Skip to main content
Springer Nature Link
Log in

Convolutional neural network with support vector machine for motor imagery EEG signal classification

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Electroencephalography (EEG) motor imagery (MI) signals has recently attracted a great deal of attention as these signals encrypt a person's desire of executing a command. MI signals are used to assist disabled people and even for autonomous driving through some control devices like wheelchairs just by thinking about it. Therefore, an accurate MI tasks classification from EEG signals is cricial to get a reliable Brain Computer Interface (BCI) system. In this paper, we proposed a new method of classifying MI tasks based on Convolutional Neural Network (CNN) methods. We applied a simple preprocessing to the data followed by a feature extraction step using Common Spatial Pattern (CSP) to extract spatial features and Wavelet Packet Decomposition (WPD) to extract frequency-time features. We then tested our four proposed models: CNN, CNN+LSTM, CNN-SVM and CNN+LSTM-SVM using BCI Competition IV 2a dataset. The obtained experimental results show that the proposed CNN-SVM gives the best results. Our results are really promising achieving interesting accuracy, precision, recall, and F1 score of 64.33%, 65.05%, 66.11%, et 64.11%, respectively.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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
Fig. 12
Fig. 13
Fig. 14
Fig. 15

Similar content being viewed by others

Data availability

The dataset used in this study is public and can be found at the following links: BCI Competition IV dataset 2a.

References

  1. Abdel-Hamid O, Mohamed AR, Jiang H, Deng L, Penn G, Yu D (2014) Convolutional neural networks for speech recognition. IEEE-ACM Transac Audio Speech Lang Proc 22(10):1533–1545

    Article  Google Scholar 

  2. Arshad J, Qaisar A, Rehman AU, Shakir M, Nazir MK, Rehman AU, Eldin ET, Ghamry NA, Hamam H (2022) Intelligent Control of Robotic Arm Using Brain Computer Interface and Artificial Intelligence. Appl Sci 12:10813. https://doi.org/10.3390/app122110813

    Article  Google Scholar 

  3. Bashivan P, Rish I, Yeasin M, Codella NC(2015) Learning representations from EEG with deep recurrentconvolutional neural networks. arXiv arXiv:1511.06448

  4. Bengio Y, Ca M (2015). Rmsprop and equilibrated adaptive learning rates for nonconvex optimization. corrabs/1502.04390

  5. Donahue J, Hendricks LA, Rohrbach M, Venugopalan S, Guadarrama S, Saenko K, Darrell T (2017) Long-term recurrent convolutional networks for visual recognition and description. IEEE Trans Pattern Anal Mach Intell:677–691

  6. Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PC, Mark RG et al (2000) PhysioBank, physiotoolkit, and physionet components of a new research resource for complex physiologic signals. Circulation 101:215–220

    Article  Google Scholar 

  7. Grosse-Wentrup M, Liefhold C, Gramann K, Buss M (2009) Beamforming in noninvasive brain-computer interfaces. Biomed Eng, IEEE Transac 56(4):1209–1219

    Article  Google Scholar 

  8. Hou Y, Jia S, Lun X, Zhang S, Chen T, Wang F, Lv J (2022) Deep Feature Mining via the Attention-Based Bidirectional Long Short Term Memory Graph Convolutional Neural Network for Human Motor Imagery Recognition. Front Bioeng Biotechnol

  9. Ikhtiyor M, Taegkeun W (2019) Efficient Classification of Motor Imagery Electroencephalography Signals Using Deep Learning Methods. Sensors 19:1736

    Article  Google Scholar 

  10. Islam MR, Islam MM, Rahman MM et al (2021) EEG Channel Correlation Based Model for Emotion Recognition. Comput Biol Med 136:104757

    Article  Google Scholar 

  11. Islam MR, Moni MA, Islam MM et al (2021) Emotion Recognition From EEG Signal Focusing on Deep Learning and Shallow Learning Techniques. In: IEEE Access, 9, pp. 94601-94624, https://doi.org/10.1109/ACCESS.2021.3091487

  12. Kumar S, Sharma A, Mamun K, Tsunoda T (2017) A deep learning approach for motor imagery EEG signal classification. In: Proceedings of the 2016 3rd Asia-Pacific World Congress on Computer Science and Engineering (APWC on CSE), (Nadi: IEEE)

  13. Parra LC, Spence CD, Gerson AD, Sajda P (2005) Recipes for the linear analysis of EEG. NeuroImage 28(2):326–341

    Article  Google Scholar 

  14. Ramoser H, Muller-Gerking J, Pfurtscheller G (2000) Optimal spatial filtering of single trial EEG during imagined hand movement Rehabilitation Engineering. IEEE Transac 8:441–446

    Google Scholar 

  15. Sakhavi S, Guan C (2017) Convolutional neural network-based transfer learning and knowledge distillation using multi-subject data in motor imagery BCI. In: Proceedings of the International IEEE/EMBS Conference on Neural Engineering, Shanghai: IEEE, pp. 588–591

  16. Sakhavi S, Guan C, Yan S (2015) Parallel convolutional-linear neural network for motor imagery classification. In: Proceedings of the European Signal Processing Conference, (Nice: IEEE), pp. 2736–2740

  17. Schirrmeister RT, Springenberg JT, Fiederer LDJ, Glasstetter M, Eggensperger K, Tangermann M et al (2017) Deep learning with convolutional neural networks for EEG decoding and visualization. Hum Brain Mapp 38:5391–5420. https://doi.org/10.1002/hbm.23730

    Article  Google Scholar 

  18. Tabar YR, Halici U (2017) A novel deep learning approach for classification of EEG motor imagery signals. J Neural Eng 14:016003. https://doi.org/10.1088/1741-2560/14/1/016003

    Article  Google Scholar 

  19. Thanh N, Imali H, Amin K, Lee G-B, Chee PL, Saeid N (2018) Classification of Multi-Class BCI Data by Common Spatial Pattern and Fuzzy System. IEEE Access 6:27873–27884

    Article  Google Scholar 

  20. Vinyals O, Toshev A, Bengio S, Erhan D (2015) Show and tell: a neural image caption generator. Proc IEEE Comp Soc Conf on Comp Vis and Pattern Recognition:3156–3164

  21. Ward R, Wu X, Bottou L (2019) AdaGrad stepsizes: Sharp convergence over nonconvex landscapes. In: International Conference on Machine Learning (pp. 6677-6686). PMLR

  22. Yu Z, Albera L, Le Bouquin Jeannes R, Kachenoura A, Karfoul A, Yang C, Shu H (2022) Epileptic Seizure Prediction Using Deep Neural Networks Via Transfer Learning and Multi-Feature Fusion. Int J Neural Syst 32(7):2250032. https://doi.org/10.1142/S0129065722500320

    Article  Google Scholar 

  23. Zhang Z (2018) Improved adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS) (pp. 1-2). IEEE

  24. W. Zheng and Z. Lin (2009) Optimizing multi-class spatio-spectral filters via Bayes error estimation for EEG classification. In: Proc. Adv. Neural Inf. Processing Syst, pp. 2268-2276

  25. Zhu X, Li P, Li C, Yao D, Zhang R, Xu P (2019) Separated channel convolutional neural network to realize the training free motor imagery BCI systems. Biomed Sign Process Control 49:396–403

    Article  Google Scholar 

Download references

Funding

No funding was involved in the present work.

Author information

Authors and Affiliations

  1. ATMS Lab, Advanced Technologies for Medicine and Signals, ENIS, Sfax University, Sfax, Tunisia

    Amira Echtioui & Mohamed Ghorbel

  2. King Abdulaziz University (KAU), Jeddah, Saudi Arabia

    Wassim Zouch

  3. Department of Neurology, Habib Bourguiba University Hospital, Sfax, Tunisia

    Chokri Mhiri

  4. Neuroscience Laboratory “LR-12-SP-19”, Faculty of Medicine, Sfax University, Sfax, Tunisia

    Chokri Mhiri

Authors
  1. Amira Echtioui
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Wassim Zouch
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Mohamed Ghorbel
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Chokri Mhiri
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Amira Echtioui.

Ethics declarations

Informed consent statement

Not applicable.

Conflicts of interest

The authors declare they have no conflicts of interests.

Additional information

Publisher’s note

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Echtioui, A., Zouch, W., Ghorbel, M. et al. Convolutional neural network with support vector machine for motor imagery EEG signal classification. Multimed Tools Appl 82, 45891–45911 (2023). https://doi.org/10.1007/s11042-023-15468-w

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-023-15468-w

Keywords