这些技术的目标是消除伪影,同时保持尽可能多的EEG图信息。这种分类包括以下技术:简单的线性滤波器去除某些频段(Panych et al ., 1989);回归方法使用参考信号从EEG中去除EOG或ECG信号(Wallstrom et al ., 2004),自适应滤波器与参考信号(Marque et al ., 2005),维纳滤波器(Sweeney et al ., 2012)或贝叶斯过滤器(Sameni et al ., 2007)。
这些方法将每个单独的通道分解为基本波形,剔除含有伪迹的波形,重建脑电信号的干净通道。这些方法的主要例子是小波分解(Unser & Aldroubi, 1996),以及一些较少研究的变体,如经验模式分解(EMD)(Safieddine et al.,2012)或非线性模式分解(NMD)(Iatsenko et al.2015)。
Akhtar, M. T., Mitsuhashi, W., & James, C. J. (2012). Employing spatially constrained ICA and wavelet denoising, for automatic removal of artifacts from multichannel EEG data. Signal processing, 92(2), 401-416.
Bell, A. J., & Sejnowski, T. J. (1995). An information-maximisation approach to blind separation and blind deconvolution. Neural Computation, (February 1995), 1004–1034.
Choi, S., Cichocki, A., Park, H. M., & Lee, S. Y. (2005). Blind source separation and independent component analysis: A review. Neural Information Processing-Letters and Reviews, 6(1), 1-57.
Clark, J. W. (1998). The origin of biopotentials. Medical instrumentation: application and design, 3, 121-182.
Correa, A. G., Laciar, E., Patiño, H. D., & Valentinuzzi, M. E. (2007). Artifact removal from EEG signals using adaptive filters in cascade. In Journal of Physics: Conference Series (Vol. 90, No. 1, p. 012081)
Iatsenko, D., McClintock, P. V., & Stefanovska, A. (2015). Nonlinear mode decomposition: a noise-robust, adaptive decomposition method. Physical Review E, 92(3), 032916.
Ismal, K., Rastegarnia, A., & Yang, Z. (2016). Methods for artifact detection and removal from scalp EEG: A review. Clinical Neurophysiology, 46, 287–305.
Marque, C., Bisch, C., & Dantas, R. (2005). Adaptive filtering for ECG rejection from surface EMG recordings. Journal of Electromyography and Kinesiology, 15, 310–315.
Mijovic, B., De Vos, M., Gligorijevic, I., Taelman, J., & Van Huffel, S. (2010). Source separation from single-channel recordings by combining empirical-mode decomposition and independent component analysis. IEEE transactions on biomedical engineering, 57(9), 2188-2196.
Nolan, H., Whelan, R., & Reilly, R. B. (2010). FASTER : Fully Automated Statistical Thresholding for EEG artifact Rejection. Journal of Neuroscience Methods, 192, 152–162.
Nunez, P. L., & Srinivasan, R. (2006). Electric fields of the brain: The neurophysics of EEG. Oxford: Oxford University Press.
Panych, L. P., Wada, J. A., & Beddoes, M. P. (1989). Practical digital filters for reducing EMG artefact in EEG seizure recordings. Electroencephalography and Clinical Neurophysiology, 72, 268–276.
Safieddine, D., Kachenoura, A., Albera, L., Birot, G., Karfoul, A., Pasnicu, A., Biraben, A., Wendling, F., Senhadji, L. & Merlet, I. (2012). Removal of muscle artifact from EEG data: comparison between stochastic (ICA and CCA) and deterministic (EMD and wavelet-based) approaches. EURASIP Journal on Advances in Signal Processing, 2012(1), 127.
Sameni, R., Shamsollahi, M. B., Jutten, C., & Clifford, G. D. (2007). A Nonlinear Bayesian Filtering Framework for ECG Denoising. IEEE Transactions on Biomedical Engineering, 54(12), 2172–2185.
Sörnmo, L., & Laguna, P. (2005). Bioelectrical Signal Processing in Cardiac and Neurological Applications (Vol. 1). Elsevier.
Sweeney, K. T., Member, S., Ward, E., Member, S., Mcloone, F., & Member, S. (2012). Artifact Removal in Physiological Signals — Practices and Possibilities. IEEE Transactions on Information Technology in Biomedicine, 16(3), 488–500.
Unser, M., & Aldroubi, A. (1996). A review of wavelets in biomedical applications. Proceedings of the IEEE, 84(4), 626-638.
Urigüen, J. A., & Garcia-Zapirain, B. (2015). EEG artifact removal — state-of-the-art and guidelines. Journal of Neural Engineering, 031001(12).
Wallstrom, G. L., Kass, R. E., Miller, A., Cohn, J. F., & Fox, N. A. (2004). Automatic correction of ocular artifacts in the EEG : a comparison of regression-based and component-based methods. International Journal of Psychophysiology, 53, 105–119.
Winkler, I., Haufe, S., & Tangermann, M. (2011). Automatic Classification of Artifactual ICA-Components for Artifact Removal in EEG Signals. Behavioral and Brain Functions, 7(1), 30.