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Summary and Conclusions

  • Zhiguo ZhangEmail author
  • Li Hu
Chapter

Abstract

EEG remains useful and irreplaceable in multiple clinical applications and scientific researches, with regard to its massive advantages. As EEG continues to spread widely over time, EEG signal processing is still a highly promising and evolving field. Recent developments of EEG processing techniques, such as advanced machine learning for EEG and EEG-related multimodality brain imaging, are expected to make EEG a more powerful and versatile tool in the future.

Keywords

EEG signal processing Machine learning Multimodality imaging 

References

  1. Biasiucci A, Franceschiello B, Murray MM. Electroencephalography. Curr Biol. 2019;29(3):R80–5.CrossRefGoogle Scholar
  2. Cavanagh JF. Electrophysiology as a theoretical and methodological hub for the neural sciences. Psychophysiology. 2019;56(2):e13314.  https://doi.org/10.1111/psyp.13314.CrossRefPubMedGoogle Scholar
  3. Sejnowski TJ, Churchland PS, Movshon JA. Putting big data to good use in neuroscience. Nat Neurosci. 2014;17(11):1440–1.CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.School of Biomedical Engineering, Health Science CenterShenzhen UniversityShenzhenChina
  2. 2.CAS Key Laboratory of Mental Health, Institute of PsychologyChinese Academy of SciencesBeijingChina
  3. 3.Department of PsychologyUniversity of Chinese Academy of SciencesBeijingChina

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