Abstract
Electroencephalography (EEG), a recording of brain activities, is usually feature fused. Although such feature fusion characteristic makes many brain-computer interfaces (BCI) possible, it makes it hard to distinguish task-specific features. As a result, current works usually use the whole EEG signal or features for a specific task like classification, regardless of the fact that many of the features are not task-related. In this paper, we aim to analyze the task-specific significance of EEG features. Specifically, we extract the frequency domain features and perform classification on them. To ensure a generalized conclusion, we use various classification architectures like Multilayer Perceptron (MLP) and 2D convolutional neural network (Conv2D). Extensive experiments are conducted on the UCI EEG dataset. We find that the front part of the brain, namely channel Fpz, AFz, Fp1, and Fp2 contains the general distinct features. Besides, the beta frequency band of the EEG signal is the most significant in the alcoholism classification task.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Abhang, P.A., Gawali, B.W.: Correlation of eeg images and speech signals for emotion analysis. Curr. J. Appl. Sci. Technol. 10(5), 1–13 (2015)
Garson, D.G.: Interpreting neural network connection weights. AI Expert, pp. 47–51 (1991)
Gedeon, T.D.: Data mining of inputs: analysing magnitude and functional measures. Int. J. Neural Syst. 8(02), 209–218 (1997)
Gedeon, T., Harris, D.: Network reduction techniques. In: Proceedings International Conference on Neural Networks Methodologies and Applications, pp. 119–126. AMSE (1991)
Lebedev, M.A., Nicolelis, M.A.: Brain-machine interfaces: past, present and future. TRENDS Neurosci. 29(9), 536–546 (2006)
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), R1 (2007)
Rostov, M., Hossain, M.Z., Rahman, J.S.: Robotic emotion monitoring for mental health applications: preliminary outcomes of a survey. In: Ardito, C., et al. (eds.) INTERACT 2021. LNCS, vol. 12936, pp. 481–485. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-85607-6_62
Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017)
Uddin, M.B., Hossain, M., Ahmad, M., Ahmed, N., Rashid, M.A.: Effects of caffeinated beverage consumption on electrocardiographic parameters among healthy adults. Modern Appl. Sci. 8(2), 69 (2014)
Wang, H., et al.: Score-cam: Score-weighted visual explanations for convolutional neural networks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, June 2020
Yao, Y., Plested, J., Gedeon, T.: Deep feature learning and visualization for EEG recording using autoencoders. In: Cheng, L., Leung, A.C.S., Ozawa, S. (eds.) ICONIP 2018. LNCS, vol. 11307, pp. 554–566. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-04239-4_50
Zhang, Y., Hossain, M.Z., Rahman, S.: DeepVANet: a deep end-to-end network for multi-modal emotion recognition. In: Ardito, C., et al. (eds.) INTERACT 2021. LNCS, vol. 12934, pp. 227–237. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-85613-7_16
Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., Torralba, A.: Learning deep features for discriminative localization. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2921–2929 (2016)
Yao, Y., Plested, J., Gedeon, T.: Information-preserving feature filter for short-term EEG signals. Neurocomputing 408, 91–99 (2020)
Yao, Y., Zheng, L., Yang, X., Naphade, M., Gedeon, T.: Simulating content consistent vehicle datasets with attribute Descent. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12351, pp. 775–791. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58539-6_46
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Zhang, Y., Yao, Y., Hossain, Z., Rahman, S., Gedeon, T. (2021). EEG Feature Significance Analysis. In: Mantoro, T., Lee, M., Ayu, M.A., Wong, K.W., Hidayanto, A.N. (eds) Neural Information Processing. ICONIP 2021. Communications in Computer and Information Science, vol 1517. Springer, Cham. https://doi.org/10.1007/978-3-030-92310-5_25
Download citation
DOI: https://doi.org/10.1007/978-3-030-92310-5_25
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-92309-9
Online ISBN: 978-3-030-92310-5
eBook Packages: Computer ScienceComputer Science (R0)