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An efficient EEG signal fading processing framework based on the cognitive limbic system and deep learning

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Abstract

Non-invasive electroencephalography (EEG) is a technique for monitoring brain activity that is valuable in the diagnosis and study of the brain. However, due to factors such as brain-computer interface (BCI) devices deficiency, dynamic network limitations, and subject issues, EEG signals may fade throughout the entire process from signal generation to acquisition. The fading of EEG signals can cause changes in the data distribution, blur the information and have a negative impact on subsequent research and application. In order to reduce the adverse effects of data fading, this paper proposes a hierarchical bidirectional long-short term memory (LSTM)-Attention network based on cognitive brain limbic system (HBLANet). HBLANet classifies randomly fading signals multiple times by way of a dimensionality-reducing classification algorithm in order to narrow down the feature interval processed by a single neural network. Then the different types of EEG signals acquired from the classification are processed in a more focused manner using a bidirectional LSTM-Attention network. In this paper, the overall performance of the network is greatly improved by decomposing complex signal processing tasks into smaller tasks. The model is evaluated on the EEG-denoisenet dataset and compared with competitive networks such as feature pyramid network (FPN), UNet, MultiResUNet, 1D-ResCNN etc., and the results show that the proposed network achieves better fading processing outcomes. In the overall experiment, the processed EEG signals achieve the relative root mean squared error (RRMSE) value of 0.009, the signal-to-noise ratio (SNR) of 32.78, and the correlation coefficient (CC) of 0.98. Furthermore, the denoising task in the overall experiment achieves even more exciting results, with a SNR of 40.48 and a CC of 0.991 for the processed EEG signal in the case of processing high SNR signals (the range of the SNR is from -2 to 2). Therefore, we believe that the framework has an important reference value for future research on signal quality restoration.

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References

  1. Chen X et al (2022) Toward open-world electroencephalogram decoding via deep learning: a comprehensive survey. IEEE Signal Process Mag 39(2):117–134

  2. Hassan M, Wendling F (2018) Electroencephalography source connectivity: aiming for high resolution of brain networks in time and space. IEEE Signal Process Mag 35(3):81–96

    Article  Google Scholar 

  3. Liu Y et al (2021) Brain-computer interface for human-multirobot strategic consensus with a differential world model. Appl Intell 51:3645–3663

  4. Taran S et al (2020) Classification of motor-imagery tasks from EEG signals using the rational dilation wavelet transform. Modelling and Analysis of Active Biopotential Signals in Healthcare, vol 2. IOP Publishing, Bristol, UK, pp 1–1

  5. Tasci I et al (2023) Epilepsy detection in 121 patient populations using hypercube pattern from EEG signals. Inf Fusion 96:252–268

  6. Wang W, Li B, Wang H (2022) A novel end-to-end network based on a bidirectional GRU and a self-attention mechanism for denoising of electroencephalography signals. Neuroscience 505:10–20

    Article  CAS  PubMed  Google Scholar 

  7. Wan Z et al (2022) EEG fading data classification based on improved manifold learning with adaptive neighborhood selection. Neurocomputing 482:186–196

  8. Akmal M (2022) Tensor factorization and attention-based CNN-LSTM deep-learning architecture for improved classification of missing physiological sensors data. IEEE Sens J 23(2):1286–1294

    Article  ADS  Google Scholar 

  9. Dora M, Holcman D (2022) Adaptive single-channel EEG artifact removal with applications to clinical monitoring. IEEE Trans Neural Syst Rehabil Eng 30:286–295

    Article  PubMed  Google Scholar 

  10. Mihajlović V, Grundlehner B (2012) The effect of force and electrode material on electrode-to-skin impedance. 2012 IEEE biomedical circuits and systems conference (BioCAS). IEEE

  11. Niu X et al (2021) Surface bioelectric dry electrodes: a review. Measurement 183:109774

  12. Al-Marridi AZ et al (2019) Efficient eeg mobile edge computing and optimal resource allocation for smart health applications. 2019 15th international wireless communications & mobile computing conference (IWCMC). IEEE

  13. Yum M-K et al (2014) Timely event-related synchronization fading and phase de-locking and their defects in migraine. Clin Neurophysiol 125(7):1400–1406

  14. Mahmud S et al (2023) MLMRS-Net: Electroencephalography (EEG) motion artifacts removal using a multi-layer multi-resolution spatially pooled 1D signal reconstruction network. Neural Comput Appl 35(11): 8371–8388

  15. Aggarwal S, Chugh N (2022) Review of machine learning techniques for EEG based brain computer interface. Arch Computat Methods Eng 29:3001–3020

  16. Tasci G et al (2023) QLBP: Dynamic patterns-based feature extraction functions for automatic detection of mental health and cognitive conditions using EEG signals. Chaos Solit Fractals 172:113472

  17. Wang H, Shao Y (2023) Sparse and robust SVM classifier for large scale classification. Appl Intell 53:19647–19671

  18. Dai G et al (2020) HS-CNN: a CNN with hybrid convolution scale for EEG motor imagery classification. J Neural Eng 17(1):016025

  19. Miao Z, Zhao M, Zhang X, Ming D (2023) LMDA-Net: a lightweight multi-dimensional attention network for general EEG-based brain-computer interfaces and interpretability. NeuroImage 276:120209

  20. Ma S, Zhang T, Zhao YB, Kang Y, Bai P (2023) TCLN: a transformer-based Conv-LSTM network for multivariate time series forecasting. Appl Intell 53(23):28401–28417

  21. Zuo X et al (2022) Driver distraction detection using bidirectional long short-term network based on multiscale entropy of EEG. IEEE Trans Intell Transp Syst 23(10):19309–19322

  22. Li Y et al (2021) A cognitive brain model for multimodal sentiment analysis based on attention neural networks. Neurocomputing 430:159–173

  23. Jiang X, Bian G-B, Tian Z (2019) Removal of artifacts from EEG signals: a review. Sensors 19(5):987

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  24. Tamburro G et al (2018) A new ICA-based fingerprint method for the automatic removal of physiological artifacts from EEG recordings. PeerJ 6:e4380

  25. Gorjan D et al (2022) Removal of movement-induced EEG artifacts: current state of the art and guidelines. J Neural Eng 19(1):011004

  26. Zangeneh Soroush M et al (2022) EEG artifact removal using sub-space decomposition, nonlinear dynamics, stationary wavelet transform and machine learning algorithms. Front Physiol 13:910368

  27. Ghosh R et al (2023) Automatic eyeblink and muscular artifact detection and removal from EEG signals using k-nearest neighbor classifier and long short-term memory networks. IEEE Sensors J 23(5):5422–5436

  28. Zhang H et al (2021) EEGdenoiseNet: a benchmark dataset for deep learning solutions of EEG denoising. J Neural Eng 18(5):056057

  29. Pester B et al (2018) Influence of imputation strategies on the identification of brain functional connectivity networks. J Neurosci Methods 309:199–207

  30. Akmal M, Zubair S, Alquhayz H (2021) Classification analysis of tensor-based recovered missing EEG data. IEEE Access 9:41745–41756

    Article  Google Scholar 

  31. Lemke H et al (2022) The course of disease in major depressive disorder is associated with altered activity of the limbic system during negative emotion processing. Biol Psychiatry Cogn Neurosci Neuroimaging 7(3):323–332

  32. Buzzell GA et al (2022) A practical introduction to EEG time-frequency principal components analysis (TF-PCA). Dev Cogn Neurosci 55:101114

  33. Antony MJ et al (2022) Classification of EEG using adaptive SVM classifier with CSP and online recursive independent component analysis. Sensors 22(19):7596

  34. Scholkopf B et al (1997) Comparing support vector machines with Gaussian kernels to radial basis function classifiers. IEEE Trans Signal Process 45(11):2758–2765

  35. Galassi A, Lippi M, Torroni P (2020) Attention in natural language processing. IEEE Trans Neural Netw Learn Syst 32(10):4291–4308

    Article  Google Scholar 

  36. Peng Y, Dalmia S, Lane I, Watanabe S (2022) Branchformer: parallel mlp-attention architectures to capture local and global context for speech recognition and understanding. In: International Conference on Machine Learning. PMLR, pp 17627–17643

  37. Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, ... Polosukhin I (2017) Attention is all you need. Adv Neural Inf Process Syst 30:5998–6008

  38. Chen X et al (2017) Independent vector analysis applied to remove muscle artifacts in EEG data. IEEE Trans Instrum Meas 66(7):1770–1779

  39. Ronneberger O, Fischer P, Brox T (2015) U-net: convolutional networks for biomedical image segmentation. In: Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th International Conference, Munich, Germany, 5–9 October 2015, Proceedings, Part III 18. Springer International Publishing, pp 234–241

  40. Sun W et al (2020) A novel end-to-end 1D-ResCNN model to remove artifact from EEG signals. Neurocomputing 404:108–121

  41. Lin TY, Dollár P, Girshick R, He K, Hariharan B, Belongie S (2017) Feature pyramid networks for object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2117–2125

  42. Ibtehaz N, Sohel Rahman M (2020) MultiResUNet: Rethinking the U-Net architecture for multimodal biomedical image segmentation. Neural Netw 121:74–87

  43. Lawhern VJ et al (2018) EEGNet: a compact convolutional neural network for EEG-based brain–computer interfaces. J Neural Eng 15(5):056013

  44. Ding Y, Robinson N, Tong C, Zeng Q, Guan C (2023) LGGNet: learning from local-global-graph representations for brain–computer interface. IEEE Trans Neural Netw Learn Syst 1–14

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Authors and Affiliations

  1. The School of Electrical Engineering, Shanghai Dianji University, Shanghai, 201306, China

    Wenlong Wang, Baojiang Li, Haiyan Wang & Xichao Wang

  2. Intelligent Decision and Control Technology Institute, Shanghai Dianji University, Shanghai, 201306, China

    Wenlong Wang, Baojiang Li, Haiyan Wang & Xichao Wang

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  1. Wenlong Wang
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Wang, W., Li, B., Wang, H. et al. An efficient EEG signal fading processing framework based on the cognitive limbic system and deep learning. Appl Intell 54, 1566–1584 (2024). https://doi.org/10.1007/s10489-023-05258-0

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