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Single-channel electroencephalography decomposition by detector-atom network and its pre-trained model

Published 5 Aug 2024 in eess.SP | (2408.02185v3)

Abstract: This paper presents a novel single-channel decomposition approach to facilitate the decomposition of electroencephalography (EEG) signals recorded with limited channels. Our model posits that an EEG signal comprises short, shift-invariant waves, referred to as atoms. We design a decomposer as an artificial neural network aimed at estimating these atoms and detecting their time shifts and amplitude modulations within the input signal. The efficacy of our method was validated across various scenarios in brain--computer interfaces and neuroscience, demonstrating enhanced performance. Additionally, cross-dataset validation indicates the feasibility of a pre-trained model, enabling a plug-and-play signal decomposition module.

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References (53)
  1. Mother of all BCI Benchmarks. http://moabb.neurotechx.com.
  2. A systematic review of EEG source localization techniques and their applications on diagnosis of brain abnormalities. Journal of Neuroscience Methods, 339(February), https://doi.org/10.1016/j.jneumeth.2020.108740.
  3. A review of multitaper spectral analysis. IEEE Transactions on Biomedical Engineering, 61(5), 1555–1564, https://doi.org/10.1109/TBME.2014.2311996.
  4. Classification of seizure and nonseizure EEG signals using empirical mode decomposition. IEEE Transactions on Information Technology in Biomedicine, 16(6), 1135–1142, https://doi.org/10.1109/TITB.2011.2181403.
  5. Multivariate temporal dictionary learning for EEG. Journal of Neuroscience Methods, 215(1), 19–28, https://doi.org/10.1016/j.jneumeth.2013.02.001.
  6. Single-trial analysis and classification of ERP components–A tutorial. NeuroImage, 56(2), 814–25, https://doi.org/10.1016/j.neuroimage.2010.06.048.
  7. Optimizing spatial filters for robust EEG single-trial analysis. IEEE Signal Processing Magazine, 25(1), 41–56.
  8. Learning recurrent waveforms within EEGs. IEEE Transactions on Biomedical Engineering, 63(1), 43–54, https://doi.org/10.1109/TBME.2015.2499241.
  9. Atomic decomposition by basis pursuit. SIAM Journal on Scientific Computing, 20(1), 33–61, https://doi.org/10.1137/S1064827596304010.
  10. Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications. Wiley.
  11. Removal of ocular artifact from the EEG: A review. Neurophysiologie Clinique/Clinical Neurophysiology, 30(1), 5–19, https://doi.org/10.1016/S0987-7053(00)00055-1.
  12. The deep-match framework: R-peak detection in Ear-ECG. IEEE Transactions on Biomedical Engineering, 71(7), 2014–2021, https://doi.org/10.1109/TBME.2024.3359752.
  13. Interactions between different EEG frequency bands and their effect on alpha–fMRI correlations. NeuroImage, 47(1), 69–76, https://doi.org/10.1016/j.neuroimage.2009.04.029.
  14. Preventing dataset shift from breaking machine-learning biomarkers. GigaScience, 10(9), https://doi.org/10.1093/gigascience/giab055.
  15. The Nencki-Symfonia electroencephalography/event-related potential dataset: Multiple cognitive tasks and resting-state data collected in a sample of healthy adults. GigaScience, 11, https://doi.org/10.1093/gigascience/giac015.
  16. Friedman, J. H. (1989). Regularized discriminant analysis. Journal of the American Statistical Association, 84(405), 165–175, https://doi.org/10.1080/01621459.1989.10478752.
  17. Convolutional dictionary learning: A comparative review and new algorithms. IEEE Transactions on Computational Imaging, 4(3), 366–381, https://doi.org/10.1109/tci.2018.2840334.
  18. Gramfort, A. (2013). MEG and EEG data analysis with MNE-Python. Frontiers in Neuroscience, 7(7 DEC), https://doi.org/10.3389/fnins.2013.00267.
  19. Shift-invariant sparse coding for audio classification. In 23rd Conference on Uncertainty in Artificial Intelligence (UAI’07) (pp. 66–79).
  20. EEG-based brain-computer interfaces (BCIs): A survey of recent studies on signal sensing technologies and computational intelligence approaches and their applications. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 18(5), 1645–1666, https://doi.org/10.1109/TCBB.2021.3052811.
  21. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of The Royal Society A: Mathematical, Physical and Engineering Sciences, 454(1971), 903–995, https://doi.org/10.1098/rspa.1998.0193.
  22. Independent component analysis: Algorithms and applications. Neural Networks, 13(4-5), 411–430, https://doi.org/10.1016/S0893-6080(00)00026-5.
  23. Learning the morphology of brain signals using alpha-stable convolutional sparse coding. In Advances in Neural Information Processing Systems.
  24. Single-channel P300 decomposition using detector-kernel networks. In 2024 International Technical Conference on Circuits/Systems, Computers, and Communications (ITC-CSCC) (pp. 1–5).
  25. Dictionary learning algorithms for sparse representation. Neural Computation, 15(2), 349–396, https://doi.org/10.1162/089976603762552951.
  26. A comparison of classification techniques for the P300 Speller. Journal of Neural Engineering, 3(4), 299.
  27. EEGNet: A compact convolutional neural network for EEG-based brain-computer interfaces. Journal of Neural Engineering, 15(5), 1–30, https://doi.org/10.1088/1741-2552/aace8c.
  28. Coding time-varying signals using sparse, shift-invariant representations. In Advances in Neural Information Processing Systems.
  29. Frequency recognition based on canonical correlation analysis for SSVEP-based BCIs. IEEE Transactions on Biomedical Engineering, 53(12), 2610–2614.
  30. BETA: A large benchmark database toward SSVEP-BCI application. Frontiers in Neuroscience, 14(June), https://doi.org/10.3389/fnins.2020.00627.
  31. A review of classification algorithms for EEG-based brain-computer interfaces: A 10 year update. Journal of Neural Engineering, 15(3), aab2f2, https://doi.org/10.1088/1741-2552/aab2f2.
  32. Separation of sources from single-channel EEG signals using independent component analysis. IEEE Transactions on Instrumentation and Measurement, 67(2), 382–393, https://doi.org/10.1109/TIM.2017.2775358.
  33. Mallat, S. (1998). A Wavelet Tour of Signal Processing. Academic Press.
  34. Objective and subjective evaluation of online error correction during P300-based spelling. Advances in Human-Computer Interaction, 2012, https://doi.org/10.1155/2012/578295.
  35. Harmonizing and aligning M/EEG datasets with covariance-based techniques to enhance predictive regression modeling. Imaging Neuroscience, 1, 1–23, https://doi.org/10.1162/imag_a_00040.
  36. Image segmentation using deep learning: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(7), 3523–3542, https://doi.org/10.1109/TPAMI.2021.3059968.
  37. Reinforcement-related brain potentials from medial frontal cortex: origins and functional significance. Neuroscience & Biobehavioral Reviews, 28(4), 441–448, https://doi.org/10.1016/j.neubiorev.2004.05.003.
  38. FASTER: Fully Automated Statistical Thresholding for EEG artifact Rejection. Journal of Neuroscience Methods, 192(1), 152–162, https://doi.org/10.1016/j.jneumeth.2010.07.015.
  39. Upper limb movements can be decoded from the time-domain of low-frequency EEG. PLoS ONE, 12(8), 1–24, https://doi.org/10.1371/journal.pone.0182578.
  40. Continual lifelong learning with neural networks: A review. Neural Networks, 113, 54–71, https://doi.org/10.1016/j.neunet.2019.01.012.
  41. Response error correction—A demonstration of improved human-machine performance using real-time EEG monitoring. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 11(2), 173–177, https://doi.org/10.1109/TNSRE.2003.814446.
  42. Dataset Shift in Machine Learning. MIT Press.
  43. Deep learning with convolutional neural networks for EEG decoding and visualization. Human Brain Mapping, 38(11), 5391–5420, https://doi.org/10.1002/hbm.23730.
  44. Rethinking the CSC model for natural images. In Advances in Neural Information Processing Systems (pp. 2274–2284).
  45. Convolutional neural networks demystified: A matched filtering perspective-based tutorial. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 53(6), 3614–3628, https://doi.org/10.1109/TSMC.2022.3228597.
  46. Empirical mode decomposition and its extensions applied to EEG analysis: A review. Advances in Data Science and Adaptive Analysis, 10(02), 1840001, https://doi.org/10.1142/S2424922X18400016.
  47. Convolutional neural network-based dictionary learning for SAR target recognition. IEEE Geoscience and Remote Sensing Letters, 18(10), 1776–1780, https://doi.org/10.1109/LGRS.2020.3008212.
  48. A review of wavelets in biomedical applications. Proceedings of the IEEE, 84(4), 626–638, https://doi.org/10.1109/5.488704.
  49. Beamforming: A versatile approach to spatial filtering. IEEE ASSP Magazine, 5(2), 4–24, https://doi.org/10.1109/53.665.
  50. Vapnik, V. N. (1998). Statistical Learning Theory. Wiley-Interscience.
  51. Virtanen, T. (2007). Monaural sound source separation by nonnegative matrix factorization with temporal continuity and sparseness criteria. IEEE Transactions on Audio, Speech and Language Processing, 15(3), 1066–1074, https://doi.org/10.1109/TASL.2006.885253.
  52. Blind noise reduction for multisensory signals using ICA and subspace filtering, with application to EEG analysis. Biological Cybernetics, 86(4), 293–303, https://doi.org/10.1007/s00422-001-0298-6.
  53. A benchmark dataset for SSVEP-based brain–computer interfaces. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 25(10), 1746–1752, https://doi.org/10.1109/TNSRE.2016.2627556.

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