Removal of EOG artifacts from EEG using a cascade of sparse autoencoder and recursive least squares adaptive filter
Electrooculogram (EOG) artifacts are the most important form of interferences in electroencephalogram (EEG) based brain computer interfaces (BCIs). In traditional methods for EOG artifacts removal, either an additional EOG recording in real time or multi-channel (more than three channels) EEG record...
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| Veröffentlicht in: | Neurocomputing (Amsterdam) Jg. 214; S. 1053 - 1060 |
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19.11.2016
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| Abstract | Electrooculogram (EOG) artifacts are the most important form of interferences in electroencephalogram (EEG) based brain computer interfaces (BCIs). In traditional methods for EOG artifacts removal, either an additional EOG recording in real time or multi-channel (more than three channels) EEG recording is required. To address these limitations of existing methods, a method using a cascade of sparse autoencoder (SAE) and recursive least squares (RLS) adaptive filter is proposed to remove the EOG artifacts from EEG. The proposed approach consists of offline stage and online stage. The high-order statistical moments information in the EOG artifacts can be learned automatically by using only EOG signals during offline stage and so an SAE model is obtained. In the online stage, the learned SAE model is firstly used to identify and extract preliminary EOG artifacts from a given raw EEG signal. Then an RLS adaptive filter uses the identified EOG artifacts as reference signal to remove interference without parallel EOG recordings. Compared with the exiting methods, the proposed method has the following advantages: (i) nonuse of an additional EOG recording in removal process, (ii) few number of EEG channels being used in removal process, and (iii) time-saving. The performance of the proposed method is evaluated by EEG classification accuracy and time consumption. Compared with traditional methods, the proposed method is proven to be more effective and faster. Moreover, experiment results also show good generalization ability in cross-subject testing scenarios.
•The proposed method does not need an additional EOG recording, which is portable for online using.•The proposed method is suitable for any number of EEG channels.•Compared with traditional methods, the proposed method is more time saving and effective. |
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| AbstractList | Electrooculogram (EOG) artifacts are the most important form of interferences in electroencephalogram (EEG) based brain computer interfaces (BCIs). In traditional methods for EOG artifacts removal, either an additional EOG recording in real time or multi-channel (more than three channels) EEG recording is required. To address these limitations of existing methods, a method using a cascade of sparse autoencoder (SAE) and recursive least squares (RLS) adaptive filter is proposed to remove the EOG artifacts from EEG. The proposed approach consists of offline stage and online stage. The high-order statistical moments information in the EOG artifacts can be learned automatically by using only EOG signals during offline stage and so an SAE model is obtained. In the online stage, the learned SAE model is firstly used to identify and extract preliminary EOG artifacts from a given raw EEG signal. Then an RLS adaptive filter uses the identified EOG artifacts as reference signal to remove interference without parallel EOG recordings. Compared with the exiting methods, the proposed method has the following advantages: (i) nonuse of an additional EOG recording in removal process, (ii) few number of EEG channels being used in removal process, and (iii) time-saving. The performance of the proposed method is evaluated by EEG classification accuracy and time consumption. Compared with traditional methods, the proposed method is proven to be more effective and faster. Moreover, experiment results also show good generalization ability in cross-subject testing scenarios.
•The proposed method does not need an additional EOG recording, which is portable for online using.•The proposed method is suitable for any number of EEG channels.•Compared with traditional methods, the proposed method is more time saving and effective. |
| Author | Yang, Banghua Duan, Kaiwen Zhang, Tao |
| Author_xml | – sequence: 1 givenname: Banghua surname: Yang fullname: Yang, Banghua – sequence: 2 givenname: Kaiwen surname: Duan fullname: Duan, Kaiwen – sequence: 3 givenname: Tao surname: Zhang fullname: Zhang, Tao |
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| Cites_doi | 10.1109/TNNLS.2015.2411671 10.1016/j.neucom.2014.10.038 10.1631/FITEE.1400299 10.1111/j.1469-8986.1991.tb03397.x 10.1016/j.neucom.2014.05.029 10.1016/j.neucom.2015.05.082 10.1016/j.neucom.2014.09.040 10.1016/j.neucom.2013.03.070 10.1109/TFUZZ.2012.2210555 10.1109/IWECA.2014.6845678 10.1016/j.jfranklin.2014.10.022 10.21437/Interspeech.2013-130 10.1016/j.neucom.2012.04.016 10.1109/TNSRE.2007.906956 10.1016/S1388-2457(00)00541-1 10.1007/BF02344717 10.1016/j.neucom.2014.01.062 10.1016/j.neucom.2013.11.009 |
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| Keywords | Electrooculogram (EOG) Recursive least squares (RLS) adaptive filtering Brain computer interfaces (BCIs) Electroencephalogram (EEG) Sparse autoencoder (SAE) |
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| References | Nguyen, Musson, Li (bib24) 2012; 97 Albalawi, Song (bib2) 2012 Qiu, Ding, Gao (bib21) 2015; 99 Kenemans, Molenaar, Verbaten (bib3) 1991; 28 Vincent, Larochelle, Lajoie (bib6) 2010; 11 Huang, Sun (bib11) 2015; 174 He, Wilson, Russell (bib8) 2004; 42 Zhang, Xi, Zhang (bib12) 2015; 168 Qiu, Wei, Karimi (bib20) 2015; 352 Kumar, Dewal, Anand (bib17) 2014; 133 Ahirwal, Kumar, Singh (bib26) 2014; 144 Wang, Gao, Qiu (bib19) 2016; 27 Y.L, Zhou, Li (bib25) 2015; 188 Leeb, Brunner, Müller-Putz (bib15) 2008 Cruz, Feng, Chi (bib1) 2015; 149 Leeb, Lee, Keinrath (bib14) 2007; 15 Yang, He, Lin (bib7) 2015; 16 Mi, Xu (bib9) 2014; 137 Hagemann, Naumann (bib23) 2001; 112 Qiu, Feng, Gao (bib22) 2013; 21 Hu, Wang, Wu (bib18) 2015; 151 Makeig, Bell, Jung (bib4) 1996 Ng (bib10) 2011; 72 Lu, Yu, Matsuda (bib5) 2013 Fang, Chen, Zheng (bib16) 2015; 151 B.H. Yang, L.F. He, Removal of ocular artifacts from EEG signals using ICA-RLS in BCI, in: IEEE Workshop on Electronics, Computer and Applications, 2014. IEEE, 2014, pp. 544–547. 〈http://dx.doi.org/10.1109/IWECA.2014.6845678〉. Mi (10.1016/j.neucom.2016.06.067_bib9) 2014; 137 Leeb (10.1016/j.neucom.2016.06.067_bib14) 2007; 15 Kumar (10.1016/j.neucom.2016.06.067_bib17) 2014; 133 Leeb (10.1016/j.neucom.2016.06.067_bib15) 2008 Huang (10.1016/j.neucom.2016.06.067_bib11) 2015; 174 Zhang (10.1016/j.neucom.2016.06.067_bib12) 2015; 168 Fang (10.1016/j.neucom.2016.06.067_bib16) 2015; 151 Vincent (10.1016/j.neucom.2016.06.067_bib6) 2010; 11 Hu (10.1016/j.neucom.2016.06.067_bib18) 2015; 151 Hagemann (10.1016/j.neucom.2016.06.067_bib23) 2001; 112 Y.L (10.1016/j.neucom.2016.06.067_bib25) 2015; 188 Ng (10.1016/j.neucom.2016.06.067_bib10) 2011; 72 Qiu (10.1016/j.neucom.2016.06.067_bib22) 2013; 21 Lu (10.1016/j.neucom.2016.06.067_bib5) 2013 Cruz (10.1016/j.neucom.2016.06.067_bib1) 2015; 149 Nguyen (10.1016/j.neucom.2016.06.067_bib24) 2012; 97 10.1016/j.neucom.2016.06.067_bib13 Yang (10.1016/j.neucom.2016.06.067_bib7) 2015; 16 Albalawi (10.1016/j.neucom.2016.06.067_bib2) 2012 Wang (10.1016/j.neucom.2016.06.067_bib19) 2016; 27 Kenemans (10.1016/j.neucom.2016.06.067_bib3) 1991; 28 He (10.1016/j.neucom.2016.06.067_bib8) 2004; 42 Qiu (10.1016/j.neucom.2016.06.067_bib21) 2015; 99 Ahirwal (10.1016/j.neucom.2016.06.067_bib26) 2014; 144 Makeig (10.1016/j.neucom.2016.06.067_bib4) 1996 Qiu (10.1016/j.neucom.2016.06.067_bib20) 2015; 352 |
| References_xml | – volume: 99 year: 2015 ident: bib21 article-title: Fuzzy-model-based reliable static output feedback h-infinity control of nonlinear hyperbolic PDE systems publication-title: IEEE Trans. Fuzzy Syst. – volume: 352 start-page: 189 year: 2015 end-page: 215 ident: bib20 article-title: New approach to delay-dependent H∞ control for continuous-time Markovian jump systems with time-varying delay and deficient transition descriptions publication-title: J. Frankl. I – year: 2008 ident: bib15 article-title: BCI Competition 2008–Graz Data Set B – volume: 72 start-page: 1 year: 2011 end-page: 19 ident: bib10 article-title: Sparse autoencoder publication-title: CS294A Lecture Notes – volume: 15 start-page: 473 year: 2007 end-page: 482 ident: bib14 article-title: Brain-computer communication: motivation, aim, and impact of exploring a virtual apartment publication-title: IEEE Trans. Neural Syst. Rehabil. Eng. – volume: 16 start-page: 486 year: 2015 end-page: 496 ident: bib7 article-title: Fast removal of ocular artifacts from electroencephalogram signals using spatial constraint independent component analysis based recursive least squares in brain-computer interface publication-title: Front. Inf. Technol. Electron. Eng. – start-page: 436 year: 2013 end-page: 440 ident: bib5 article-title: Speech enhancement based on deep denoising autoencoder publication-title: Interspeech – volume: 188 start-page: 50 year: 2015 end-page: 62 ident: bib25 article-title: Computational aesthetics of photos quality assessment based on improved artificial neural network combined with autoencoder technique publication-title: Neurocomputing. – volume: 112 start-page: 215 year: 2001 end-page: 231 ident: bib23 article-title: The effects of ocular artifacts on (lateralized) broadband power in the EEG publication-title: Clin. Neurophysiol. – start-page: 1 year: 2012 end-page: 4 ident: bib2 article-title: A study of kernel CSP-based motor imagery brain computer interface classification publication-title: Signal Process. Med. Biol. Symp. IEEE. – start-page: 145 year: 1996 end-page: 151 ident: bib4 article-title: Independent component analysis of electroencephalographic data publication-title: Adv. Neural. Inform. Process. Syst. – volume: 151 start-page: 1477 year: 2015 end-page: 1485 ident: bib16 article-title: Extracting features from phase space of EEG signals in brain–computer interfaces publication-title: Neurocomputing – volume: 21 start-page: 245 year: 2013 end-page: 261 ident: bib22 article-title: Static-output-feedback control of continuous-time T-S fuzzy affine systems via piecewise lyapunov functions publication-title: IEEE Trans. Fuzzy Syst. – volume: 11 start-page: 3371 year: 2010 end-page: 3408 ident: bib6 article-title: Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion publication-title: J. Mach. Learn. Res. – volume: 149 start-page: 93 year: 2015 end-page: 99 ident: bib1 article-title: Adaptive time-window length based on online performance measurement in SSVEP-based BCIs publication-title: Neurocomputing. – volume: 137 start-page: 157 year: 2014 end-page: 164 ident: bib9 article-title: A comparative study and improvement of two ICA using reference signal methods publication-title: Neurocomputing – volume: 42 start-page: 407 year: 2004 end-page: 412 ident: bib8 article-title: Removal of ocular artifacts from electro-encephalogram by adaptive filtering publication-title: Med. Biol. Eng. Comput. – reference: B.H. Yang, L.F. He, Removal of ocular artifacts from EEG signals using ICA-RLS in BCI, in: IEEE Workshop on Electronics, Computer and Applications, 2014. IEEE, 2014, pp. 544–547. 〈http://dx.doi.org/10.1109/IWECA.2014.6845678〉. – volume: 133 start-page: 271 year: 2014 end-page: 279 ident: bib17 article-title: Epileptic seizure detection using DWT based fuzzy approximate entropy and support vector machine publication-title: Neurocomputing – volume: 97 start-page: 374 year: 2012 end-page: 389 ident: bib24 article-title: EOG artifact removal using a wavelet neural network publication-title: Neurocomputing – volume: 144 start-page: 282 year: 2014 end-page: 294 ident: bib26 article-title: Improved range selection method for evolutionary algorithm based adaptive filtering of EEG/ERP signals publication-title: Neurocomputing – volume: 28 start-page: 114 year: 1991 end-page: 121 ident: bib3 article-title: Removal of the ocular artifact from the EEG: a comparison of time and frequency domain methods with simulated and real data publication-title: Psychophysiology – volume: 27 start-page: 416 year: 2016 end-page: 425 ident: bib19 article-title: A combined adaptive neural network and nonlinear model predictive control for multirate networked industrial process control publication-title: IEEE Trans. Neural Netw. Learn. Syst. – volume: 174 start-page: 60 year: 2015 end-page: 71 ident: bib11 article-title: Building feature space of extreme learning machine with sparse denoising stacked-autoencoder publication-title: Neurocomputing – volume: 168 start-page: 454 year: 2015 end-page: 463 ident: bib12 article-title: Deep learning driven blockwise moving object detection with binary scene modeling publication-title: Neurocomputing – volume: 151 start-page: 278 year: 2015 end-page: 287 ident: bib18 article-title: Removal of EOG and EMG artifacts from EEG using combination of functional link neural network and adaptive neural fuzzy inference system publication-title: Neurocomputing – volume: 27 start-page: 416 year: 2016 ident: 10.1016/j.neucom.2016.06.067_bib19 article-title: A combined adaptive neural network and nonlinear model predictive control for multirate networked industrial process control publication-title: IEEE Trans. Neural Netw. Learn. Syst. doi: 10.1109/TNNLS.2015.2411671 – start-page: 145 year: 1996 ident: 10.1016/j.neucom.2016.06.067_bib4 article-title: Independent component analysis of electroencephalographic data publication-title: Adv. Neural. Inform. Process. Syst. – volume: 151 start-page: 1477 year: 2015 ident: 10.1016/j.neucom.2016.06.067_bib16 article-title: Extracting features from phase space of EEG signals in brain–computer interfaces publication-title: Neurocomputing doi: 10.1016/j.neucom.2014.10.038 – start-page: 1 year: 2012 ident: 10.1016/j.neucom.2016.06.067_bib2 article-title: A study of kernel CSP-based motor imagery brain computer interface classification publication-title: Signal Process. Med. Biol. Symp. IEEE. – volume: 174 start-page: 60 year: 2015 ident: 10.1016/j.neucom.2016.06.067_bib11 article-title: Building feature space of extreme learning machine with sparse denoising stacked-autoencoder publication-title: Neurocomputing – volume: 16 start-page: 486 issue: 6 year: 2015 ident: 10.1016/j.neucom.2016.06.067_bib7 article-title: Fast removal of ocular artifacts from electroencephalogram signals using spatial constraint independent component analysis based recursive least squares in brain-computer interface publication-title: Front. Inf. Technol. Electron. Eng. doi: 10.1631/FITEE.1400299 – volume: 28 start-page: 114 issue: 1 year: 1991 ident: 10.1016/j.neucom.2016.06.067_bib3 article-title: Removal of the ocular artifact from the EEG: a comparison of time and frequency domain methods with simulated and real data publication-title: Psychophysiology doi: 10.1111/j.1469-8986.1991.tb03397.x – volume: 144 start-page: 282 issue: 1 year: 2014 ident: 10.1016/j.neucom.2016.06.067_bib26 article-title: Improved range selection method for evolutionary algorithm based adaptive filtering of EEG/ERP signals publication-title: Neurocomputing doi: 10.1016/j.neucom.2014.05.029 – volume: 188 start-page: 50 year: 2015 ident: 10.1016/j.neucom.2016.06.067_bib25 article-title: Computational aesthetics of photos quality assessment based on improved artificial neural network combined with autoencoder technique publication-title: Neurocomputing. – volume: 168 start-page: 454 year: 2015 ident: 10.1016/j.neucom.2016.06.067_bib12 article-title: Deep learning driven blockwise moving object detection with binary scene modeling publication-title: Neurocomputing doi: 10.1016/j.neucom.2015.05.082 – year: 2008 ident: 10.1016/j.neucom.2016.06.067_bib15 – volume: 151 start-page: 278 year: 2015 ident: 10.1016/j.neucom.2016.06.067_bib18 article-title: Removal of EOG and EMG artifacts from EEG using combination of functional link neural network and adaptive neural fuzzy inference system publication-title: Neurocomputing doi: 10.1016/j.neucom.2014.09.040 – volume: 11 start-page: 3371 issue: 6 year: 2010 ident: 10.1016/j.neucom.2016.06.067_bib6 article-title: Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion publication-title: J. Mach. Learn. Res. – volume: 137 start-page: 157 year: 2014 ident: 10.1016/j.neucom.2016.06.067_bib9 article-title: A comparative study and improvement of two ICA using reference signal methods publication-title: Neurocomputing doi: 10.1016/j.neucom.2013.03.070 – volume: 21 start-page: 245 issue: 2 year: 2013 ident: 10.1016/j.neucom.2016.06.067_bib22 article-title: Static-output-feedback control of continuous-time T-S fuzzy affine systems via piecewise lyapunov functions publication-title: IEEE Trans. Fuzzy Syst. doi: 10.1109/TFUZZ.2012.2210555 – ident: 10.1016/j.neucom.2016.06.067_bib13 doi: 10.1109/IWECA.2014.6845678 – volume: 352 start-page: 189 issue: 1 year: 2015 ident: 10.1016/j.neucom.2016.06.067_bib20 article-title: New approach to delay-dependent H∞ control for continuous-time Markovian jump systems with time-varying delay and deficient transition descriptions publication-title: J. Frankl. I doi: 10.1016/j.jfranklin.2014.10.022 – start-page: 436 year: 2013 ident: 10.1016/j.neucom.2016.06.067_bib5 article-title: Speech enhancement based on deep denoising autoencoder publication-title: Interspeech doi: 10.21437/Interspeech.2013-130 – volume: 97 start-page: 374 issue: 1 year: 2012 ident: 10.1016/j.neucom.2016.06.067_bib24 article-title: EOG artifact removal using a wavelet neural network publication-title: Neurocomputing doi: 10.1016/j.neucom.2012.04.016 – volume: 15 start-page: 473 issue: 4 year: 2007 ident: 10.1016/j.neucom.2016.06.067_bib14 article-title: Brain-computer communication: motivation, aim, and impact of exploring a virtual apartment publication-title: IEEE Trans. Neural Syst. Rehabil. Eng. doi: 10.1109/TNSRE.2007.906956 – volume: 112 start-page: 215 year: 2001 ident: 10.1016/j.neucom.2016.06.067_bib23 article-title: The effects of ocular artifacts on (lateralized) broadband power in the EEG publication-title: Clin. Neurophysiol. doi: 10.1016/S1388-2457(00)00541-1 – volume: 42 start-page: 407 issue: 3 year: 2004 ident: 10.1016/j.neucom.2016.06.067_bib8 article-title: Removal of ocular artifacts from electro-encephalogram by adaptive filtering publication-title: Med. Biol. Eng. Comput. doi: 10.1007/BF02344717 – volume: 72 start-page: 1 year: 2011 ident: 10.1016/j.neucom.2016.06.067_bib10 article-title: Sparse autoencoder publication-title: CS294A Lecture Notes – volume: 99 year: 2015 ident: 10.1016/j.neucom.2016.06.067_bib21 article-title: Fuzzy-model-based reliable static output feedback h-infinity control of nonlinear hyperbolic PDE systems publication-title: IEEE Trans. Fuzzy Syst. – volume: 149 start-page: 93 year: 2015 ident: 10.1016/j.neucom.2016.06.067_bib1 article-title: Adaptive time-window length based on online performance measurement in SSVEP-based BCIs publication-title: Neurocomputing. doi: 10.1016/j.neucom.2014.01.062 – volume: 133 start-page: 271 year: 2014 ident: 10.1016/j.neucom.2016.06.067_bib17 article-title: Epileptic seizure detection using DWT based fuzzy approximate entropy and support vector machine publication-title: Neurocomputing doi: 10.1016/j.neucom.2013.11.009 |
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| Title | Removal of EOG artifacts from EEG using a cascade of sparse autoencoder and recursive least squares adaptive filter |
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