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Comparison of two methods of removing EOG artifacts for use in a motor imagery-based brain computer interface

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Abstract

Blind Source Separation (BSS) methods are signal processing tools that are widely utilized for Electroencephalogram (EEG) data analysis. The BSS separates a set of recorded EEG signals into a set of components. Note that the EEG measurements are often contaminated with various types of artifacts such as eye movements and blinks (Electrooculogram, EOG) that make data analysis very difficult. Therefore, we present a comparative study between two methods of removing EOG artifacts to be use in a Motor Imagery-based Brain Computer Interface (MI-BCI). In literature, a method of artifact removal based on Independent Component Analysis (ICA) using the concepts of Renyi’s entropy and kurtosis has been presented that we call this method ICA (the first method). However, applying the ICA method to EEG signal directly can cause the significant distortion of the clean EEG signal because this method completely removes independent components capturing artifacts. In this study, we improve this method by applying Wavelet Denoising (WD) only to segments affected by artifacts from the detected artifactual components instead of the complete removal of these components. This method is called ICA–WD (the second method). The quality of removing the artifact is evaluated using the Relative Root-Mean-Squared Error and Average Correlation Coefficient criteria which are computed between the original and processed signals. The results show that the ICA–WD removes the EOG artifacts better than the ICA. Next, we apply the ICA–WD method to dataset 2a of BCI competition IV for the EOG artifact removal. Then, the cleaned EEG signals are employed for feature extraction and classification. The results indicate that the proposed method outperforms the three best competitors and another study performed on the BCI competition IV dataset 2a.

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References

  • Al-Qazzaz NK, Ali S, Ahmad SA, Islam MS (2014) Ariff MI Selection of mother wavelets thresholding methods in denoising multi-channel EEG signals during working memory task. In: IEEE conference on biomedical engineering and sciences (IECBES), pp 214–219

  • Ang KK, Chin ZY, Zhang H, Guan C (2008) Filter bank common spatial pattern (FBCSP) in brain-computer interface. In: IEEE joint conference on neural networks (IEEE world congress on computational intelligence), pp 2390–2397

  • Ang KK, Chin ZY, Wang C, Guan C, Zhang H (2012) Filter bank common spatial pattern algorithm on BCI competition IV datasets 2a and 2b. Front Neurosci 6:1–9

    Google Scholar 

  • Ashok A, Bharathan AK, Soujya VR, Nandakumar P (2013) Tikhonov regularized spectrally weighted common spatial patterns. In: IEEE conference on control communication and computing, pp 315–318

  • Bell AJ, Sejnowski TJ (1995) An information-maximization approach to blind separation and blind deconvolution. Neural Comput 7(6):1129–1159

    Google Scholar 

  • Belouchrani A, Abed-Meraim K, Cardoso JF, Moulines E (1997) A blind source separation technique using second order statistics. IEEE Trans Signal Process 45(2):434–444

    Article  Google Scholar 

  • Birbaumer N, Cohen LG (2007) Brain-computer-interfaces (BCI): communication and restoration of movement in paralysis. J Physiol 579(3):621–636

    Google Scholar 

  • Blankertz B, Tomioka R, Lemm S, Kawanabe M, Muller KR (2008) Optimizing spatial filters for robust EEG single-trial analysis. IEEE Signal Process Mag 25(1):41–56

    Google Scholar 

  • Blum DE (1998) Computer-based electroencephalography: technical basics, basis for new applications, and potential pitfalls. Clin Neurophysiol 106(2):118–126

    Google Scholar 

  • Brunner C, Naeem M, Leeb R, Graimann B, Pfurtscheller G (2007) Spatial filtering and selection of optimized components in four class motor imagery EEG data using independent components analysis. Pattern Recogn Lett 28(8):957–964

    Google Scholar 

  • Brunner C, Leeb R, Müller-Putz GR, Schlögl A, Pfurtscheller G (2008) BCI competition 2008–Graz dataset A. http://ida.first.fraunhofer.de/projects/bci/competitioniv/desc2a.pdfCardoso

  • Castellanos N, Makarov V (2006) Recovering EEG brain signals: artifact suppression with wavelet enhanced independent component analysis. J Neurosci Methods 158(2):300–312

    Google Scholar 

  • Chen X, Peng H, Yu F, Wang K (2017) Independent vector analysis applied to remove muscle artifacts in EEG data. IEEE Trans Instrume Measurement 66(7):1770–1779

    Google Scholar 

  • Cohen J (1960) A coefficient of agreement for nominal scales. Educ Psychol Measur 20(1):37–46

    Google Scholar 

  • Comon P, Jutten C (2010) Handbook of blind source separation: independent component analysis and applications. ELSEVIER, Oxford

    Google Scholar 

  • Congedo M (2013) EEG source analysis. Ph.D. thesis, University of Grenoble, France

  • Congedo M, Gouy-Pailler C, Jutten C (2008) On the blind source separation of human electroencephalogram by approximate joint diagonalization of second order statistics. Clin Neurophysiol 119(12):2677–2686

    Google Scholar 

  • Dornhege G, Blankertz B, Krauledat M, Losch F, Curio G, Muller KR (2006) Combined optimization of spatial and temporal filters for improving brain-computer interfacing. IEEE Trans Biomed Eng 53(11):2274–2281

    Google Scholar 

  • Duprès A, Cabestaing F, Rouillard J (2016) Supervision of time-frequency features selection in EEG signals by a human expert for brain-computer interfacing based on motor imagery. In: IEEE Conference on Systems, Man, and Cybernetics, pp 000861–000866

  • Ghaheri H, Ahmadyfard A (2012) Temporal windowing in CSP method for multi-class motor imagery classification. In: 20th Iranian conference on electrical engineering, pp 1602–1607‏

  • Gouy-Pailler C, Congedo M, Brunner C, Jutten C, Pfurtscheller G (2010) Nonstationary brain source separation for multiclass motor imagery. IEEE Trans Biomed Eng 57(2):469–478

    Google Scholar 

  • Greco A, Mammone N, Morabito FC, Versaci M (2006) Kurtosis, Renyi’s entropy and independent component scalp maps for the automatic artifact rejection from EEG data. Int J Signal Process 2(4):240–244

    Google Scholar 

  • Grosse-Wentrup M, Buss M (2008) Multiclass common spatial patterns and information theoretic feature extraction. IEEE Trans Biomed Eng 55(8):1991–2000

    Google Scholar 

  • Hyvärinen A (1999) Fast and robust fixed-point algorithms for independent component analysis. IEEE Trans Neural Netw 10(3):626–634

    Google Scholar 

  • Hyvärinen A, Oja E (1997) A fast fixed-point algorithm for independent component analysis. Neural Comput 9(7):1483–1492

    Google Scholar 

  • Inuso G, La Foresta F, Mammone N, Morabito F (2007) Wavelet-ICA methodology for efficient artifact removal from electroencephalographic recordings. In: International joint conference on electrical engineering, pp 1524–1529

  • James CJ, Hesse CW (2004) Independent component analysis for biomedical signals. Physiol Meas 26(1):R15–R39

    Google Scholar 

  • Jung TP, Makeig S, Humphries C, Lee TW, Mckeown MJ, Iragui V, Sejnowski TJ (2000) Removing electroencephalographic artifacts by blind source separation. Psychophysiology 37(2):163–178

    Google Scholar 

  • Kitamura D, Ono N, Sawada H, Kameoka H, Saruwatari H (2016) Determined blind source separation unifying independent vector analysis and nonnegative matrix factorization. IEEE/ACM Trans Audio Speech Lang Process 24(9):1626–1641

    Google Scholar 

  • Klados MA, Bamidis PD (2016) A semi-simulated EEG/EOG dataset for the comparison of EOG artifact rejection techniques. Data Brief 8:1004–1006

    Google Scholar 

  • Krishnaveni V, Jayaraman S, Anitha L, Ramadoss K (2006) Removal of ocular artifacts from EEG using adaptive thresholding of wavelet coefficients. J Neural Eng 3:338–346

    Google Scholar 

  • Lemm S, Blankertz B, Curio G, Muller KR (2005) Spatio-spectral filters for improved classification of single trial EEG. IEEE Trans Biomed Eng 52(9):1541–1548

  • Li M, Cui Y, Yang J (2013) Automatic removal of ocular artifact from EEG with DWT and ICA method. Appl Math Inform Sci 7(2):809–816

    Google Scholar 

  • Lins OG, Picton TW, Berg P, Scherg M (1993) Ocular artifacts in EEG and event-related potentials I: scalp topography. Brain Topogr 6(1):51–63

    Google Scholar 

  • Loboda A, Margineanu A, Rotariu G, Lazar AM (2014) Discrimination of EEG-based motor imagery tasks by means of a simple phase information method. Int J Adv Res Artif Intell 3(10):1–5

    Google Scholar 

  • Lotte F, Guan C (2011) Regularizing common spatial patterns to improve BCI designs: unified theory and new algorithms. IEEE Trans Biomed Eng 58(2):355–362

    Google Scholar 

  • Makeig S, Bell AJ, Jung TP, Sejnowski TJ (1996) Independent component analysis of electroencephalographic data. In: International conference on advances in neural information processing systems, pp 145–151

  • Makeig S, Bell T, Lee TW, Jung TP, S. Enghoff (2000) EEGLAB: ICA toolbox for psychophysiological research,” WWW Site, Swartz Center for Computational Neuroscience, Institute of Neural Computation, University of San Diego California www.sccn.ucsd.edu/eeglab/

  • Mallis D, Sgouros T, Mitianoudis N (2018) Convolutive audio source separation using robust ICA and an intelligent evolving permutation ambiguity solution. Evol Syst 9(4):315–329

    Google Scholar 

  • Mammone N, La Foresta F, Morabito FC (2012) Automatic artifact rejection from multichannel scalp EEG by wavelet ICA. IEEE Sens J 12(3):533–542

    Google Scholar 

  • Melissant C, Ypma A, Frietman EE, Stam CJ (2005) A method for detection of Alzheimer’s disease using ICA-enhanced EEG measurements. Artif Intell Med 33(3):209–222

    Google Scholar 

  • Mosavi MR, Ayatollahi A, Afrakhteh S (2019) An efficient method for classifying motor imagery using CPSO-trained ANFIS prediction, Evol Syst. https://doi.org/10.1007/s12530-019-09280-x

  • Müller-Gerking J, Pfurtscheller G, Flyvbjerg H (1999) Designing optimal spatial filters for single-trial EEG classification in a movement task. Clin Neurophysiol 110(5):787–798

    Google Scholar 

  • Ng SC, Raveendran P (2009) Enhanced μ rhythm extraction using blind source separation and wavelet transform. IEEE Trans Biomed Eng 56(8):2024–2034

    Google Scholar 

  • Nicolas-Alonso LF, Gomez-Gil J (2012) Brain computer interfaces, a review. Sensors 12(2):1211–1279

    Google Scholar 

  • Noureddin B, Lawrence PD, Birch GE (2007) Time–frequency analysis of eye blinks and saccades in EOG for EEG artifact removal. In: IEEE international conference on neural engineering, pp 564–567

  • Ochoa C, Polich J (2000) P300 and blink instructions. Clin Neurophysiol 111(1):93–98

    Google Scholar 

  • Pohjalainen J, Räsänen O, Kadioglu S (2015) Feature selection methods and their combinations in high-dimensional classification of speaker likability, intelligibility and personality traits. Comput Speech Lang 29(1):145–171

    Google Scholar 

  • Ramoser H, Müller-Gerking J, Pfurtscheller G (2000) Optimal spatial filtering of single trial EEG during imagined hand movement. IEEE Trans Rehabil Eng 8(4):441–446

    Google Scholar 

  • Senay S (2019) An evolutionary spectral representation for blind separation of biosignals. Evol Syst 10(2):285–294

    MathSciNet  Google Scholar 

  • Sweeney KT, Ward TE, McLoone SF (2012) Artifact removal in physiological signals-Practices and possibilities. IEEE Trans Inf Technol Biomed 16(3):488–500

    Google Scholar 

  • Tangermann M, Müller KR, Aertsen A, Birbaumer N, Braun C, Brunner C et al (2012) Review of the BCI competition IV. Front Neurosci 6(55):1–31

    Google Scholar 

  • Unser M, Aldroubi A (1996) A review of wavelets in biomedical applications. Proc IEEE 84(4):626–638

    Google Scholar 

  • Vigário RN (1997) Extraction of ocular artifacts from EEG using independent component analysis. Electroencephalogr Clin Neurophysiol 103(3):395–404

    Google Scholar 

  • Wolpaw JR, Birbaumer N, McFarland DJ, Pfurtscheller G, Vaughan TM (2002) Brain-computer interfaces for communication and control. Clin Neurophysiol 113(6):767–791

    Google Scholar 

  • Zima M, Tichavský P, Paul K, Krajča V (2012) Robust removal of short-duration artifacts in long neonatal EEG recordings using wavelet-enhanced ICA and adaptive combining of tentative reconstructions. Physiol Meas 33(8):39–49

    Google Scholar 

Download references

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  1. Department of Electrical Engineering, Iran University of Science and Technology, Narmak, Tehran, 16846-13114, Iran

    M. Mohammadi & M. R. Mosavi

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  1. M. Mohammadi
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  2. M. R. Mosavi
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Mohammadi, M., Mosavi, M.R. Comparison of two methods of removing EOG artifacts for use in a motor imagery-based brain computer interface. Evolving Systems 12, 527–540 (2021). https://doi.org/10.1007/s12530-019-09311-7

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