Abstract
Classifying Motor Imagery (MI) electroencephalography (EEG) signals poses significant challenges, especially when dealing with multiple classes. EEG signals are inherently noisy, which complicates the task further. Using raw EEG signals directly in deep learning systems is possible, but without preprocessing, the system’s ability to distinguish between classes may be compromised due to the noise and variability in the data. In this study, the incorporation of electrooculography (EOG) channels alongside EEG channels was explored to facilitate the automatic extraction of cleaner data by enabling a proposed deep learning model to identify and interpret EOG as noise. The inclusion of EOG alongside with EEG method was compared with well-established preprocessing techniques such as EOG regression, wavelet-based denoising, common spatial pattern (CSP), and common average referencing (CAR). Using the BCI Competition IV dataset IIa, the integration of EOG channels demonstrated a notable performance improvement, particularly in the subject-independent model, when compared with other preprocessing methods. The subject-dependent model achieved a performance score of 0.879, while the subject-independent models reached 0.874. Additionally, testing the inclusion of EOG channels with only 3 out of 22 EEG channels showed that this setup was nearly as effective as using all 22 EEG channels, highlighting the efficiency of including EOG channels in enhancing EEG data quality and classification accuracy in BCI systems.
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References
Ramoser, H., Müller-Gerking, J., Pfurtscheller, G.: Optimal spatial filtering of single trial EEG during imagined hand movement. IEEE Trans. Rehabil. Eng. 8(4) (2000). https://doi.org/10.1109/86.895946
Martín-Clemente, R., Olias, J., Thiyam, D.B., Cichocki, A., Cruces, S.: Information theoretic approaches for motor-imagery BCI systems: Review and experimental comparison. Entropy 20(1) (2018). https://doi.org/10.3390/e20010007
Naeem, M., Brunner, C., Leeb, R., Graimann, B., Pfurtscheller, G.: Seperability of four-class motor imagery data using independent components analysis. J. Neural Eng. 3(3) (2006). https://doi.org/10.1088/1741-2560/3/3/003
Ang, K.K., Chin, Z.Y., Zhang, H., Guan, C.: Filter Bank Common Spatial Pattern (FBCSP) in brain-computer interface. In: Proceedings of the International Joint Conference on Neural Networks (2008). https://doi.org/10.1109/IJCNN.2008.4634130
Park, S.H., Lee, D., Lee, S.G.: Filter bank regularized common spatial pattern ensemble for small sample motor imagery classification. IEEE Trans. Neural Syst. Rehabil. Eng. 26(2) (2018). https://doi.org/10.1109/TNSRE.2017.2757519
Lotte, F., Congedo, M., Lécuyer, A., Lamarche, F., Arnaldi, B.: A review of classification algorithms for EEG-based brain-computer interfaces. J. Neural Eng. 4(2) (2007). https://doi.org/10.1088/1741-2560/4/2/R01
Hu, D., Li, W., Chen, X.: Feature extraction of motor imagery EEG signals based on wavelet packet decomposition. In: 2011 IEEE/ICME International Conference on Complex Medical Engineering, CME 2011 (2011). https://doi.org/10.1109/ICCME.2011.5876829
Chatterjee, R., Bandyopadhyay, T., Sanyal, D.K., Guha, D.: Comparative analysis of feature extraction techniques in motor imagery EEG signal classification. In: Smart Innovation, Systems and Technologies (2018). https://doi.org/10.1007/978-981-10-5828-8_8
Xu, B., Song, A.: Pattern recognition of motor imagery EEG using wavelet transform. J. Biomed. Sci. Eng. 1(1) (2008). https://doi.org/10.4236/jbise.2008.11010
Baig, M.Z., Mehmood, Y., Ayaz, Y.: A BCI system classification technique using median filtering and wavelet transform. In: Lecture Notes in Logistics (2016). https://doi.org/10.1007/978-3-319-23512-7_34
Kant, P., Hazarika, J., Laskar, S.H.: Wavelet transform based approach for EEG feature selection of motor imagery data for braincomputer interfaces. In: Proceedings of the 3rd International Conference on Inventive Systems and Control, ICISC 2019 (2019). https://doi.org/10.1109/ICISC44355.2019.9036445
Xu, B., et al.: Wavelet transform time-frequency image and convolutional network-based motor imagery EEG classification. IEEE Access 7 (2019). https://doi.org/10.1109/ACCESS.2018.2889093
Tyagi, A., Nehra, V.: Time frequency analysis of non-stationary motor imagery EEG signals. In: 2017 International Conference on Computing and Communication Technologies for Smart Nation, IC3TSN 2017 (2017). https://doi.org/10.1109/IC3TSN.2017.8284448
Dokur, Z., Olmez, T.: Classification of motor imagery electroencephalogram signals by using a divergence based convolutional neural network. Appl. Soft Comput. 113 (2021). https://doi.org/10.1016/j.asoc.2021.107881
Korhan, N., Olmez, T., Dokur, Z.: Generating ten BCI commands using four simple motor imageries and classification by divergence-based DNN. Neural Comput. Appl. 35(2) (2023). https://doi.org/10.1007/s00521-022-07787-0
Lawhern, V.J., Solon, A.J., Waytowich, N.R., Gordon, S.M., Hung, C.P., Lance, B.J.: EEGNet: a compact convolutional neural network for EEG-based brain-computer interfaces. J. Neural Eng. 15(5) (2018). https://doi.org/10.1088/1741-2552/aace8c
Gratton, G., Coles, M.G.H., Donchin, E.: A new method for off-line removal of ocular artifact. Electroencephal. Clin. Neurophysiol. 55(4) (1983). https://doi.org/10.1016/0013-4694(83)90135-9
Cohen, M.X.: A tutorial on generalized eigendecomposition for denoising, contrast enhancement, and dimension reduction in multichannel electrophysiology. NeuroImage 247 (2022). https://doi.org/10.1016/j.neuroimage.2021.118809
BCI Competitions IV-2a (2008). http://www.bbci.de/competition/iv/
Sakhavi, S., Guan, C., Yan, S.: Learning temporal information for brain-computer interface using convolutional neural networks. IEEE Trans. Neural Networks Learn. Syst. 29(11) (2018). https://doi.org/10.1109/TNNLS.2018.2789927
Abbas, W., Khan, N.A.: DeepMI: deep learning for multiclass motor imagery classification. In: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS (2018). https://doi.org/10.1109/EMBC.2018.8512271
Zhao, X., Zhang, H., Zhu, G., You, F., Kuang, S., Sun, L.: A multi-branch 3d convolutional neural network for EEG-based motor imagery classification. IEEE Trans. Neural Syst. Rehabil. Eng. 27(10) (2019). https://doi.org/10.1109/TNSRE.2019.2938295
Deng, X., Zhang, B., Yu, N., Liu, K., Sun, K.: Advanced TSGL-EEGNet for motor imagery EEG-based brain-computer interfaces. IEEE Access 9 (2021). https://doi.org/10.1109/ACCESS.2021.3056088
Olivas-Padilla, B.E., Chacon-Murguia, M.I.: Classification of multiple motor imagery using deep convolutional neural networks and spatial filters. Appl. Soft Comput. J. 75 (2019). https://doi.org/10.1016/j.asoc.2018.11.031
Aristimunha, B., et al.: Mother of all BCI Benchmarks (MOABB) (2023). https://doi.org/10.5281/zenodo.10034223
Larson, E.: MNE-Python. Zenodo, Nov. 20, 2023. https://doi.org/10.5281/zenodo.10161630
Gramfort, A., et al.: MEG and EEG data analysis with MNE-python. Front. Neurosci. 7(267), 1–13 (2013). https://doi.org/10.3389/fnins.2013.00267
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Özkahraman, A., Ölmez, T., Dokur, Z. (2024). Impact of Noise Elimination Methods on Classification Performance in Motor Imagery EEG. In: Mylonas, P., Kardaras, D., Caro, J. (eds) Novel and Intelligent Digital Systems: Proceedings of the 4th International Conference (NiDS 2024). NiDS 2024. Lecture Notes in Networks and Systems, vol 1170. Springer, Cham. https://doi.org/10.1007/978-3-031-73344-4_6
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