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EEG Signals for Measuring Cognitive Development

A Study of EEG Signals Challenges and Prospects

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Intelligent Human Computer Interaction (IHCI 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11278))

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Abstract

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.

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References

  1. Bell, M.A., Cuevas, K.: Using EEG to study cognitive development: issues and practices. J. Cogn. Dev. 13(3), 281–294 (2012). https://doi.org/10.1080/15248372.2012.691143

    Article  Google Scholar 

  2. Cognitive Development: Encyclopedia of Children’s Health. www.healthofchildren.com/C/Cognitive-Development.html

  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). https://doi.org/10.1007/978-981-10-4361-1_35

    Chapter  Google Scholar 

  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). https://doi.org/10.1504/ijbet.2015.070033

    Article  Google Scholar 

  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). https://doi.org/10.1016/j.procs.2017.09.158

    Article  Google Scholar 

  6. Welcome to the Center for Functional MRI: Home - Center for Functional MRI - UC San Diego School of Medicine. cfmriweb.ucsd.edu/

  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). https://doi.org/10.1016/j.neuropsychologia.2016.02.001

    Article  Google Scholar 

  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). https://doi.org/10.1109/iccp.2017.8116986

  9. Belmonte, M.K.: Autism and abnormal development of brain connectivity. J. Neurosci. 24(42), 9228–9231 (2004). https://doi.org/10.1523/jneurosci.3340-04.2004

    Article  Google Scholar 

  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). https://doi.org/10.2307/1131523

    Article  Google Scholar 

  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). https://doi.org/10.1109/csnt.2014.181

  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). https://doi.org/10.1016/j.ijpsycho.2015.05.004

    Article  Google Scholar 

  13. Ghassemi, F., et al.: Using non-linear features of EEG for ADHD/normal participants’ classification. Procedia Soc. Behav. Sci. 32, 148–152 (2012). https://doi.org/10.1016/j.sbspro.2012.01.024

    Article  Google Scholar 

  14. Sanei, S., Chambers, J.A.: EEG Signal Processing, October 2007. https://doi.org/10.1002/9780470511923

    Book  Google Scholar 

  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). https://doi.org/10.1007/978-3-642-15020-3_20

    Chapter  Google Scholar 

  16. Jung, T.-P., et al.: Analysis and visualization of single-trial event-related potentials. Hum. Brain Mapp. 14(3), 166–185 (2001). https://doi.org/10.1002/hbm.1050

    Article  Google Scholar 

  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). https://doi.org/10.3390/brainsci7060058

    Article  Google Scholar 

  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). https://doi.org/10.1109/sensor.1995.717088

  19. Lopez-Gordo, M., et al.: Dry EEG Electrodes. Sensors 14(7), 12847–12870 (2014). https://doi.org/10.3390/s140712847

    Article  Google Scholar 

  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). https://doi.org/10.1115/detc2010-28477

  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), https://doi.org/10.1109/mmsp.2010.5662010

  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). https://doi.org/10.1007/11861898_41

    Chapter  Google Scholar 

  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). https://doi.org/10.1155/2014/730218

    Article  Google Scholar 

  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). https://doi.org/10.5120/5769-7993

    Article  Google Scholar 

  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). https://doi.org/10.1016/j.jphysparis.2009.07.003

    Article  Google Scholar 

  26. Ivannikov, A., et al.: Extraction of ERP from EEG data. In: 2007 9th International Symposium on Signal Processing and Its Applications (2007). https://doi.org/10.1109/isspa.2007.4555470

  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). https://doi.org/10.1016/s1388-2457(02)00163-3

    Article  Google Scholar 

  28. Ramadan, R.A., et al.: Basics of brain computer interface. Brain Comput. Interfaces Intell. Syst. Ref. Libr. 31–50 (2014). https://doi.org/10.1007/978-3-319-10978-7_2

    Google Scholar 

  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). https://doi.org/10.1007/978-3-642-22336-5_13

    Chapter  Google Scholar 

  30. Kumar, P., et al.: Envisioned speech recognition using EEG sensors. Pers. Ubiquitous Comput. 22(1), 185–199 (2017). https://doi.org/10.1007/s00779-017-1083-4

    Article  Google Scholar 

  31. Aboalayon, K., et al.: Sleep stage classification using EEG signal analysis: a comprehensive survey and new investigation. Entropy 18(9), 272 (2016). https://doi.org/10.3390/e18090272

    Article  Google Scholar 

  32. Yu, S., et al.: Support vector machine based detection of drowsiness using minimum EEG features. In: 2013 International Conference on Social Computing (2013). https://doi.org/10.1109/socialcom.2013.124

  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). https://doi.org/10.1109/embc.2013.6610677

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Authors and Affiliations

  1. Department of Computer Engineering, Netaji Subhas Institute of Technology, University of Delhi, New Delhi, India

    Swati Aggarwal, Prakriti Bansal & Sameer Garg

Authors
  1. Swati Aggarwal
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  2. Prakriti Bansal
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  3. Sameer Garg
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Corresponding author

Correspondence to Swati Aggarwal .

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Editors and Affiliations

  1. Indian Institute of Information Technology, Allahabad, India

    Uma Shanker Tiwary

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Aggarwal, S., Bansal, P., Garg, S. (2018). EEG Signals for Measuring Cognitive Development. In: Tiwary, U. (eds) Intelligent Human Computer Interaction. IHCI 2018. Lecture Notes in Computer Science(), vol 11278. Springer, Cham. https://doi.org/10.1007/978-3-030-04021-5_7

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  • DOI: https://doi.org/10.1007/978-3-030-04021-5_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-04020-8

  • Online ISBN: 978-3-030-04021-5

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