Electroencephalogram (EEG) measures brainwaves that have been the widely used modality for brain–computer interface (BCI) applications. EEG signal with machine learning has gained substantial success in the BCI. However, the availability of limited training data, appropriate model selection and high false-positive rates are the challenges that need immediate attention. Therefore, in this Letter, the authors present a mental task classification model based on the notion of transfer learning that addresses the issue of data scarcity, model selection and misclassification ratio. In the framework, the proposed model uses pre-trained network for the extraction of diverse feature and classify using support vector machine. The authors employed four pre-trained networks to identify the optimal network for the proposed framework: Vgg16, Vgg19, Resnet18 and Resnet50. The highest classification accuracy of 86.85% (using Resnet50) was achieved using transfer learning. Comparison results showed that convolutional neural network-based approach outperformed conventional machine learning approaches and hence it can be concluded that the EEG-based classification of the mental task using transfer learning model could be used in developing a superior model despite the limited data availability.

References

1. 1)
  - 2. Gupta, A., Khan, R.U., Singh, V.K., et al: ‘A novel approach for classification of mental tasks using multiview ensemble learning (MEL)’Neurocomputing, vol. 417, pp. 558–584, 2020, (doi: 10.1016/j.neucom.2020.07.050).
2. 2)
  - 9. Tan, C., Sun, F., Kong, T., et al: ‘A survey on deep transfer learning’. in 27th Int. Conf. Artificial Neural Networks, Rhodes, Greece, 2018, vol. 11141, LNCS, pp. 270–279, doi: 10.1007/978-3-030-01424-7_27.
3. 3)
  - 12. Keirn, Z.A., Aunon, J.I.: ‘A new mode of communication between man and his surroundings’, IEEE Trans. Biomed. Eng., 1990, 37, (12), pp. 1209–1214 (doi: 10.1109/10.64464).
4. 4)
  - 13. Gupta, A., Agrawal, R.K., Kirar, J.S., et al: ‘On the utility of power spectral techniques with feature selection techniques for effective mental task classification in noninvasive BCI’, IEEE Trans. Syst. Man, Cybern. Syst., 2019, pp. 1–13, (doi: 10.1109/TSMC.2019.2917599).
5. 5)
  - 7. Ju, C., Gao, D., Mane, R., et al: ‘Federated transfer learning for EEG signal classification’, 2020, no. Dl, pp. 3040–3045.
6. 6)
  - 14. Zhang, L., He, W., He, C., et al: ‘Improving mental task classification by adding high frequency band information’, J. Med. Syst., 2010, 34, (1), pp. 51–60, (doi: 10.1007/s10916-008-9215-z).
7. 7)
  - 3. Nandish, M., Stafford, M., Kumar, P.H., et al: ‘Feature extraction and classification of EEG signal using neural network based techniques’, Int. J. Eng. Innov. Technol., 2012, 2, (4), pp. 1–5.
8. 8)
  - 5. Gupta, A., Agrawal, R.K., Kaur, B.: ‘Performance enhancement of mental task classification using EEG signal: a study of multivariate feature selection methods’, Soft Comput.., 2015, 19, (10), pp. 2799–2812 (doi: 10.1007/s00500-014-1443-1).
9. 9)
  - 6. Mohd Daud, S., Yunus, J.: ‘Classification of mental tasks using de-noised EEG signals’. Int. Conf. Signal Proces. Proc., ICSP, Beijing, People's Republic of China, 2004, vol. 3, pp. 2206–2209, doi: 10.1109/icosp.2004.1442216.
10. 10)
  - 8. Wu, D., Xu, Y., Lu, B.-L.: ‘Transfer learning for EEG-based brain-computer interfaces: a review of progress made since 2016’, 2020, vol. 8920, no. c, pp. 1–16, doi: 10.1109/TCDS.2020.3007453.
11. 11)
  - 11. Tan, C., Sun, F., Zhang, W.: ‘Deep transfer learning for EEG-based brain computer interface’, 2018, pp. 916–920, doi: 10.1016/j.metabol.2013.12.004.
12. 12)
  - 10. Raghu, , Sriraam, N., Temel, Y., et al: ‘A convolutional neural network based framework for classification of seizure types’. in Proc. Annual Int. Conf. IEEE Engineering in Medicine and Biology Society, EMBS, Berlin, Germany, 2019, pp. 2547–2550, doi: 10.1109/EMBC.2019.8857359.
13. 13)
  - 4. Garrett, D., Peterson, D.A., Anderson, C.W., et al: ’Comparison of linear, nonlinear, and feature selection methods for EEG signal classification’, IEEE Trans. Neural Syst. Rehabil. Eng., 2003, 11, (2), pp. 141–144 (doi: 10.1109/TNSRE.2003.814441).
14. 14)
  - 1. Wu, D., Peng, R., Huang, J., et al: ‘Transfer learning for brain-computer interfaces: a complete pipeline’, 2020, pp. 1–9. Available at http://arxiv.org/abs/2007.03746.

Realising transfer learning through convolutional neural network and support vector machine for mental task classification

References

Related content