EEG Signals for Measuring Cognitive Development

A Study of EEG Signals Challenges and Prospects
  • Swati AggarwalEmail author
  • Prakriti Bansal
  • Sameer Garg
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11278)


The use of EEG signals for measuring cognitive development is an upcoming field of research for a strong cognitive analysis to revolutionize the field of biomedical informatics and neuroscience. This paper highlights the importance of EEG signals in cognitive development experimentation and the challenges faced in conducting an EEG experiment and its intelligent analysis. Solutions to these challenges have been elaborated upon as well. Finally, the paper discusses future prospects in EEG research. A focus on these issues is essential for conducting successful EEG experimentation and choosing an accurate and precise EEG signal analysis methodology.


Electroencephalography (EEG) Cognitive development Neuroscience EEG signal analysis 


  1. 1.
    Bell, M.A., Cuevas, K.: Using EEG to study cognitive development: issues and practices. J. Cogn. Dev. 13(3), 281–294 (2012). Scholar
  2. 2.
    Cognitive Development: Encyclopedia of Children’s Health.
  3. 3.
    Huong, N.T.M., Linh, H.Q., Khai, L.Q.: Classification of left/right hand movement EEG signals using event related potentials and advanced features. 6th International Conference on the Development of Biomedical Engineering in Vietnam (BME6). IP, vol. 63, pp. 209–215. Springer, Singapore (2018). Scholar
  4. 4.
    Mahajan, R., Bansal, D.: Depression diagnosis and management using EEG-based affective brain mapping in real time. Int. J. Biomed. Eng. Technol. 18(2), 115 (2015). Scholar
  5. 5.
    Mahajan, R., Bansal, D.: Real time EEG based cognitive brain computer interface for control applications via Arduino interfacing. Procedia Comput. Sci. 115, 812–820 (2017). Scholar
  6. 6.
    Welcome to the Center for Functional MRI: Home - Center for Functional MRI - UC San Diego School of Medicine.
  7. 7.
    Agyei, S.B., et al.: Longitudinal study of preterm and full-term infants: high-density EEG analyses of cortical activity in response to visual motion. Neuropsychologia 84, 89–104 (2016). Scholar
  8. 8.
    Nedelcu, E., et al.: Artifact detection in EEG using machine learning. In: 2017 13th IEEE International Conference on Intelligent Computer Communication and Processing (ICCP) (2017).
  9. 9.
    Belmonte, M.K.: Autism and abnormal development of brain connectivity. J. Neurosci. 24(42), 9228–9231 (2004). Scholar
  10. 10.
    Bell, M.A., Fox, N.A.: The relations between frontal brain electrical activity and cognitive development during infancy. Child Dev. 63(5), 1142 (1992). Scholar
  11. 11.
    Unde, S.A., Shriram, R.: Coherence analysis of EEG signal using power spectral density. In: 2014 Fourth International Conference on Communication Systems and Network Technologies (2014).
  12. 12.
    Burle, B., et al.: Spatial and temporal resolutions of EEG: is it really black and white? A scalp current density view. Int. J. Psychophysiol. 97(3), 210–220 (2015). Scholar
  13. 13.
    Ghassemi, F., et al.: Using non-linear features of EEG for ADHD/normal participants’ classification. Procedia Soc. Behav. Sci. 32, 148–152 (2012). Scholar
  14. 14.
    Sanei, S., Chambers, J.A.: EEG Signal Processing, October 2007. Scholar
  15. 15.
    Valentová, H., Havlík, J.: Initial analysis of the EEG signal processing methods for studying correlations between muscle and brain activity. In: Khuri, S., Lhotská, L., Pisanti, N. (eds.) ITBAM 2010. LNCS, vol. 6266, pp. 220–225. Springer, Heidelberg (2010). Scholar
  16. 16.
    Jung, T.-P., et al.: Analysis and visualization of single-trial event-related potentials. Hum. Brain Mapp. 14(3), 166–185 (2001). Scholar
  17. 17.
    Puce, A., Hämäläinen, M.: A review of issues related to data acquisition and analysis in EEG/MEG studies. Brain Sci. 7(12), 58 (2017). Scholar
  18. 18.
    Alizadeh-Taheri, B., et al.: An active, microfabricated, scalp electrode-array for EEG recording. In: Proceedings of the International Solid-State Sensors and Actuators Conference - TRANSDUCERS 1995 (1995).
  19. 19.
    Lopez-Gordo, M., et al.: Dry EEG Electrodes. Sensors 14(7), 12847–12870 (2014). Scholar
  20. 20.
    Nguyen, T.A., Zeng, Y.: Analysis of design activities using EEG signals. In: Volume 5: 22nd International Conference on Design Theory and Methodology; Special Conference on Mechanical Vibration and Noise (2010).
  21. 21.
    Dobrea, M.-C., et al.: Spectral EEG features and tasks selection process: some considerations toward BCI applications. In: 2010 IEEE International Workshop on Multimedia Signal Processing (2010),
  22. 22.
    Hill, N.J., et al.: Classifying event-related desynchronization in EEG, ECoG and MEG Signals. In: Franke, K., Müller, K.-R., Nickolay, B., Schäfer, R. (eds.) DAGM 2006. LNCS, vol. 4174, pp. 404–413. Springer, Heidelberg (2006). Scholar
  23. 23.
    Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of EEG signal features extraction using linear analysis in frequency and time-frequency domains. ISRN Neurosci. 2014, 1–7 (2014). Scholar
  24. 24.
    Kumarahirwal, M., Londhe, N.D.: Power spectrum analysis of EEG signals for estimating visual attention. Int. J. Comput. Appl. 42(15), 34–40 (2012). Scholar
  25. 25.
    Väisänen, O., Malmivuo, J.: Improving the SNR of EEG generated by deep sources with weighted multielectrode leads. J. Physiol. Paris 103(6), 306–314 (2009). Scholar
  26. 26.
    Ivannikov, A., et al.: Extraction of ERP from EEG data. In: 2007 9th International Symposium on Signal Processing and Its Applications (2007).
  27. 27.
    Marshall, P.J., et al.: Development of the EEG from 5 months to 4 years of age. Clin. Neurophysiol. 113(8), 1199–1208 (2002). Scholar
  28. 28.
    Ramadan, R.A., et al.: Basics of brain computer interface. Brain Comput. Interfaces Intell. Syst. Ref. Libr. 31–50 (2014). Scholar
  29. 29.
    Liu, Y., Sourina, O., Nguyen, M.K.: Real-time EEG-based emotion recognition and its applications. In: Gavrilova, M.L., Tan, C.J.K., Sourin, A., Sourina, O. (eds.) Transactions on Computational Science XII. LNCS, vol. 6670, pp. 256–277. Springer, Heidelberg (2011). Scholar
  30. 30.
    Kumar, P., et al.: Envisioned speech recognition using EEG sensors. Pers. Ubiquitous Comput. 22(1), 185–199 (2017). Scholar
  31. 31.
    Aboalayon, K., et al.: Sleep stage classification using EEG signal analysis: a comprehensive survey and new investigation. Entropy 18(9), 272 (2016). Scholar
  32. 32.
    Yu, S., et al.: Support vector machine based detection of drowsiness using minimum EEG features. In: 2013 International Conference on Social Computing (2013).
  33. 33.
    Phan, H., et al.: Metric learning for automatic sleep stage classification. In: 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (2013).

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Department of Computer Engineering, Netaji Subhas Institute of TechnologyUniversity of DelhiNew DelhiIndia

Personalised recommendations