[1]
A. Kapur, S. Kapur, P. Maes, AlterEgo: A Personalized Wearable Silent Speech Interface, in: 23rd Int. Conf. Intell. User Interfaces, ACM, 2018: p.43–53.
DOI: 10.1145/3172944.3172977
Google Scholar
[2]
A.N. Malik, J. Iqbal, M.I. Tiwana, Temporal based EEG Signals Classification for Talocrural and Knee Joint Movements using Emotive Head Set, J. Biomed. Eng. Med. Imaging. 2 (2016) 69.
DOI: 10.14738/jbemi.26.1730
Google Scholar
[3]
D. Marshall, D. Coyle, S. Wilson, M. Callaghan, Games, gameplay, and BCI: the state of the art, IEEE Trans. Comput. Intell. AI Games. 5 (2013) 82–99.
DOI: 10.1109/tciaig.2013.2263555
Google Scholar
[4]
M. Semprini, M. Laffranchi, V. Sanguineti, L. Avanzino, R. De Icco, L. De Michieli, M. Chiappalone, technological Approaches for Neurorehabilitation: From robotic Devices to Brain stimulation and Beyond, Front. Neurol. 9 (2018) 212.
DOI: 10.3389/fneur.2018.00212
Google Scholar
[5]
D. Nurseitov, A. Serekov, A. Shintemirov, B. Abibullaev, Design and evaluation of a P300-ERP based BCI system for real-time control of a mobile robot, in: Brain-Computer Interface (BCI), 2017 5th Int. Winter Conf., IEEE, 2017: p.115–120.
DOI: 10.1109/iww-bci.2017.7858177
Google Scholar
[6]
S. Saulynas, C. Lechner, R. Kuber, Towards the use of brain–computer interface and gestural technologies as a potential alternative to PIN authentication, Int. J. Human–Computer Interact. 34 (2018) 433–444.
DOI: 10.1080/10447318.2017.1357905
Google Scholar
[7]
X. Fan, L. Bi, T. Teng, H. Ding, Y. Liu, A brain–computer interface-based vehicle destination selection system using P300 and SSVEP signals, IEEE Trans. Intell. Transp. Syst. 16 (2015) 274–283.
DOI: 10.1109/tits.2014.2330000
Google Scholar
[8]
S. Barua, M.U. Ahmed, C. Ahlstrom, S. Begum, P. Funk, Automated EEG Artifact Handling with Application in Driver Monitoring, IEEE J. Biomed. Heal. Informatics. 22 (2017) 1350-1361.
DOI: 10.1109/jbhi.2017.2773999
Google Scholar
[9]
E. Maiorana, D. La Rocca, P. Campisi, On the permanence of EEG signals for biometric recognition, IEEE Trans. Inf. Forensics Secur. 11 (2016) 163–175.
DOI: 10.1109/tifs.2015.2481870
Google Scholar
[10]
D.J. McFarland, J.R. Wolpaw, EEG-Based Brain-Computer Interfaces, Curr. Opin. Biomed. Eng. 4 (2017) 194-200.
Google Scholar
[11]
V. Mihajlović, B. Grundlehner, R. Vullers, J. Penders, Wearable, wireless EEG solutions in daily life applications: what are we missing?, IEEE J. Biomed. Heal. Informatics. 19 (2015) 6–21.
DOI: 10.1109/jbhi.2014.2328317
Google Scholar
[12]
B. Somers, T. Francart, A. Bertrand, A generic EEG artifact removal algorithm based on the multi-channel Wiener filter, J. Neural Eng. 15 (2018) 036007.
DOI: 10.1088/1741-2552/aaac92
Google Scholar
[13]
X. Chen, A. Liu, Q. Chen, Y. Liu, L. Zou, M.J. McKeown, Simultaneous ocular and muscle artifact removal from EEG data by exploiting diverse statistics, Comput. Biol. Med. 88 (2017) 1–10.
DOI: 10.1016/j.compbiomed.2017.06.013
Google Scholar
[14]
J. Minguillon, M.A. Lopez-Gordo, F. Pelayo, Trends in EEG-BCI for daily-life: Requirements for artifact removal, Biomed. Signal Process. Control. 31 (2017) 407–418.
DOI: 10.1016/j.bspc.2016.09.005
Google Scholar
[15]
Y. Zhu, Z. Wang, C. Dai, D. Pi, Artifact Removal Methods in Motor Imagery of EEG, in: Int. Conf. Intell. Data Eng. Autom. Learn., Springer, 2017: p.287–294.
DOI: 10.1007/978-3-319-68935-7_32
Google Scholar
[16]
F. Ghaderi, S.K. Kim, E.A. Kirchner, Effects of eye artifact removal methods on single trial P300 detection, a comparative study, J. Neurosci. Methods. 221 (2014) 41–47.
DOI: 10.1016/j.jneumeth.2013.08.025
Google Scholar
[17]
M. Kim, S.-P. Kim, A comparsion of artifact rejection methods for a BCI using event related potentials, in: Brain-Computer Interface (BCI), 2018 6th Int. Conf., IEEE, 2018: p.1–4.
DOI: 10.1109/iww-bci.2018.8311530
Google Scholar
[18]
L. Frølich, I. Winkler, K.-R. Müller, W. Samek, Investigating effects of different artefact types on motor imagery BCI, in: Eng. Med. Biol. Soc. (EMBC), 2015 37th Annu. Int. Conf. IEEE, 2015: p.1942–(1945).
DOI: 10.1109/embc.2015.7318764
Google Scholar
[19]
C.S. Kim, J. Sun, D. Liu, Q. Wang, S.G. Paek, Removal of ocular artifacts using ICA and adaptive filter for motor imagery-based BCI, IEEE/CAA J. Autom. Sin. (2017) 1-8.
DOI: 10.1109/jas.2017.7510370
Google Scholar
[20]
M. Chaumon, D.V.M. Bishop, N.A. Busch, A practical guide to the selection of independent components of the electroencephalogram for artifact correction, J. Neurosci. Methods. 250 (2015) 47–63.
DOI: 10.1016/j.jneumeth.2015.02.025
Google Scholar
[21]
J.A. Urigüen, B. Garcia-Zapirain, EEG artifact removal—state-of-the-art and guidelines, J. Neural Eng. 12 (2015) 31001.
DOI: 10.1088/1741-2560/12/3/031001
Google Scholar
[22]
Y. Kopsinis, S. McLaughlin, Development of EMD-based denoising methods inspired by wavelet thresholding, IEEE Trans. Signal Process. 57 (2009) 1351–1362.
DOI: 10.1109/tsp.2009.2013885
Google Scholar
[23]
V. Krishnaveni, S. Jayaraman, L. Anitha, K. Ramadoss, Removal of ocular artifacts from EEG using adaptive thresholding of wavelet coefficients, J. Neural Eng. 3 (2006) 338.
DOI: 10.1088/1741-2560/3/4/011
Google Scholar
[24]
R.J. Croft, R.J. Barry, Removal of ocular artifact from the EEG: a review, Neurophysiol. Clin. Neurophysiol. 30 (2000) 5–19.
Google Scholar
[25]
K.T. Sweeney, T.E. Ward, S.F. McLoone, Artifact removal in physiological signals—Practices and possibilities, IEEE Trans. Inf. Technol. Biomed. 16 (2012) 488–500.
DOI: 10.1109/titb.2012.2188536
Google Scholar
[26]
H.-A.T. Nguyen, J. Musson, F. Li, W. Wang, G. Zhang, R. Xu, C. Richey, T. Schnell, F.D. McKenzie, J. Li, EOG artifact removal using a wavelet neural network, Neurocomputing. 97 (2012) 374–389.
DOI: 10.1016/j.neucom.2012.04.016
Google Scholar
[27]
T.T.H. Pham, R.J. Croft, P.J. Cadusch, R.J. Barry, A test of four EOG correction methods using an improved validation technique, Int. J. Psychophysiol. 79 (2011) 203–210.
DOI: 10.1016/j.ijpsycho.2010.10.008
Google Scholar
[28]
A.K. Abdullah, Z.C. Zhu, L. Siyao, S.M. Hussein, Blind source separation techniques based eye blinks rejection in EEG signals, Inf. Technol. J. 13 (2014) 401–413.
DOI: 10.3923/itj.2014.401.413
Google Scholar
[29]
Z. Zhang, H. Li, D. Mandic, Blind source separation and artefact cancellation for single channel bioelectrical signal, in: Wearable Implant. Body Sens. Networks (BSN), 2016 IEEE 13th Int. Conf., IEEE, 2016: p.177–182.
DOI: 10.1109/bsn.2016.7516255
Google Scholar
[30]
H. Ghandeharion, A. Erfanian, A fully automatic ocular artifact suppression from EEG data using higher order statistics: Improved performance by wavelet analysis, Med. Eng. Phys. 32 (2010) 720–729.
DOI: 10.1016/j.medengphy.2010.04.010
Google Scholar
[31]
S. Hoffmann, M. Falkenstein, The correction of eye blink artefacts in the EEG: a comparison of two prominent methods, PLoS One. 3 (2008) e3004.
DOI: 10.1371/journal.pone.0003004
Google Scholar
[32]
M.A. Klados, C. Papadelis, C. Braun, P.D. Bamidis, REG-ICA: a hybrid methodology combining blind source separation and regression techniques for the rejection of ocular artifacts, Biomed. Signal Process. Control. 6 (2011) 291–300.
DOI: 10.1016/j.bspc.2011.02.001
Google Scholar
[33]
T.-P. Jung, S. Makeig, C. Humphries, T.-W. Lee, M.J. Mckeown, V. Iragui, T.J. Sejnowski, Removing electroencephalographic artifacts by blind source separation, Psychophysiology. 37 (2000) 163–178.
DOI: 10.1111/1469-8986.3720163
Google Scholar
[34]
W. Kong, Z. Zhou, S. Hu, J. Zhang, F. Babiloni, G. Dai, Automatic and direct identification of blink components from scalp EEG, Sensors. 13 (2013) 10783–10801.
DOI: 10.3390/s130810783
Google Scholar
[35]
A. Mognon, J. Jovicich, L. Bruzzone, M. Buiatti, ADJUST: An automatic EEG artifact detector based on the joint use of spatial and temporal features, Psychophysiology. 48 (2011) 229–240.
DOI: 10.1111/j.1469-8986.2010.01061.x
Google Scholar
[36]
H. Nolan, R. Whelan, R.B. Reilly, FASTER: fully automated statistical thresholding for EEG artifact rejection, J. Neurosci. Methods. 192 (2010) 152–162.
DOI: 10.1016/j.jneumeth.2010.07.015
Google Scholar
[37]
M.T. Akhtar, W. Mitsuhashi, C.J. James, Employing spatially constrained ICA and wavelet denoising, for automatic removal of artifacts from multichannel EEG data, Signal Processing. 92 (2012) 401–416.
DOI: 10.1016/j.sigpro.2011.08.005
Google Scholar
[38]
R.E. Kelly Jr, G.S. Alexopoulos, Z. Wang, F.M. Gunning, C.F. Murphy, S.S. Morimoto, D. Kanellopoulos, Z. Jia, K.O. Lim, M.J. Hoptman, Visual inspection of independent components: defining a procedure for artifact removal from fMRI data, J. Neurosci. Methods. 189 (2010) 233–245.
DOI: 10.1016/j.jneumeth.2010.03.028
Google Scholar
[39]
I. Daly, R. Scherer, M. Billinger, G. Müller-Putz, FORCe: Fully online and automated artifact removal for brain-computer interfacing, IEEE Trans. Neural Syst. Rehabil. Eng. 23 (2015) 725–736.
DOI: 10.1109/tnsre.2014.2346621
Google Scholar
[40]
J. Dammers, M. Schiek, F. Boers, C. Silex, M. Zvyagintsev, U. Pietrzyk, K. Mathiak, Integration of amplitude and phase statistics for complete artifact removal in independent components of neuromagnetic recordings, IEEE Trans. Biomed. Eng. 55 (2008) 2353–2362.
DOI: 10.1109/tbme.2008.926677
Google Scholar
[41]
L. Frølich, T.S. Andersen, M. Mørup, Classification of independent components of EEG into multiple artifact classes, Psychophysiology. 52 (2015) 32–45.
DOI: 10.1111/psyp.12290
Google Scholar
[42]
I.I. Goncharova, D.J. McFarland, T.M. Vaughan, J.R. Wolpaw, EMG contamination of EEG: spectral and topographical characteristics, Clin. Neurophysiol. 114 (2003) 1580–1593.
DOI: 10.1016/s1388-2457(03)00093-2
Google Scholar
[43]
J.-A. Jiang, C.-F. Chao, M.-J. Chiu, R.-G. Lee, C.-L. Tseng, R. Lin, An automatic analysis method for detecting and eliminating ECG artifacts in EEG, Comput. Biol. Med. 37 (2007) 1660–1671.
DOI: 10.1016/j.compbiomed.2007.03.007
Google Scholar
[44]
S. Romero, M.A. Mañanas, M.J. Barbanoj, A comparative study of automatic techniques for ocular artifact reduction in spontaneous EEG signals based on clinical target variables: a simulation case, Comput. Biol. Med. 38 (2008) 348–360.
DOI: 10.1016/j.compbiomed.2007.12.001
Google Scholar
[45]
O. Aydemir, S. Pourzare, T. Kayikcioglu, Classifying various EMG and EOG artifacts in EEG signals, Przegląd Elektrotechniczny. 88 (2012) 218–222.
Google Scholar
[46]
S.H. Oh, Y.R. Lee and, H.N. Kim, A novel EEG feature extraction method using Hjorth parameter, International Journal of Electronics and Electrical Engineering. 2 (2014) 106-110.
DOI: 10.12720/ijeee.2.2.106-110
Google Scholar
[47]
V. Lawhern, W.D. Hairston, K. McDowell, M. Westerfield, K. Robbins, Detection and classification of subject-generated artifacts in EEG signals using autoregressive models, J. Neurosci. Methods. 208 (2012) 181–189.
DOI: 10.1016/j.jneumeth.2012.05.017
Google Scholar
[48]
V. Lawhern, W.D. Hairston, K. Robbins, Optimal feature selection for artifact classification in EEG time series, in: Int. Conf. Augment. Cogn., Springer, 2013: p.326–334.
DOI: 10.1007/978-3-642-39454-6_34
Google Scholar
[49]
W.-Y. Hsu, Improving classification accuracy of motor imagery EEG using genetic feature selection, Clin. EEG Neurosci. 45 (2014) 163–168.
DOI: 10.1177/1550059413491559
Google Scholar
[50]
B. Nakisa, M.N. Rastgoo, D. Tjondronegoro, V. Chandran, Evolutionary computation algorithms for feature selection of EEG-based emotion recognition using mobile sensors, Expert Syst. Appl. 93 (2017) 143-155.
DOI: 10.1016/j.eswa.2017.09.062
Google Scholar
[51]
I. Rejer, Genetic algorithms for feature selection for brain computer interface, Int. J. Pattern Recognit. Artif. Intell. 29 (2015) 1559008.
DOI: 10.1142/s0218001415590089
Google Scholar
[52]
P. Bhuvaneswari, J.S. Kumar, Support vector machine technique for EEG signals, Int. J. Comput. Appl. 63 (2013) 1-5.
Google Scholar
[53]
W.-Y. Hsu, Assembling a multi-feature EEG classifier for left–right motor imagery data using wavelet-based fuzzy approximate entropy for improved accuracy, Int. J. Neural Syst. 25 (2015) 1550037.
DOI: 10.1142/s0129065715500379
Google Scholar
[54]
B.-G. Lee, B.-L. Lee, W.-Y. Chung, Mobile healthcare for automatic driving sleep-onset detection using wavelet-based EEG and respiration signals, Sensors. 14 (2014) 17915–17936.
DOI: 10.3390/s141017915
Google Scholar
[55]
W.-Y. Hsu, C.-H. Lin, H.-J. Hsu, P.-H. Chen, I.-R. Chen, Wavelet-based envelope features with automatic EOG artifact removal: Application to single-trial EEG data, Expert Syst. Appl. 39 (2012) 2743–2749.
DOI: 10.1016/j.eswa.2011.08.132
Google Scholar
[56]
I. Winkler, S. Haufe, M. Tangermann, Automatic classification of artifactual ICA-components for artifact removal in EEG signals, Behav. Brain Funct. 7 (2011) 30.
DOI: 10.1186/1744-9081-7-30
Google Scholar
[57]
T. Radüntz, J. Scouten, O. Hochmuth, B. Meffert, EEG artifact elimination by extraction of ICA-component features using image processing algorithms, J. Neurosci. Methods. 243 (2015) 84–93.
DOI: 10.1016/j.jneumeth.2015.01.030
Google Scholar
[58]
Y. Zou, V. Nathan, R. Jafari, Automatic identification of artifact-related independent components for artifact removal in EEG recordings, IEEE J. Biomed. Heal. Informatics. 20 (2016) 73–81.
DOI: 10.1109/jbhi.2014.2370646
Google Scholar