427 (2007)

    Article  Google Scholar 

  6. Schurholz, M., Rana, M., Robinson, N., Ramos-Murguialday, A., Cho, W., Rohm, M., Rupp, R., Birbaumer, N., Sitaram, R.: Differences in hemodynamic activations between motor imagery and upper limb FES with NIRS. In: IEEE Engineering in Medicine and Biology Society, Annual International Conference of the IEEE, pp. 4728–4731 (2012)

    Google Scholar 

  7. Rea, M., Rana, M., Lugato, N., Terekhin, P., Gizzi, L., Brotz, D., Fallgatter, A., Birbaumer, N., Sitaram, R., Caria, R.: Lower limb movement preparation in chronic stroke: a pilot study toward an fNIRS-BCI for gait rehabilitation. Neurorehabilitation Neural Repair 28(6), 564–575 (2014)

    Article  Google Scholar 

  8. Hwang, H., Kim, S., Choi, S., Im, C.: EEG-based brain-computer interfaces: a thorough literature survey. Int. J. Hum. Comput. Interact. 29(12), 814–826 (2013)

    Article  Google Scholar 

  9. Daly, J., Huggins, J.: Brain-computer interface: current and emerging rehabilitation applications. Arch. Phys. Med. Rehabil. 96(3), S1–S7 (2015)

    Article  Google Scholar 

  10. Placidi, G.: A smart virtual glove for the hand telerehabilitation. Comput. Biol. Med. 37(8), 1100–1107 (2007)

    Article  Google Scholar 

  11. Franchi, D., Maurizi, A., Placidi, G.: A Numerical Hand Model for a Virtual Glove Rehabilitation System, pp. 41–44 (2009)

    Google Scholar 

  12. Franchi, D., Maurizi, A., Placidi, G.: Characterization of a simmechanics model for a virtual glove rehabilitation system. In: 2nd International Symposium on Computational Modeling of Objects Represented in Images, Fundamentals, Methods and Applications 2010. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 141–150 (2010)

    Chapter  Google Scholar 

  13. Spezialetti, M., Avola, D., Placidi, G., De Gasperis, G.: Movement analysis based on virtual reality and 3D depth sensing camera for whole body rehabilitation. In: 3rd International Symposium on Computational Modelling of Objects Represented in Images: Fundamentals, Methods and Applications, Rome, Sept 2012, pp. 367–372 (2012)

    Google Scholar 

  14. Bisconti, S., Spezialetti, M., Placidi, G., Quaresima, V.: Functional near-infrared frontal cortex imaging for virtual reality neuro-rehabilitation assessment. In: 3rd International Symposium on Computational Modelling of Objects Represented in Images: Fundamentals, Methods and Applicationsp, Rome, Sept 2012, pp. 187–192

    Google Scholar 

  15. Placidi, G., Avola, D., Iacoviello, D., Cinque, L.: Overall design and implementation of the virtual glove. Comput. Biol. Med. 43(11), 1927–1940 (2013)

    Article  Google Scholar 

  16. Placidi, G., Avola, D., Ferrari, M., Iacoviello, D., Petracca, A., Quaresima, V., Spezialetti, M.: A low-cost real time virtual system for postural stability assessment at home. Comput. Methods Programs Biomed. 117(2), 322–333 (2014)

    Article  Google Scholar 

  17. Petracca, A., Carrieri, M., Avola, D., Basso Moro, S., Brigadoi, S., Lancia, S., Spezialetti, M., Ferrari, M., Quaresima, V., Placidi, G.: A virtual ball task driven by forearm movements for neuro-rehabilitation. In: 2015 International Conference on Virtual Rehabilitation Proceedings (ICVR), IEEE, pp. 162–163 (2015)

    Google Scholar 

  18. Moro, S.B., Carrieri, M., Avola, D., Brigadoi, S., Lancia, S., Petracca, A., Quaresima, V.: A novel semi-immersive virtual reality visuo-motor task activates ventrolateral prefrontal cortex: a functional near-infrared spectroscopy study. J. Neural Eng. 13(3), 036002 (2016)

    Article  Google Scholar 

  19. Avola, D., Cinque, L., Placidi, G.: A novel multimodal framework to support human-computer interaction. In: Proceedings of the 6th International Symposium of GIRPR, pp. 1–12 (2012)

    Google Scholar 

  20. Avola, D., Spezialetti, M., Placidi, G.: Design of an efficient framework for fast prototyping of customized human–computer interfaces and virtual environments for rehabilitation. Comput. Methods Programs Biomed. 110(3), 490–502 (2013)

    Article  Google Scholar 

  21. Picard, R.: Affective Computing. MIT Press, Cambridge, Mass. (1997)

    Google Scholar 

  22. Molina, G., Tsoneva, T., Nijholt, A.: Emotional brain-computer interfaces. Int. J. Auton. Adapt. Commun. Syst. 6(1), 9–25 (2013)

    Article  Google Scholar 

  23. Spezialetti, M., Cinque, L., João Manuel Tavares, R.S., Placidi, G.: Towards EEG-based BCI driven by emotions for addressing BCI-illiteracy: a meta-analytic review. Behav. Inf. Technol. 37(8), 855–871 (2018)

    Article  Google Scholar 

  24. Bos, D.O.: Eeg-based emotion recognition: the influence of visual and auditory stimuli. [Online]. http://hmi.ewi.utwente.nl/verslagen/capita-selecta/CS-Oude_Bos-Danny.pdf

  25. Wang, X., Nie, D., Lu, B.: Emotional state classification from EEG data using machine learning approach. Neurocomputing 129, 94–106 (2014)

    Article  Google Scholar 

  26. Stikic, M., Johnson, R., Tan, V., Berka, C.: EEG-based classification of positive and negative affective states. Brain-Comput. Interfaces 1(2), 99–112 (2014)

    Article  Google Scholar 

  27. Lang, P.J., Bradley, M.M., Cuthbert, B.N.: International affective picture system (IAPS): affective ratings of pictures and instruction manual. Univ. of Florida, Gainesville, FL, Tech. Rep. A-8 (2008)

    Google Scholar 

  28. Bradley, M.M., Lang, P.J.: International affective digitized sounds (IADS): stimuli, instruction manual and affective ratings. The Center for Research in Psychophysiology, Univ. of Florida, Gainesville, FL, Tech. Rep. B-2 (1999)

    Google Scholar 

  29. Avola, D., Cinque, L., Levialdi, S., Petracca, A., Placidi, G., Spezialetti, M.: Time-of-flight camera based virtual reality interaction for balance rehabilitation purposes. In: International Symposium Computational Modeling of Objects Presented in Images Springer, Cham, pp. 363–374 (2014)

    Google Scholar 

  30. Ferrari, M., Bisconti, S., Spezialetti, M., Basso Moro, S., Di Palo, C., Placidi, G., Quaresima, V.: Prefrontal cortex activated bilaterally by a tilt board balance task: a functional near-infrared spectroscopy study in a semi-immersive virtual reality environment. Brain Topogr. 27(3), 353–365 (2014)

    Article  Google Scholar 

  31. Basso Moro, S., Bisconti, S., Muthalib, M., Spezialetti, M., Cutini, S., Ferrari, M., Placidi, G., Quaresima, V.: A semi-immersive virtual reality incremental swing balance task activates prefrontal cortex: a functional near-infrared spectroscopy study. NeuroImage 85, 451–460 (2014)

    Article  Google Scholar 

  32. Avola, D., Cinque, L., Levialdi, S., Petracca, A., Placidi, G., Spezialetti, M.: Markerless hand gesture interface based on LEAP motion controller. In: DMS 2014, pp. 260–266 (2014)

    Google Scholar 

  33. Placidi, G., Avola, D., Petracca, A., Sgallari, F., Spezialetti, M.: Basis for the implementation of an EEG-based single-trial binary brain computer interface through the disgust produced by remembering unpleasant odors. Neurocomputing 160, 308–318 (2015)

    Article  Google Scholar 

  34. Iacoviello, D., Petracca, A., Spezialetti, M., Placidi, G.: A real time classification algorithm for EEG-based BCI driven by self induced emotions. Comput. Methods Programs Biomed. 122(3), 293–303 (2015)

    Article  Google Scholar 

  35. Iacoviello, D., Petracca, A., Spezialetti, M., Placidi, G.: A classification algorithm for electroencephalography signals by self-induced emotional stimuli. IEEE Trans. Cybern. 46(12), 3171–3180 (2015)

    Article  Google Scholar 

  36. Placidi, G., Petracca, A., Spezialetti, M., Iacoviello, D.: Classification strategies for a single-trial binary brain computer interface based on remembering unpleasant odors. In: IEEE Engineering in Medicine and Biology Society (2015) 37th Annual International Conference the IEEE, pp. 7019–7022 (2015)

    Google Scholar 

  37. Pistoia, F., Carolei, A., Iacoviello, D., Petracca, A., Sacco, S., Sarà, M., Spezialetti, M., Placidi, G.: EEG-detected olfactory imagery to reveal covert consciousness in minimally conscious state. Brain Inj. 29(13–14), 1729–1735 (2015)

    Article  Google Scholar 

  38. Placidi, G., Petracca, A., Spezialetti, M., Iacoviello, D.: A modular framework for EEG web based binary brain computer interfaces to recover communication abilities in impaired people. J. Med. Syst. 40(1), 34 (2015)

    Article  Google Scholar 

  39. Placidi, G., Cinque, L., Di Giamberardino, P., Iacoviello, D., Spezialetti, M.: An affective BCI driven by self-induced emotions for people with severe neurological disorders. International Conference on Image Analysis and Processing, pp. 155–162. Springer, Cham (2017)

    Google Scholar 

  40. Di Giamberardino, P., Iacoviello, D., Placidi, G., Polsinelli, M., Spezialetti, M.: A brain computer interface by EEG signals from self induced emotions. Lect. Notes Comput. Vis. Biomech. 27, 713–721 (2018)

    Article  Google Scholar 

  41. Iacoviello, D., Pagnani, N., Petracca, A., Spezialetti, M., Placidi, G.: A poll oriented classifier for affective brain computer interfaces. In: NEUROTECHNIX 2015—Proceedings of the 3rd International Congress on Neurotechnology, Electronics and Informatics, Lisbon, pp. 41–48 (2015)

    Google Scholar 

  42. Placidi, G., Di Giamberardino, P., Petracca, A., Spezialetti, M., Iacoviello, D.: Classification of emotional signals from the DEAP dataset. In: Proceedings of the 4th International Congress on Neurotechnology, Electronics and Informatics (NEUROTECHNIX 2016), Porto 7–8/11/2016, pp. 15–21 (2016)

    Google Scholar 

  43. Michel, V., Mazzola, L., Lemesle, M., Vercueil, L.: Long-term EEG in adults: sleep-deprived EEG (SDE), ambulatory EEG (Amb-EEG) and long-term video-EEG recording (LTVER). Neurophysiol. Clin./Clin. Neurophysiol. 45(1), 47–64 (2015)

    Article  Google Scholar 

  44. Agrawal, D., Das, S., El Abbadi, A.: Big data and cloud computing: current state and future opportunities. In: Proceedings of the 14th International Conference Extending Database Technology—EDBT/ICDT (2011)

    Google Scholar 

  45. Draper, N., Smith, H.: Applied Regression Analysis. Wiley, New York (1998)

    Book  Google Scholar 

  46. Kemp, B., Olivan, J.: European data format ‘plus’ (EDF+), an EDF alike standard format for the exchange of physiological data. Clin. Neurophysiol. 114(9), 1755–1761 (2003)

    Article  Google Scholar 

  47. Kemp, B.: European Data Format (EDF) (2015) Edfplus.info, [Online]. http://www.edfplus.info/

  48. Talia, D.: Clouds for scalable big data analytics. Computer 46(5), 98–101 (2013)

    Article  Google Scholar 

  49. Liu, H.: Big data drives cloud adoption in enterprise. IEEE Internet Comput. 17(4), 68–71 (2013)

    Article  Google Scholar 

  50. Han, J., Haihong, E., Guan, L., Du, J.: Survey on NoSQL database. In: Pervasive Computing and Applications (ICPCA), 6th International Conference on IEEE 2011, pp. 363–366 (2011)

    Google Scholar 

  51. Hecht, R., Jablonski, S.: NoSQL evaluation: a use case oriented survey. In: International Conference on Cloud and Service Computing (CSC). IEEE 2001, pp. 336–341 (2011)

    Google Scholar 

  52. Apache Team.: Apache HBase™ Reference Guide. Hbase.apache.org (2015). [Online]. https://hbase.apache.org/book.html

  53. Jurcak, V., Tsuzuki, D., Dan, I.: 10/20, 10/10, and 10/5 systems revisited: their validity as relative head-surface-based positioning systems. NeuroImage 34(4), 1600–1611 (2007)

    Article  Google Scholar 

  54. Oracle.com.: Oracle | Hardware and Software, Engineered to Work Together (2015). [online] http://www.oracle.com

  55. Spring.io.: spring.io (2015). [Online]. https://spring.io/

  56. Camras, L., Plutchik, R., Kellerman, H.: Emotion: theory, research, and experience. Vol. 1. Theories of emotion. Am. J. Psychol. 94(2), 370 (1981)

    Google Scholar 

  57. Leff, A., Rayfield, J.: Web-application development using the model/view/controller design pattern. In: Proceedings of the 5th IEEE International Enterprise Distributed Object Computing Conference (2001)

    Google Scholar 

  58. Hibernate.org.: Hibernate. Everything data. – Hibernate (2015). [Online]. http://hibernate.org/

  59. W3.org.: HTML5 (2015). [Online]. http://www.w3.org/TR/html5/

  60. W3.org.: Cascading Style Sheets (2015). [Online]. http://www.w3.org/Style/CSS/

  61. jquery.org, “jQuery”, Jquery.com.: (2015). [Online]. https://jquery.com/

  62. Marcotte, E.: Responsive Web Design (2010). Alistapart.com [Online]. http://alistapart.com/article/responsive-web-design

  63. W3.org.: “Extensible Markup Language (XML),” (2015). [Online]. http://www.w3.org/XML/

  64. Tools.ietf.org, “RFC 7159 - The JavaScript Object Notation (JSON) Data Interchange Format”, 2015. [Online]. http://tools.ietf.org/html/rfc7159

  65. Richardson, L., Ruby, S.: RESTful Web Services. O’Reilly, Farnham (2007)

    Google Scholar 

  66. Fielding, R., Taylor, R.: Principled design of the modern web architecture. In: Proceedings of the 22nd International Conference on Software Engineering (2000)

    Google Scholar 

  67. Henkin, R., Levy, L.: Lateralization of brain activation to imagination and smell of odors using functional magnetic resonance imaging (fMRI): left hemispheric localization of pleasant and right hemispheric localization of unpleasant odors. J. Comput. Assist. Tomogr. 25(4), 493–514 (2001)

    Article  Google Scholar 

  68. Niemic, C.: Studies of emotion: a theoretical and empirical review of psychophysiological studies of emotion. J. Undergrad. Res. 1(1), 15–18 (2002)

    Google Scholar 

  69. Balconi, M., Lucchiari, C.: Consciousness and arousal effects on emotional face processing as revealed by brain oscillations. A gamma band analysis. Int. J. Psychophysiol. 67(1), 41–46 (2008)

    Article  Google Scholar 

  70. Li, M., Lu, B.: Emotion classification based on gamma-band EEG. In: Engineering in Medicine and Biology Society, 2009. EMBC 2009. Annual International Conference of the IEEE, pp. 1223–1226 (2009)

    Google Scholar 

  71. Kassam, K., Markey, A., Cherkassky, V., Loewenstein, G., Just, M.: Identifying emotions on the basis of neural activation. PLoS ONE 8(6), e66032 (2013)

    Article  Google Scholar 

  72. Wang, L., Chen, D., Ranjan, R., Khan, S., KolOdziej, J., Wang, J.: In: 2012 IEEE 18th International Conference on Parallel and Distributed Systems (ICPADS). IEEE, pp. 164–171 (2012)

    Google Scholar 

  73. Dean, J., Ghemawat, S.: MapReduce: simplified data processing on large clusters. Commun. ACM 51(1), 107 (2008)

    Article  Google Scholar 

  74. Dean, J., Ghemawat, S.: MapReduce: a flexible data processing tool. Commun. ACM 53(1), 72 (2010)

    Article  Google Scholar 

  75. Hadoop.apache.org.: “Welcome to Apache™ Hadoop®!” (2015). [Online]. https://hadoop.apache.org/

  76. Berrada, G., van Keulen, M., Habib, M.: Hadoop for EEG storage and processing: a feasibility study. Brain Informatics and Health, pp. 218–230 (2014)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. A2VI-Lab, C/O Department of Life, Health and Environmental Sciences, University of L’Aquila, L’Aquila, Italy

    Giuseppe Placidi & Matteo Polsinelli

  2. Department of Computer Science, Sapienza University of Rome, Rome, Italy

    Luigi Cinque

Authors
  1. Giuseppe Placidi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Luigi Cinque
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Matteo Polsinelli
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Giuseppe Placidi .

Editor information

Editors and Affiliations

  1. Dipartimento di Psicologia and International Institute for Advanced Scientific Studies (IIASS), Università degli Studi della Campania “Luigi Vanvitelli”, Caserta, Italy

    Anna Esposito

  2. Sezione di Napoli, Osservatorio Vesuviano, Istituto Nazionale di Geofisica e Vulcanologia, Napoli, Italy

    Antonietta M. Esposito

  3. University of Canberra, Canberra, ACT, Australia

    Lakhmi C. Jain

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Placidi, G., Cinque, L., Polsinelli, M. (2019). A Web Application for Characterizing Spontaneous Emotions Using Long EEG Recording Sessions. In: Esposito, A., Esposito, A., Jain, L. (eds) Innovations in Big Data Mining and Embedded Knowledge. Intelligent Systems Reference Library, vol 159. Springer, Cham. https://doi.org/10.1007/978-3-030-15939-9_10

Download citation

Publish with us

Policies and ethics

Search

Navigation