Skip to main content
SpringerLink
Log in

Criminal psychological emotion recognition based on deep learning and EEG signals

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

The difficulty of criminal psychological recognition is that it is difficult to classify emotions, and the accuracy of traditional recognition methods is insufficient. Therefore, it is necessary to improve the accuracy rate in combination with modern computer technology. This study uses deep learning as technical support and combines EEG computer signals to classify criminal psychological emotions. Moreover, a method for classifying EEG signals based on the state of mind of neural networks was constructed in the study. In addition, the EEG is denoised preprocessed by time-domain regression method, and features of the EEG signal parameters of different criminal psychological tasks are extracted and used as the input of the neural network. Finally, in order to verify the effectiveness of the algorithm, a simulation experiment is designed to study the effectiveness of the algorithm. The results show that the method proposed in this paper has certain practical effects.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  1. Anderson R, Sandsten M (2017) Stochastic modelling and optimal spectral estimation of EEG signals[M]//EMBEC & NBC 2017. Springer, Singapore, pp 908–911

    Google Scholar 

  2. Mutlu AY (2018) Detection of epileptic dysfunctions in EEG signals using Hilbert vibration decomposition. Biomed Signal Process Control 40:33–40

    Article  Google Scholar 

  3. Ma J, Sun Y, Zhang X (2019) Multimodal emotion recognition for the fusion of speech and EEG signals. Xi’an Dianzi Keji Daxue Xuebao/J Xidian Univ 46(1):143–150

    Google Scholar 

  4. Cuesta-Frau D, Miró–Martínez P, Núñez JJ et al (2017) Noisy EEG signals classification based on entropy metrics. Performance assessment using first and second generation statistics. Comput Biol Med 87:141–151

    Article  Google Scholar 

  5. Handojoseno AMA, Naik GR, Gilat M et al (2018) Prediction of freezing of gait in patients with Parkinson’s disease using EEG signals. Stud Health Technol Inf 246:124–131

    Google Scholar 

  6. Navea RF, Dadios E (2016) Classification of wavelet-denoised musical tone stimulated EEG signals using artificial neural networks[C]//2016 IEEE Region 10 Conference (TENCON). IEEE, pp 1503–1508

  7. Zhang H, Su J, Wang Q et al (2017) Predicting seizure by modeling synaptic plasticity based on EEG signals—a case study of inherited epilepsy. Commun Nonlinear Sci Numer Simul 56:330–343

    Article  MathSciNet  Google Scholar 

  8. Sharma M, Deb D, Acharya UR (2018) A novel three-band orthogonal wavelet filter bank method for an automated identification of alcoholic EEG signals. Appl Intell 48(5):1368–1378

    Google Scholar 

  9. Majdouli MAE, Bougrine S, Rbouh I et al (2017) A comparative study of the EEG signals big optimization problem using evolutionary, swarm and memetic computation algorithms. In: Proceedings of the genetic and evolutionary computation conference companion, pp 1357–1364

  10. Hamzah N, Abidin NZ, Salehuddin M et al (2017) Classification of EEG signals using support vector machine to distinguish different hand motor movements. Adv Sci Lett 23(6):5379–5382

    Article  Google Scholar 

  11. Selvathi D, Selvaraj H (2017) FPGA implementation for epileptic seizure detection using amplitude and frequency analysis of EEG signals. In: 2017 25th international conference on systems engineering (ICSEng). IEEE Computer Society

  12. Jadhav N, Manthalkar R, Joshi Y (2017) Assessing effect of meditation on cognitive workload using EEG signals. In: Second international workshop on pattern recognition. International Society for Optics and Photonics, vol 10443, p 104431J

  13. Corsi MC, Chavez M, Schwartz D et al (2019) Integrating eeg and meg signals to improve motor imagery classification in brain–computer interface[J]. Int J Neural Syst 29(01):1850014

    Article  Google Scholar 

  14. Chatterjee S, Pratiher S, Bose R (2017) Multifractal detrended fluctuation analysis based novel feature extraction technique for automated detection of focal and non focal EEG signals. IET Sci Meas Technol 11(8):1014–1021

    Article  Google Scholar 

  15. Barua S, Ahmed MU, Begum S (2017) Classifying drivers’ cognitive load using EEG signals. Stud Health Technol Inform 237:99–106

    Google Scholar 

  16. Nguyen CH, Karavas GK, Artemiadis P (2017) Inferring imagined speech using EEG signals: a new approach using Riemannian manifold features. J Neural Eng 15(1):016002

    Article  Google Scholar 

  17. Spyrou L, Escudero J (2017) Graph regularised tensor factorisation of EEG signals based on network connectivity measures. In: 2017 IEEE international conference on acoustics, speech and signal processing (ICASSP). IEEE, pp 944–948

  18. Hussain M, Aboalsamh H, Abdul W et al (2016) An intelligent system to classify epileptic and non-epileptic EEG signals. In: 2016 12th international conference on signal-image technology & internet-based systems (SITIS). IEEE, pp 230–235

  19. Ieracitano C, Duun-Henriksen J, Mammone N, et al (2017) Wavelet coherence-based clustering of EEG signals to estimate the brain connectivity in absence epileptic patients. In: 2017 international joint conference on neural networks (IJCNN), pp 1297–1304. IEEE

  20. Taqi AM, Al-Azzo F, Mariofanna M et al (2017) Classification and discrimination of focal and non-focal EEG signals based on deep neural network. In: 2017 international conference on current research in computer science and information technology (ICCIT), pp 86–92. IEEE

  21. Bashar MK, Reza F, Idris Z et al (2016) Epileptic seizure classification from intracranial EEG signals: a comparative study EEG-based seizure classification. In: 2016 IEEE EMBS conference on biomedical engineering and sciences (IECBES), pp 96–101. IEEE

  22. Begum D, Ravikumar KM, Vykuntaraju KN (2016) An initiative to classify different neurological disorder in children using multichannel EEG signals. In: 2016 IEEE international conference on recent trends in electronics, information and communication technology (RTEICT), pp 1563–1566. IEEE

  23. Lv Z, Kong W, Zhang X et al (2019) Intelligent security planning for regional distributed energy internet. IEEE Trans Ind Inf 16:3540–3547.

    Article  Google Scholar 

  24. Asif A, Majid M, Anwar SM et al (2019) Human stress classification using EEG signals in response to music tracks. Comput Biol Med 107:182–196

    Article  Google Scholar 

  25. Lv Z, Hu B, Lv H (2019) Infrastructure monitoring and operation for smart cities based on IoT system. IEEE Trans Ind Inf 16:1957–1962.

    Article  Google Scholar 

  26. Shi T, Ren L, Cui W (2019) Feature recognition of motor imaging EEG signals based on deep learning. Pers Ubiquit Comput 23(3–4):499–510

    Article  Google Scholar 

  27. Lv Z, Li X, Lv H, Xiu W (2019) BIM big data storage in WebVRGIS. IEEE Trans Ind Inf 16:2566–2573

    Article  Google Scholar 

Download references

Acknowledgements

The study was supported by the National Key R&D Program of China (Grant No. 2017YFC0820200).

Author information

Authors and Affiliations

  1. Institute of Crime, People’s Public Security University of China, Beijing, 100038, China

    Qi Liu & Hongguang Liu

Authors
  1. Qi Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hongguang Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Qi Liu.

Ethics declarations

Conflict of interest

The authors have no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liu, Q., Liu, H. Criminal psychological emotion recognition based on deep learning and EEG signals. Neural Comput & Applic 33, 433–447 (2021). https://doi.org/10.1007/s00521-020-05024-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-020-05024-0

Keywords

Search

Navigation