Electroencephalogram Signal Classification and Artifact Removal with Deep Networks and Adaptive Thresholding
Journal of Shanghai Jiaotong University (Science) - Trang 1-9 - 2023
Tóm tắt
Physiological signals such as electroencephalogram (EEG) signals are often corrupted by artifacts during the acquisition and processing. Some of these artifacts may deteriorate the essential properties of the signal that pertains to meaningful information. Most of these artifacts occur due to the involuntary movements or actions the human does during the acquisition process. So, it is recommended to eliminate these artifacts with signal processing approaches. This paper presents two mechanisms of classification and elimination of artifacts. In the first step, a customized deep network is employed to classify clean EEG signals and artifact-included signals. The classification is performed at the feature level, where common space pattern features are extracted with convolutional layers, and these features are later classified with a support vector machine classifier. In the second stage of the work, the artifact signals are decomposed with empirical mode decomposition, and they are then eliminated with the proposed adaptive thresholding mechanism where the threshold value changes for every intrinsic mode decomposition in the iterative mechanism.
Tài liệu tham khảo
SARIN M, VERMA A, MEHTA D H, et al. Automated ocular artifacts identification and removal from EEG data using hybrid machine learning methods [C]//2020 7th International Conference on Signal Processing and Integrated Networks. Noida: IEEE, 2020: 1054–1059.
B ABD RANI M S, BT MANSOR W. Detection of eye blinks from EEG signals for home lighting system activation [C]//2009 6th International Symposium on Mechatronics and its Applications. Sharjah: IEEE, 2009: 1–4.
MUTHUKUMARASWAMY S D. High-frequency brain activity and muscle artifacts in MEG/EEG: A review and recommendations [J]. Frontiers in Human Neuroscience, 2013, 7: 138.
MATHE M, MIDIDODDI P, KRISHNA B T. Artifact removal methods in EEG recordings: A review [J]. Proceedings of Engineering and Technology Innovation, 2022, 20: 35–56.
ZHENG W L, LU B L. Investigating critical frequency bands and channels for EEG-based emotion recognition with deep neural networks [J]. IEEE Transactions on Autonomous Mental Development, 2015, 7(3): 162–175.
SHUKLA S, ROY V, PRAKASH A. Wavelet based empirical approach to mitigate the effect of motion artifacts from EEG signal [C]//2020 IEEE 9th International Conference on Communication Systems and Network Technologies. Gwalior: IEEE, 2020: 323–326.
HASSAN M, BOUDAOUD S, TERRIEN J, et al. Combination of canonical correlation analysis and empirical mode decomposition applied to denoising the labor electrohysterogram [J]. IEEE Transactions on Bio-Medical Engineering, 2011, 58(9): 2441–2447.
AUNG S T, WONGSAWAT Y. Analysis of EEG signals contaminated with motion artifacts using multi-scale modified-distribution entropy [J]. IEEE Access, 2021, 9: 33911–33921.
GAJBHIYE P, MINGCHINDA N, CHEN W, et al. Wavelet domain optimized Savitzky–Golay filter for the removal of motion artifacts from EEG recordings [J]. IEEE Transactions on Instrumentation and Measurement, 2021, 70: 1–11.
PHADIKAR S, SINHA N, GHOSH R. Automatic eye-blink artifact removal from EEG signal using wavelet transform with heuristically optimized threshold [J]. IEEE Journal of Biomedical and Health Informatics, 2021, 25(2): 475–484.
ISLAM M K, RASTEGARNIA A. Wavelet-based artifact removal algorithm for EEG data by optimizing mother wavelet and threshold parameters [C]//2020 Emerging Technology in Computing, Communication and Electronics. Bangladesh: IEEE, 2020: 1–6.
MASHHADI N, KHUZANI A Z, HEIDARI M, et al. Deep learning denoising for EOG artifacts removal from EEG signals [C]// 2020 IEEE Global Humanitarian Technology Conference. Seattle: IEEE, 2020: 1–6.
LECUN Y, BENGIO Y, HINTON G. Deep learning [J]. Nature, 2015, 521(7553): 436–444.
TABAR Y R, HALICI U. A novel deep learning approach for classification of EEG motor imagery signals [J]. Journal of Neural Engineering, 2017, 14(1): 016003.
BASHIVAN P, YEASIN M, BIDELMAN G M. Temporal progression in functional connectivity determines individual differences in working memory capacity [C]//2017 International Joint Conference on Neural Networks. Anchorage: IEEE, 2017: 2943–2949.
RADÜNTZ T, SCOUTEN J, HOCHMUTH O, et al. EEG artifact elimination by extraction of ICA-component features using image processing algorithms [J]. Journal of Neuroscience Methods, 2015, 243: 84–93.
LÄNGKVIST M, KARLSSON L, LOUTFI A. A review of unsupervised feature learning and deep learning for time-series modeling [J]. Pattern Recognition Letters, 2014, 42: 11–24.
NGUYEN A, YOSINSKI J, CLUNE J. Deep neural networks are easily fooled: High confidence predictions for unrecognizable images [C]//2015 IEEE Conference on Computer Vision and Pattern Recognition. Boston: IEEE, 2015: 427–436.
MATHE M, PADMAJA M, TIRUMALA KRISHNA B. Intelligent approach for artifacts removal from EEG signal using heuristic-based convolutional neural network [J]. Biomedical Signal Processing and Control, 2021, 70: 102935.
WAHIBA M, BEKKA R E. New denoising method based on empirical mode decomposition and improved thresholding function [J]. Journal of Physics Conference Series, 2017, 787(1): 012014.
KOPSINIS Y, MCLAUGHLIN S. Development of EMD-based denoising methods inspired by wavelet thresholding [J]. IEEE Transactions on Signal Processing, 2009, 57(4): 1351–1362.
BHATTACHARYYA A, SINGH L, PACHORI R B. Fourier-Bessel series expansion based empirical wavelet transform for analysis of non-stationary signals [J]. Digital Signal Processing, 2018, 78: 185–196.
SREEJA S R, SAHAY R R, SAMANTA D, et al. Removal of eye blink artifacts from EEG signals using sparsity [J]. IEEE Journal of Biomedical and Health Informatics, 2018, 22(5): 1362–1372.
MAMMONE N, LA FORESTA F, MORABITO F C. Automatic artifact rejection from multichannel scalp EEG by wavelet ICA [J]. IEEE Sensors Journal, 2012, 12(3): 533–542.
MAMMONE N, MORABITO F C. Enhanced automatic wavelet independent component analysis for electroencephalographic artifact removal [J]. Entropy, 2014, 16(12): 6553–6572.