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Volume 20, Issue 1
  • ISSN: 1573-4056
  • E-ISSN: 1875-6603
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Abstract

Biomedical signal and image processing is the study of the dynamic behavior of various bio-signals, which benefits academics and research. Signal processing is used to assess the behavior of analogue and digital signals for the assessment, reconfiguration, improved efficiency, extraction of features, and reorganization of patterns. This paper unveils hidden characteristic information about input signals using feature extraction methods. The main feature extraction methods used in signal processing are based on studying time, frequency, and frequency domain. Feature exaction methods are used for data reduction, comparison, and reducing dimensions, producing the original signal with sufficient accuracy with a structure of an efficient and robust pattern for the classifier system. Therefore, an attempt has been made to study the various feature extraction methods, feature transformation methods, classifiers, and datasets for biomedical signals.

© 2024 The Author(s). Published by Bentham Science Publisher.
This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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2023-05-02
2024-11-23

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References

  1. MarchioniniG. Information seeking in electronic environmentsCambridge university press1997
     [Google Scholar]
  2. YilmazT. FosterR. HaoY. Detecting vital signs with wearable wireless sensors.Sensors (Basel)20101012108371086210.3390/s10121083722163501
     [Google Scholar]
  3. DeyN. AshourA.S. Sources localization and DOAE techniques of moving multiple sources.In: Direction of arrival estimation and localization of multi-speech sources.Springer2018233410.1007/978‑3‑319‑73059‑2_3
     [Google Scholar]
  4. DeyN. AshourA.S. Computing in medical image analysis.In: Soft computing based medical image analysis.Elsevier201831110.1016/B978‑0‑12‑813087‑2.00025‑7
     [Google Scholar]
  5. ElhayatmyG. DeyN. AshourA.S. Internet of things based wireless body area network in healthcare.In: Internet of things and big data analytics toward next-generation intelligence.Springer201832010.1007/978‑3‑319‑60435‑0_1
     [Google Scholar]
  6. KumarS. VeerK. KumarS. A spider tool-based qualitative analysis of machine learning for wrist pulse analysis.Netw. Model. Anal. Health Inform. Bioinform.20221111910.1007/s13721‑022‑00361‑734849327
     [Google Scholar]
  7. PoojaS.K.P. PahujaS.K. VeerK. Recent approaches on classification and feature extraction of eeg signal: a review.Robotica20224017710110.1017/S0263574721000382
     [Google Scholar]
  8. GhaderiF. Signal processing techniques for extracting signals with periodic structure: Applications to biomedical signals.Cardiff University2010
     [Google Scholar]
  9. OdinakaI. C. Identifying humans by the shape of their heartbeats and materials by their X-ray scattering profiles.McKelvey School of Engineering Theses ( Dissertations. 82014
     [Google Scholar]
  10. HaraldssonH. EdenbrandtL. OhlssonM. Detecting acute myocardial infarction in the 12-lead ECG using Hermite expansions and neural networks.Artif. Intell. Med.200432212713610.1016/j.artmed.2004.01.00315364096
     [Google Scholar]
  11. DeyN. AshourA.S. Direction of arrival estimation and localization of multi-speech sources.Springer2018xiv5310.1007/978‑3‑319‑73059‑2
     [Google Scholar]
  12. Jiminez GonzalezA. Antenatal foetal monitoring through abdominal phonogram recordings: A single-channel independent component approach.University of Southampton2010
     [Google Scholar]
  13. VeerK. A technique for classification and decomposition of muscle signal for control of myoelectric prostheses based on wavelet statistical classifier.Measurement20156028329110.1016/j.measurement.2014.10.023
     [Google Scholar]
  14. AthavaleY. R. Pattern classification of time-series signals using Fisher kernels and support vector machines.Thesis2010Ryerson University
     [Google Scholar]
  15. GuoC. HouZ. ZengZ. Advances in Neural Networks–ISNN 2013.Springer2013
     [Google Scholar]
  16. KamelM. CampilhoA. Image analysis and recognition6th International Conference, ICIAR 2009Halifax, CanadaJuly 6-8, 20095627Springer Science & Business Media2009
     [Google Scholar]
  17. WuY. Advances in computer, communication, control and automation.Springer201210.1007/978‑3‑642‑25541‑0
     [Google Scholar]
  18. HuangD-S. BevilacquaV. FigueroaJ.C. PremaratneP. Intelligent computing theories.9th International Conference, ICIC 2013Nanning, ChinaJuly 28-31, 20137995.Springer2013
     [Google Scholar]
  19. GevaA.B. Feature extraction and state identification in biomedical signals using hierarchical fuzzy clustering.Med. Biol. Eng. Comput.199836560861410.1007/BF0252443210367446
     [Google Scholar]
  20. GibsonS. JudyJ.W. MarkovićD. Technology-aware algorithm design for neural spike detection, feature extraction, and dimensionality reduction.IEEE Trans. Neural Syst. Rehabil. Eng.201018546947810.1109/TNSRE.2010.205168320525534
     [Google Scholar]
  21. JamesC.J. HesseC.W. Independent component analysis for biomedical signals.Physiol. Meas.2005261R15R3910.1088/0967‑3334/26/1/R0215742873
     [Google Scholar]
  22. LiD. PedryczW. PizziN.J. Fuzzy wavelet packet based feature extraction method and its application to biomedical signal classification.IEEE Trans. Biomed. Eng.20055261132113910.1109/TBME.2005.84837715977743
     [Google Scholar]
  23. PreeceS.J. GoulermasJ.Y. KenneyL.P.J. HowardD. A comparison of feature extraction methods for the classification of dynamic activities from accelerometer data.IEEE Trans. Biomed. Eng.200956387187910.1109/TBME.2008.200619019272902
     [Google Scholar]
  24. ElhajF.A. SalimN. HarrisA.R. SweeT.T. AhmedT. Arrhythmia recognition and classification using combined linear and nonlinear features of ECG signals.Comput. Methods Programs Biomed.2016127526310.1016/j.cmpb.2015.12.02427000289
     [Google Scholar]
  25. FriesenG.M. JannettT.C. JadallahM.A. YatesS.L. QuintS.R. NagleH.T. A comparison of the noise sensitivity of nine QRS detection algorithms.IEEE Trans. Biomed. Eng.1990371859810.1109/10.436202303275
     [Google Scholar]
  26. MeroneM. SodaP. SansoneM. SansoneC. ECG databases for biometric systems: A systematic review.Expert Syst. Appl.20176718920210.1016/j.eswa.2016.09.030
     [Google Scholar]
  27. Ghorbani AfkhamiR. AzarniaG. TinatiM.A. Cardiac arrhythmia classification using statistical and mixture modeling features of ECG signals.Pattern Recognit. Lett.201670455110.1016/j.patrec.2015.11.018
     [Google Scholar]
  28. InceT. KiranyazS. GabboujM. A generic and robust system for automated patient-specific classification of ECG signals.IEEE Trans. Biomed. Eng.20095651415142610.1109/TBME.2009.201393419203885
     [Google Scholar]
  29. DuttaS. ChatterjeeA. MunshiS. Identification of ECG beats from cross-spectrum information aided learning vector quantization.Measurement201144102020202710.1016/j.measurement.2011.08.014
     [Google Scholar]
  30. NunezP.L. SrinivasanR. Electric fields of the brain: the neurophysics of EEG.USAOxford University Press200610.1093/acprof:oso/9780195050387.001.0001
     [Google Scholar]
  31. BonnelJ. KhademiA. KrishnanS. IoanaC. Small bowel image classification using cross-co-occurrence matrices on wavelet domain.Biomed. Signal Process. Control20094171510.1016/j.bspc.2008.07.002
     [Google Scholar]
  32. Xiaoli Li KrishnanS. Ngok-Wah Ma A wavelet-PCA-based fingerprinting scheme for peer-to-peer video file sharing.IEEE Trans. Inf. Forensics Security20105336537310.1109/TIFS.2010.2051255
     [Google Scholar]
  33. ChenG. KrishnanS. Small bowel image classification using dual tree complex wavelet-based cross co-occurrence features and canonical discriminant analysis2015 International Conference on Advances in Computing, Communications and Informatics (ICACCI)20152174217910.1109/ICACCI.2015.7275938
     [Google Scholar]
  34. TurnipA. JunaidiE. Removal artifacts from EEG signal using independent component analysis and principal component analysis2014 2nd International Conference on Technology, Informatics, Management, Engineering & Environment2014296302201410.1109/TIME‑E.2014.7011635
     [Google Scholar]
  35. LuggerK. FlotzingerD. SchlöglA. PregenzerM. PfurtschellerG. Feature extraction for on-line EEG classification using principal components and linear discriminants.Med. Biol. Eng. Comput.199836330931410.1007/BF025224769747570
     [Google Scholar]
  36. MartisR.J. AcharyaU.R. MinL.C. ECG beat classification using PCA, LDA, ICA and Discrete Wavelet Transform.Biomed. Signal Process. Control20138543744810.1016/j.bspc.2013.01.005
     [Google Scholar]
  37. WangJ.S. ChiangW.C. HsuY.L. YangY.T.C. ECG arrhythmia classification using a probabilistic neural network with a feature reduction method.Neurocomputing2013116384510.1016/j.neucom.2011.10.045
     [Google Scholar]
  38. KuzilekJ. KremenV. SoucekF. LhotskaL. Independent component analysis and decision trees for ECG holter recording de-noising.PLoS One201496e9845010.1371/journal.pone.009845024905359
     [Google Scholar]
  39. AggarwalV. PatterhM.S. Quality controlled ECG compression using Discrete Cosine transform (DCT) and Laplacian Pyramid (LP)In: 2009 International Multimedia, Signal Processing and Communication Technologies20091210.1109/MSPCT.2009.5164162
     [Google Scholar]
  40. Abdul-LatifA.A. CosicI. KumarD.K. PolusB. Da CostaC. Power changes of EEG signals associated with muscle fatigue: the root mean square analysis of EEG bandsProceedings of the 2004 Intelligent Sensors, Sensor Networks and Information Processing Conference200453153410.1109/ISSNIP.2004.1417517
     [Google Scholar]
  41. VeerK. Wavelet transform to recognize muscular: Force relationship using SEMG signals.Proc. Natl. Acad. Sci., India, Sect. A Phys. Sci.201686110311210.1007/s40010‑015‑0245‑x
     [Google Scholar]
  42. SengthipphanyT. TretriluxanaS. ChitsakulK. Comparison of heart rate statistical parameters from photoplethysmographic signal in resting and exercise conditions.2015 12th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON)20151510.1109/ECTICon.2015.7207074
     [Google Scholar]
  43. StanticD. JoJ. Detecting abnormal ECG signals utilising wavelet transform and standard deviationIn: Proceedings of World Academy of Science, Engineering and Technology201271208
     [Google Scholar]
  44. HayashiH. FuruiA. KuritaY. TsujiT. A variance distribution model of surface EMG signals based on inverse gamma distribution.IEEE Trans. Biomed. Eng.201764112672268110.1109/TBME.2017.265712128129146
     [Google Scholar]
  45. PangB. Advanced EMD method using variance characterization for PPG with motion artifact.In: 2016 IEEE Biomedical Circuits and Systems Conference (BioCAS).201619619910.1109/BioCAS.2016.7833765
     [Google Scholar]
  46. WairagkarM. HayashiY. NasutoS. Movement intention detection from autocorrelation of EEG for BCIInternational conference on brain informatics and health201521222110.1007/978‑3‑319‑23344‑4_21
     [Google Scholar]
  47. ZoughiT. BoostaniR. Analyzing autocorrelation fluctuation of EEG signal for estimating depth of anesthesia2010 18th Iranian Conference on Electrical Engineering2010242910.1109/IRANIANCEE.2010.5507110
     [Google Scholar]
  48. KrishnanS. Adaptive signal processing techniques for analysis of knee joint vibroarthrographic signals.1999Thesis University of Calgary
     [Google Scholar]
  49. HosseinzadehD. KrishnanS. Gaussian mixture modeling of keystroke patterns for biometric applications.IEEE Trans. Syst. Man, Cybern. Part C200838681682610.1109/TSMCC.2008.2001696
     [Google Scholar]
  50. NallapareddyH. KrishnanS. KoliosM. Parametric analysis of ultrasound backscatter signals for monitoring cancer cell structural changes during cancer treatment.Can. Acoust.20073524754
     [Google Scholar]
  51. AthavaleY. KrishnanS. HosseinizadehP. GuergachiA. Identifying the potential for failure of businesses in the technology, pharmaceutical and banking sectors using kernel-based machine learning methods2009 IEEE International Conference on Systems, Man and Cybernetics20091073107710.1109/ICSMC.2009.5345982
     [Google Scholar]
  52. AsefiH. GhoraaniB. YeA. KrishnanS. Audio scene analysis using parametric signal features2011 24th Canadian Conference on Electrical and Computer Engineering (CCECE)201192292510.1109/CCECE.2011.6030593
     [Google Scholar]
  53. ShokrollahiM. KrishnanS. KumarD. ArjunanS. Chin EMG analysis for REM sleep behavior disordersIn: 2012 ISSNIP Biosignals and Biorobotics Conference: Biosignals and Robotics for Better and Safer Living (BRC)20121410.1109/BRC.2012.6222189
     [Google Scholar]
  54. TabatabaeiT.S. KrishnanS. GuergachiA. Emotion recognition using novel speech signal features2007 IEEE International Symposium on Circuits and Systems200734534810.1109/ISCAS.2007.378460
     [Google Scholar]
  55. ShokrollahiE. KrishnanS. NanthakumarK. Transfer function estimation of the right ventricle of canine heart.World Congress on Medical Physics and Biomedical EngineeringSeptember 7-12, 2009Munich, Germany20091588159110.1007/978‑3‑642‑03882‑2_421
     [Google Scholar]
  56. HosseinzadehD. KrishnanS. Combining vocal source and MFCC features for enhanced speaker recognition performance using GMMs2007 IEEE 9th Workshop on Multimedia Signal Processing200736536810.1109/MMSP.2007.4412892
     [Google Scholar]
  57. UmapathyK. GhoraaniB. KrishnanS. Audio signal processing using time-frequency approaches: coding, classification, fingerprinting, and watermarking.EURASIP J. Adv. Signal Process.20102010128
     [Google Scholar]
  58. UmapathyK. KrishnanS. A signal classification approach using time-width vs frequency band sub-energy distributionsIEEE Int Conf Acous, Speech, Sig Proce2005547710.1109/ICASSP.2005.1416344
     [Google Scholar]
  59. MirzaeiA. AyatollahiA. VavadiH. Statistical analysis of epileptic activities based on histogram and wavelet-spectral entropy.J. Biomed. Sci. Eng.20114320721310.4236/jbise.2011.43029
     [Google Scholar]
  60. AishwaryaR. PrabhuM. SumithraG. AnusiyaM. Feature extraction for EMG based prostheses control.ICTACT J. soft Comput.201332472477
     [Google Scholar]
  61. HalderB. MitraS. MitraM. Detection and identification of ECG waves by histogram approach2016 2nd International Conference on Control, Instrumentation, Energy & Communication (CIEC)201616817210.1109/CIEC.2016.7513749
     [Google Scholar]
  62. DuS. VuskovicM. Temporal vs. spectral approach to feature extraction from prehensile EMG signalsProceedings of the 2004 IEEE Int Conf Inform Reuse Integ2004344350
     [Google Scholar]
  63. VysataO. KukalJ. ProchazkaA. PazderaL. ValisM. Age-related changes in the energy and spectral composition of EEG.Neurophysiology2012441636710.1007/s11062‑012‑9268‑y
     [Google Scholar]
  64. AltayY.A. KremlevA.S. Analysis and systematization of noise arising by long-term recording of ECG signal2018 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus)20181053105710.1109/EIConRus.2018.8317271
     [Google Scholar]
  65. FrigoM. JohnsonS.G. FFTW: An adaptive software architecture for the FFTProceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP’98 (Cat. No. 98CH36181)199831381138410.1109/ICASSP.1998.681704
     [Google Scholar]
  66. LyonsR. LyonsR. dsp tips & tricks - the sliding DFT.IEEE Signal Process. Mag.2003202748010.1109/MSP.2003.1184347
     [Google Scholar]
  67. BahazM. BenzidR. Efficient algorithm for baseline wander and powerline noise removal from ECG signals based on discrete Fourier series.Australas. Phys. Eng. Sci. Med.201841114316010.1007/s13246‑018‑0623‑129404852
     [Google Scholar]
  68. BurgessA.P. Towards a unified understanding of event-related changes in the EEG: the firefly model of synchronization through cross-frequency phase modulation.PLoS One201279e4563010.1371/journal.pone.004563023049827
     [Google Scholar]
  69. DokurZ. ÖlmezT. YazganE. Comparison of discrete wavelet and Fourier transforms for ECG beat classification.Electron. Lett.1999351815021504
     [Google Scholar]
  70. RanjeetK. KumarA. PandeyR.K. ECG signal compression using different techniquesInternational Conference on Advances in Computing, Communication and Control2011231241
     [Google Scholar]
  71. do Vale MadeiroJ.P. CortezP.C. Monteiro FilhoJ.M.D.S. BraynerA.R.A. Developments and Applications for ECG Signal Processing: Modeling, Segmentation, and Pattern Recognition.Academic Press2018
     [Google Scholar]
  72. SeatsKevin J. LawrenceJesse F. PrietoGerman A. Improved ambient noise correlation functions using Welch′ s method.Geophys. J. Int.20121882513523
     [Google Scholar]
  73. FaustO. AcharyaR.U. AllenA.R. LinC.M. Analysis of EEG signals during epileptic and alcoholic states using AR modeling techniques.IRBM2008291445210.1016/j.rbmret.2007.11.003
     [Google Scholar]
  74. VeerK. Spectral and mathematical evaluation of electromyography signals for clinical use.Int. J. Biomath.201696165009410.1142/S1793524516500947
     [Google Scholar]
  75. HosseinzadehD. KrishnanS. On the use of complementary spectral features for speaker recognition.EURASIP J. Adv. Signal Process.20072008125818410.1155/2008/258184
     [Google Scholar]
  76. KarpagachelviS. ArthanariM. SivakumarM. ECG feature extraction techniques-a survey approach.arXiv Prepr.2010arXiv1005.0957
     [Google Scholar]
  77. KlingsporM. Hilbert transform: Mathematical theory and applications to signal processing.2015
     [Google Scholar]
  78. SahooJ.P. DasM.K. AriS. BeheraS. Autocorrelation and Hilbert transform-based QRS complex detection in ECG signal.International Journal of Signal and Imaging Systems Engineering201471525810.1504/IJSISE.2014.057939
     [Google Scholar]
  79. UmapathyK. KrishnanS. ParsaV. JamiesonD. Time-frequency modeling and classification of pathological voicesProceedings of the Second Joint 24th Annual Conference and the Annual Fall Meeting of the Biomedical Engineering Society][Engineering in Medicine and Biology2002111611710.1109/IEMBS.2002.1134413
     [Google Scholar]
  80. LearnedR.E. WillskyA.S. A wavelet packet approach to transient signal classification.Appl. Comput. Harmon. Anal.19952326527810.1006/acha.1995.1019
     [Google Scholar]
  81. FarooqT. GuergachiA. KrishnanS. Chaotic time series prediction using knowledge based Green’s kernel and least-squares support vector machines2007 IEEE International Conference on Systems, Man and Cybernetics200737337810.1109/ICSMC.2007.4414023
     [Google Scholar]
  82. SewellM. The Fisher kernel: A brief review.RN201111066
     [Google Scholar]
  83. TianY. HeL. LiZ. WuW. ZhangW-Q. LiuJ. Speaker verification using Fisher vectorThe 9th International Symposium on Chinese Spoken Language Processing201441942210.1109/ISCSLP.2014.6936620
     [Google Scholar]
  84. ThayilchiraS. KrishnanS. Detection of linear chirp and non-linear chirp interferences in a spread spectrum signal by using Hough-Radon transform2002 IEEE International Conference on Acoustics, Speech, and Signal Processing20024IV–4181
     [Google Scholar]
  85. SugavaneswaranL. UmapathyK. KrishnanS. Exploiting the ambiguity domain for non-stationary biomedical signal classification2010 Annual International Conference of the IEEE Engineering in Medicine and Biology20101934193710.1109/IEMBS.2010.5627723
     [Google Scholar]
  86. WanV. RenalsS. Evaluation of kernel methods for speaker verification and identification2002 IEEE International Conference on Acoustics, Speech, and Signal Processing20021I–669
     [Google Scholar]
  87. MacIsaacD. ParkerP.A. ScottR.N. The short-time Fourier transform and muscle fatigue assessment in dynamic contractions.J. Electromyogr. Kinesiol.200111643944910.1016/S1050‑6411(01)00021‑911738956
     [Google Scholar]
  88. YangJ. KrishnanS. Wavelet packets-based speech enhancement for hearing aids application.Can. Acoust.20053336667
     [Google Scholar]
  89. ErginS. UysalA.K. GunalE.S. GunalS. GulmezogluM.B. ECG based biometric authentication using ensemble of features2014 9th Iberian Conference on Information Systems and Technologies (CISTI)20141610.1109/CISTI.2014.6877089
     [Google Scholar]
  90. GunalS. EdizkanR. Use of novel feature extraction technique with subspace classifiers for speech recognitionIEEE International Conference on Pervasive Services2007808310.1109/PERSER.2007.4283894
     [Google Scholar]
  91. CaiS. YangS. ZhengF. LuM. WuY. KrishnanS. Knee joint vibration signal analysis with matching pursuit decomposition and dynamic weighted classifier fusion.Comput. Math. Methods Med20132013
     [Google Scholar]
  92. SubasiA. EEG signal classification using wavelet feature extraction and a mixture of expert model.Expert Syst. Appl.20073241084109310.1016/j.eswa.2006.02.005
     [Google Scholar]
  93. HazarikaN. ChenJ.Z. TsoiA.C. SergejewA. Classification of EEG signals using the wavelet transform.Signal Processing1997591617210.1016/S0165‑1684(97)00038‑8
     [Google Scholar]
  94. UmapathyK. KrishnanS. MasseS. HuX. DorianP. NanthakumarK. Optimizing cardiac resuscitation outcomes using wavelet analysis.In: 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.200967616764
     [Google Scholar]
  95. FoomanyF.H. Wavelet-based markers of ventricular fibrillation in optimizing human cardiac resuscitation2010 Annual International Conference of the IEEE Engineering in Medicine and Biology20102001200410.1109/IEMBS.2010.5627841
     [Google Scholar]
  96. AfatmirniE. NanthakumarK. MasseS. Predicting refibrillation from pre-shock waveforms in optimizing cardiac resuscitation.2011 Annu Int Conf IEEE Eng Med Biol Soc.2011251254
     [Google Scholar]
  97. SaikiaA. KakotyN.M. HazarikaS.M. Wavelet selection for EMG based grasp recognition through CWTInternational Conference on Advances in Computing and Communications201111912910.1007/978‑3‑642‑22714‑1_13
     [Google Scholar]
  98. SubasiA. AhmedA. AlickovicE. Effect of flash stimulation for migraine detection using decision tree classifiers.Procedia Comput. Sci.201814022322910.1016/j.procs.2018.10.332
     [Google Scholar]
  99. BermanA. Complete positivity.Linear Algebra and its Applications.198811075763
     [Google Scholar]
  100. MartisR.J. AcharyaU.R. MandanaK.M. RayA.K. ChakrabortyC. Cardiac decision making using higher order spectra.Biomed. Signal Process. Control20138219320310.1016/j.bspc.2012.08.004
     [Google Scholar]
  101. RajS. RayK.C. ECG signal analysis using DCT-based DOST and PSO optimized SVM.IEEE Trans. Instrum. Meas.201766347047810.1109/TIM.2016.2642758
     [Google Scholar]
  102. BaudetA. MorissetC. d’AthisP. MaillefertJ.F. CasillasJ.M. OrnettiP. LarocheD. Cross-talk correction method for knee kinematics in gait analysis using principal component analysis (PCA): a new proposal.PLoS One201497e10209810.1371/journal.pone.010209825003974
     [Google Scholar]
  103. SubasiA. Ismail GursoyM. EEG signal classification using PCA, ICA, LDA and support vector machines.Expert Syst. Appl.201037128659866610.1016/j.eswa.2010.06.065
     [Google Scholar]
  104. BarryR.J. De BlasioF.M. EEG frequency PCA in EEG-ERP dynamics.Psychophysiology2018555e1304210.1111/psyp.1304229226962
     [Google Scholar]
  105. BakirC. Classification of ECG signals with the dimension reduction methods.J. Math. Stat. Sci2007353363
     [Google Scholar]
  106. MartisR.J. AcharyaU.R. LimC.M. SuriJ.S. Characterization of ECG beats from cardiac arrhythmia using discrete cosine transform in PCA framework.Knowl. Base. Syst.201345768210.1016/j.knosys.2013.02.007
     [Google Scholar]
  107. CeylanR. ÖzbayY. KarlikB. A novel approach for classification of ECG arrhythmias: Type-2 fuzzy clustering neural network.Expert Syst. Appl.20093636721672610.1016/j.eswa.2008.08.028
     [Google Scholar]
  108. GandhiT. PanigrahiB.K. AnandS. A comparative study of wavelet families for EEG signal classification.Neurocomputing201174173051305710.1016/j.neucom.2011.04.029
     [Google Scholar]
  109. ShlensJ. A tutorial on independent component analysis.arXiv Prepr.2014arXiv1404.2986
     [Google Scholar]
  110. MartisR.J. AcharyaU.R. PrasadH. ChuaC.K. LimC.M. Automated detection of atrial fibrillation using Bayesian paradigm.Knowl. Base. Syst.20135426927510.1016/j.knosys.2013.09.016
     [Google Scholar]
  111. PoojaK.V. VeerK. PahujaS.K. Gender based assessment of gait rhythms during dual-task in Parkinson’s disease and its early detection.Biomed. Signal Process. Control20227210334610.1016/j.bspc.2021.103346
     [Google Scholar]
  112. WanV. RenalsS. Speaker verification using sequence discriminant support vector machines.IEEE Trans. Speech Audio Process.200513220321010.1109/TSA.2004.841042
     [Google Scholar]
  113. NieF. WangZ. WangR. WangZ. LiX. Towards robust discriminative projections learning via non-greedy -norm minmax.IEEE Trans. Pattern Anal. Mach. Intell.20214362086210010.1109/TPAMI.2019.296187731880539
     [Google Scholar]
  114. YeQ. HuangP. ZhangZ. ZhengY. FuL. YangW. Multiview learning with robust double-sided twin SVM.IEEE Trans. Cybern.2021
     [Google Scholar]
  115. YuY. FuL. ChengY. YeQ. Multi-view distance metric learning via independent and shared feature subspace with applications to face and forest fire recognition, and remote sensing classification.Knowl. Base. Syst.202224310835010.1016/j.knosys.2022.108350
     [Google Scholar]
  116. YanH. FuL. QiY. ChengL. YeQ. YuD.J. Learning a robust classifier for short-term traffic state prediction.Knowl. Base. Syst.202224210836810.1016/j.knosys.2022.108368
     [Google Scholar]
  117. FuL. LiZ. YeQ. Learning robust discriminant subspace based on joint L2, p-and L2, s-norm distance metrics.IEEE Trans. neural networks Learn. Syst.2022331130144
     [Google Scholar]
  118. SugiyamaM. Dimensionality Reduction of Multimodal Labeled Data by Local Fisher Discriminant Analysis.J. Mach. Learn. Res.200785102761
     [Google Scholar]
  119. LaiZ. XuY. YangJ. ShenL. ZhangD. Rotational invariant dimensionality reduction algorithms.IEEE Trans. Cybern.201747113733374610.1109/TCYB.2016.257864227390196
     [Google Scholar]
  120. YanC. ChangX. LuoM. ZhengQ. ZhangX. LiZ. NieF. Self-weighted robust LDA for multiclass classification with edge classes.ACM Trans. Intell. Syst. Technol.202112111910.1145/3418284
     [Google Scholar]
  121. WangJ. WangL. NieF. LiX. A novel formulation of trace ratio linear discriminant analysis.IEEE Trans. Neural Networks Learn. Syst.2022331055685578
     [Google Scholar]
  122. YeQ. LiZ. FuL. ZhangZ. YangW. YangG. Nonpeaked discriminant analysis for data representation.IEEE Trans. Neural Netw. Learn. Syst.201930123818383210.1109/TNNLS.2019.294486931725389
     [Google Scholar]
  123. ZhaoY. HanJ. ChenY. SunH. ChenJ. KeA. HanY. ZhangP. ZhangY. ZhouJ. WangC. Improving generalization based on l 1-norm regularization for EEG-based motor imagery classification.Front. Neurosci.20181227210.3389/fnins.2018.0027229867307
     [Google Scholar]
  124. DingL. L1-norm and L2-norm neuroimaging methods in reconstructing extended cortical sources from EEG.In: 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.200919221925
     [Google Scholar]
  125. RahimiA. XuJ. WangL. -Norm regularization in volumetric imaging of cardiac current sources.Comput. Math. Methods Med20132013
     [Google Scholar]
  126. GiarréL. ArgentiF. Mixed ℓ 2 and ℓ 1 -norm regularization for adaptive detrending with ARMA modeling.J. Franklin Inst.201835531493151110.1016/j.jfranklin.2017.12.009
     [Google Scholar]
  127. ZhangZ. DongJ. LuoX. ChoiK.S. WuX. Heartbeat classification using disease-specific feature selection.Comput. Biol. Med.201446798910.1016/j.compbiomed.2013.11.01924529208
     [Google Scholar]
  128. SpechtD.F. Probabilistic neural networks for classification, mapping, or associative memoryIEEE international conference on neural networks198812452553210.1109/ICNN.1988.23887
     [Google Scholar]
  129. ShawL. BaghaS. Online EMG signal analysis for diagnosis of neuromuscular diseases by using PCA and PNN.Int. J. Eng. Sci. Technol.201241044534459
     [Google Scholar]
  130. WuT. YangB. SunH. EEG classification based on artificial neural network in brain computer interface.In: Life system modeling and intelligent computing.Springer201015416210.1007/978‑3‑642‑15853‑7_19
     [Google Scholar]
  131. HsuW.Y. Fuzzy Hopfield neural network clustering for single-trial motor imagery EEG classification.Expert Syst. Appl.20123911055106110.1016/j.eswa.2011.07.106
     [Google Scholar]
  132. RichhariyaB. TanveerM. EEG signal classification using universum support vector machine.Expert Syst. Appl.201810616918210.1016/j.eswa.2018.03.053
     [Google Scholar]
  133. AlkanA. GünayM. Identification of EMG signals using discriminant analysis and SVM classifier.Expert Syst. Appl.2012391444710.1016/j.eswa.2011.06.043
     [Google Scholar]
  134. BablaniA. EdlaD.R. DodiaS. Classification of EEG data using k-nearest neighbor approach for concealed information test.Procedia Comput. Sci.201814324224910.1016/j.procs.2018.10.392
     [Google Scholar]
  135. VenkatesanC. KarthigaikumarP. VaratharajanR. A novel LMS algorithm for ECG signal preprocessing and KNN classifier based abnormality detection.Multimedia Tools Appl.2018778103651037410.1007/s11042‑018‑5762‑6
     [Google Scholar]
  136. SayadiO. ShamsollahiM.B. A model-based Bayesian framework for ECG beat segmentation.Physiol. Meas.200930333535210.1088/0967‑3334/30/3/00819242046
     [Google Scholar]
  137. GuttaS. ChengQ. Joint feature extraction and classifier design for ECG-based biometric recognition.IEEE J. Biomed. Health Inform.201620246046810.1109/JBHI.2015.240219925680220
     [Google Scholar]
  138. Derya ÜbeyliE. Recurrent neural networks employing Lyapunov exponents for analysis of ECG signals.Expert Syst. Appl.20103721192119910.1016/j.eswa.2009.06.022
     [Google Scholar]
  139. IbrahimyM.I. AhsanM.R. KhalifaO.O. Design and performance analysis of artificial neural network for hand motion detection from EMG signals.World Appl. Sci. J.2013236751758
     [Google Scholar]
  140. ChenY. ZhangS. Research on EEG classification with neural networks based on the levenberg-marquardt algorithmICICA201219520210.1007/978‑3‑642‑34041‑3_29
     [Google Scholar]
  141. TurnipA. HongK-S. GeS.S. Backpropagation neural networks training for single trial EEG classificationProceedings of the 29th Chinese Control Conference2010Beijing, China246267
     [Google Scholar]
  142. YadavD. YadavS. VeerK. A comprehensive assessment of brain computer interfaces: Recent trends and challenges.J. Neurosci. Methods202034610891810.1016/j.jneumeth.2020.10891832853592
     [Google Scholar]
  143. MarT. ZaunsederS. MartínezJ.P. LlamedoM. PollR. Optimization of ECG classification by means of feature selection.IEEE Trans. Biomed. Eng.20115882168217710.1109/TBME.2011.211339521317067
     [Google Scholar]
  144. LiH. YuanD. MaX. CuiD. CaoL. Genetic algorithm for the optimization of features and neural networks in ECG signals classification.Sci. Rep.2017714101110.1038/srep4101128139677
     [Google Scholar]
  145. ÖzbayY. CeylanR. KarlikB. Integration of type-2 fuzzy clustering and wavelet transform in a neural network based ECG classifier.Expert Syst. Appl.20113811004101010.1016/j.eswa.2010.07.118
     [Google Scholar]
  146. RajputK. VeerK. SEMG based recognition of hand motions for lower limb prostheses.Curr. Signal Transduct. Ther.2022171758110.2174/1574362416666210618113305
     [Google Scholar]
  147. MankarV.R. EMG signal noise removal using neural netwoks.In: Advances in Applied Electromyography.IntechOpen2011Preprint
     [Google Scholar]
  148. VeerK. A flexible approach for segregating physiological signals.Measurement201687212610.1016/j.measurement.2016.03.017
     [Google Scholar]
  149. HaseenaH.H. MathewA.T. PaulJ.K. Fuzzy clustered probabilistic and multi layered feed forward neural networks for electrocardiogram arrhythmia classification.J. Med. Syst.201135217918810.1007/s10916‑009‑9355‑920703571
     [Google Scholar]
  150. TantawiM.M. RevettK. SalemA. TolbaM.F. Fiducial feature reduction analysis for electrocardiogram (ECG) based biometric recognition.J. Intell. Inf. Syst.2013401173910.1007/s10844‑012‑0214‑7
     [Google Scholar]
  151. TantawiM.M. RevettK. SalemA.B. TolbaM.F. A wavelet feature extraction method for electrocardiogram (ECG)-based biometric recognition.Signal Image Video Process.2015961271128010.1007/s11760‑013‑0568‑5
     [Google Scholar]
  152. SeeraM. LimC.P. LiewW.S. LimE. LooC.K. Classification of electrocardiogram and auscultatory blood pressure signals using machine learning models.Expert Syst. Appl.20154273643365210.1016/j.eswa.2014.12.023
     [Google Scholar]
  153. De GaetanoA. PanunziS. RinaldiF. RisiA. SciandroneM. A patient adaptable ECG beat classifier based on neural networks.Appl. Math. Comput.2009213124324910.1016/j.amc.2009.03.013
     [Google Scholar]
  154. SpechtD.F. A general regression neural network.IEEE Trans. Neural Netw.19912656857610.1109/72.9793418282872
     [Google Scholar]
  155. LiP. WangY. HeJ. WangL. TianY. ZhouT.S. LiT. LiJ.S. High-performance personalized heartbeat classification model for long-term ECG signal.IEEE Trans. Biomed. Eng.2017641788610.1109/10.65035527046844
     [Google Scholar]
  156. SudalaimaniC. SivakumaranN. ElizabethT.T. RominusV.S. Automated detection of the preseizure state in EEG signal using neural networks.Biocybern. Biomed. Eng.201939116017510.1016/j.bbe.2018.11.007
     [Google Scholar]
  157. SubasiA. Classification of EMG signals using PSO optimized SVM for diagnosis of neuromuscular disorders.Comput. Biol. Med.201343557658610.1016/j.compbiomed.2013.01.02023453053
     [Google Scholar]
  158. ÖzbayY. TezelG. A new method for classification of ECG arrhythmias using neural network with adaptive activation function.Digit. Signal Process.20102041040104910.1016/j.dsp.2009.10.016
     [Google Scholar]
  159. CancelliereR. GemelloR. Efficient training of Time Delay Neural Networks for sequential patterns.Neurocomputing1996101334210.1016/0925‑2312(95)00044‑5
     [Google Scholar]
  160. NejadgholiI. MoradiM.H. AbdolaliF. Using phase space reconstruction for patient independent heartbeat classification in comparison with some benchmark methods.Comput. Biol. Med.201141641141910.1016/j.compbiomed.2011.04.00321536263
     [Google Scholar]
  161. Wei Jiang Seong KongG. Block-based neural networks for personalized ECG signal classification.IEEE Trans. Neural Netw.20071861750176110.1109/TNN.2007.90023918051190
     [Google Scholar]
  162. JewajindaY. ChongstitvatanaP. A parallel genetic algorithm for adaptive hardware and its application to ECG signal classification.Neural Comput. Appl.2013227-81609162610.1007/s00521‑012‑0963‑9
     [Google Scholar]
  163. KutluY. KuntalpD. A multi-stage automatic arrhythmia recognition and classification system.Comput. Biol. Med.2011411374510.1016/j.compbiomed.2010.11.00321183163
     [Google Scholar]
  164. YuS.N. ChouK.T. Selection of significant independent components for ECG beat classification.Expert Syst. Appl.20093622088209610.1016/j.eswa.2007.12.016
     [Google Scholar]
  165. EdlaD.R. AnsariM.F. ChaudharyN. DodiaS. Classification of facial expressions from eeg signals using wavelet packet transform and svm for wheelchair control operations.Procedia Comput. Sci.20181321467147610.1016/j.procs.2018.05.081
     [Google Scholar]
  166. Alonso-AtienzaF. MorgadoE. Fernández-MartínezL. García-AlberolaA. Rojo-ÁlvarezJ.L. Detection of life-threatening arrhythmias using feature selection and support vector machines.IEEE Trans. Biomed. Eng.201461383284010.1109/TBME.2013.229080024239968
     [Google Scholar]
  167. RahmanQ.A. TereshchenkoL.G. KongkatongM. AbrahamT. AbrahamM.R. ShatkayH. Utilizing ECG-based heartbeat classification for hypertrophic cardiomyopathy identification.IEEE Trans. Nanobiosci.201514550551210.1109/TNB.2015.242621325915962
     [Google Scholar]
  168. TrigoJ.D. AlesancoA. MartínezI. GarcíaJ. A review on digital ECG formats and the relationships between them.IEEE Trans. Inf. Technol. Biomed.201216343244410.1109/TITB.2011.217695522128009
     [Google Scholar]
  169. TavakoliM. BenussiC. Alhais LopesP. OsorioL.B. de AlmeidaA.T. Robust hand gesture recognition with a double channel surface EMG wearable armband and SVM classifier.Biomed. Signal Process. Control20184612113010.1016/j.bspc.2018.07.010
     [Google Scholar]
  170. LinC.W. WangJ.S. ChungP.C. Mining physiological conditions from heart rate variability analysis.IEEE Comput. Intell. Mag.201051505810.1109/MCI.2009.935309
     [Google Scholar]
  171. FaynJ. A classification tree approach for cardiac ischemia detection using spatiotemporal information from three standard ECG leads.IEEE Trans. Biomed. Eng.20115819510210.1109/TBME.2010.207187220813629
     [Google Scholar]
  172. SchetininV. JakaiteL. Classification of newborn EEG maturity with Bayesian averaging over decision trees.Expert Syst. Appl.201239109340934710.1016/j.eswa.2012.02.184
     [Google Scholar]
  173. AydemirO. KayikciogluT. Decision tree structure based classification of EEG signals recorded during two dimensional cursor movement imagery.J. Neurosci. Methods2014229687510.1016/j.jneumeth.2014.04.00724751647
     [Google Scholar]
  174. GokgozE. SubasiA. Comparison of decision tree algorithms for EMG signal classification using DWT.Biomed. Signal Process. Control20151813814410.1016/j.bspc.2014.12.005
     [Google Scholar]
  175. MartisR.J. AcharyaU.R. PrasadH. ChuaC.K. LimC.M. SuriJ.S. Application of higher order statistics for atrial arrhythmia classification.Biomed. Signal Process. Control20138688890010.1016/j.bspc.2013.08.008
     [Google Scholar]
  176. LiT. ZhouM. ECG classification using wavelet packet entropy and random forests.Entropy (Basel)201618828510.3390/e18080285
     [Google Scholar]
  177. MargauxP. EmmanuelM. SébastienD. OlivierB. JérémieM. Objective and subjective evaluation of online error correction during P300-based spellingAdv. Human-Computer Interact.2012201257825510.1155/2012/578295
     [Google Scholar]
  178. JovicA. BogunovicN. Electrocardiogram analysis using a combination of statistical, geometric, and nonlinear heart rate variability features.Artif. Intell. Med.201151317518610.1016/j.artmed.2010.09.00520980134
     [Google Scholar]
  179. AbawajyJ.H. KelarevA.V. ChowdhuryM. Multistage approach for clustering and classification of ECG data.Comput. Methods Programs Biomed.2013112372073010.1016/j.cmpb.2013.08.00224095570
     [Google Scholar]
  180. GoldbergerA.L. AmaralL.A.N. GlassL. HausdorffJ.M. IvanovP.C. MarkR.G. MietusJ.E. MoodyG.B. PengC.K. StanleyH.E. PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals.Circulation200010123E215E22010.1161/01.CIR.101.23.e21510851218
     [Google Scholar]
  181. SapsanisC. GeorgoulasG. TzesA. LymberopoulosD. Improving EMG based classification of basic hand movements using EMD2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)20135754575710.1109/EMBC.2013.6610858
     [Google Scholar]
  182. KhushabaR.N. KodagodaS. TakruriM. DissanayakeG. Toward improved control of prosthetic fingers using surface electromyogram (EMG) signals.Expert Syst. Appl.20123912107311073810.1016/j.eswa.2012.02.192
     [Google Scholar]
  183. KhushabaR.N. KodagodaS. Electromyogram (EMG) feature reduction using mutual components analysis for multifunction prosthetic fingers control2012 12th International Conference on Control Automation Robotics & Vision (ICARCV)20121534153910.1109/ICARCV.2012.6485374
     [Google Scholar]
  184. KhushabaR.N. KodagodaS. LiuD. DissanayakeG. Muscle computer interfaces for driver distraction reduction.Comput. Methods Programs Biomed.2013110213714910.1016/j.cmpb.2012.11.00223290462
     [Google Scholar]
  185. Al-TimemyA.H. KhushabaR.N. BugmannG. EscuderoJ. Improving the performance against force variation of EMG controlled multifunctional upper-limb prostheses for transradial amputees.IEEE Trans. Neural Syst. Rehabil. Eng.201624665066110.1109/TNSRE.2015.244563426111399
     [Google Scholar]
  186. KhushabaR.N. Al-TimemyA. KodagodaS. NazarpourK. Combined influence of forearm orientation and muscular contraction on EMG pattern recognition.Expert Syst. Appl.20166115416110.1016/j.eswa.2016.05.031
     [Google Scholar]
  187. NgeoJ.G. TameiT. ShibataT. Continuous and simultaneous estimation of finger kinematics using inputs from an EMG-to-muscle activation model.J. Neuroeng. Rehabil.201411112210.1186/1743‑0003‑11‑12225123024
     [Google Scholar]
  188. DuY. WenguangJ. WentaoW. GengW. CapgMyo: a high density surface electromyography database for gesture recognition.
     [Google Scholar]
  189. SchalkG. McFarlandD.J. HinterbergerT. BirbaumerN. WolpawJ.R. BCI2000: a general-purpose brain-computer interface (BCI) system.IEEE Trans. Biomed. Eng.20045161034104310.1109/TBME.2004.82707215188875
     [Google Scholar]
  190. KroupiE. VesinJ-M. EbrahimiT. Phase-amplitude coupling between eeg and eda while experiencing multimedia content2013 Humaine Association Conference on Affective Computing and Intelligent Interaction201386587010.1109/ACII.2013.162
     [Google Scholar]
  191. SykacekP. RobertsS.J. Adaptive classification by variational Kalman filtering.In: Advances in Neural Information Processing Systems2003753760
     [Google Scholar]
  192. AndrzejakR.G. LehnertzK. MormannF. RiekeC. DavidP. ElgerC.E. Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: Dependence on recording region and brain state.Phys. Rev. E Stat. Phys. Plasmas Fluids Relat. Interdiscip. Topics200164606190710.1103/PhysRevE.64.06190711736210
     [Google Scholar]
  193. DeanD.A.II GoldbergerA.L. MuellerR. KimM. RueschmanM. MobleyD. SahooS.S. JayapandianC.P. CuiL. MorricalM.G. SurovecS. ZhangG.Q. RedlineS. Scaling up scientific discovery in sleep medicine: the National Sleep Research Resource.Sleep20163951151116410.5665/sleep.577427070134
     [Google Scholar]
  194. ChoH. AhnM. AhnS. KwonM. JunS.C. EEG datasets for motor imagery brain–computer interface.Gigascience2017671810.1093/gigascience/gix03428472337
     [Google Scholar]
  195. LuciwM.D. JarockaE. EdinB.B. Multi-channel EEG recordings during 3,936 grasp and lift trials with varying weight and friction.Sci. Data20141114004710.1038/sdata.2014.4725977798
     [Google Scholar]
  196. KayaM. BinliM.K. OzbayE. YanarH. MishchenkoY. A large electroencephalographic motor imagery dataset for electroencephalographic brain computer interfaces.Sci. Data20185118021110.1038/sdata.2018.21130325349
     [Google Scholar]
  197. BlankertzB. MüllerK.R. CurioG. VaughanT.M. SchalkG. WolpawJ.R. SchlöglA. NeuperC. PfurtschellerG. HinterbergerT. SchröderM. BirbaumerN. The BCI competition 2003: progress and perspectives in detection and discrimination of EEG single trials.IEEE Trans. Biomed. Eng.20045161044105110.1109/TBME.2004.82669215188876
     [Google Scholar]
  198. BhattR.B. GopalM. FRCT: fuzzy-rough classification trees.Pattern Anal. Appl.2008111738810.1007/s10044‑007‑0080‑z
     [Google Scholar]
  199. SchirrmeisterR.T. SpringenbergJ.T. FiedererL.D.J. GlasstetterM. EggenspergerK. TangermannM. HutterF. BurgardW. BallT. Deep learning with convolutional neural networks for EEG decoding and visualization.Hum. Brain Mapp.201738115391542010.1002/hbm.2373028782865
     [Google Scholar]
  200. SaddiqueS.M. SiddiquiL.H. EEG based brain computer interface.J. Softw.20094655055410.4304/jsw.4.6.550‑554
     [Google Scholar]
  201. KoelstraS. MuhlC. SoleymaniM. Jong-Seok Lee YazdaniA. EbrahimiT. PunT. NijholtA. PatrasI. Deap: A database for emotion analysis; using physiological signals.IEEE Trans. Affect. Comput.201231183110.1109/T‑AFFC.2011.15
     [Google Scholar]
  202. YadavaM. KumarP. SainiR. RoyP.P. Prosad DograD. Analysis of EEG signals and its application to neuromarketing.Multimedia Tools Appl.20177618190871911110.1007/s11042‑017‑4580‑6
     [Google Scholar]
  203. DuanR-N. ZhuJ-Y. LuB-L. Differential entropy feature for EEG-based emotion classification2013 6th International IEEE/EMBS Conference on Neural Engineering (NER)2013818410.1109/NER.2013.6695876
     [Google Scholar]
  204. ZhengW.L. LiuW. LuY. LuB.L. CichockiA. Emotionmeter: A multimodal framework for recognizing human emotions.IEEE Trans. Cybern.20194931110112210.1109/TCYB.2018.279717629994384
     [Google Scholar]
  205. SoleymaniM. LichtenauerJ. PunT. PanticM. A multimodal database for affect recognition and implicit tagging.IEEE Trans. Affect. Comput.201231425510.1109/T‑AFFC.2011.25
     [Google Scholar]
  206. ChavarriagaR. MillanJ.R. Learning from EEG error-related potentials in noninvasive brain-computer interfaces.IEEE Trans. Neural Syst. Rehabil. Eng.201018438138810.1109/TNSRE.2010.205338720570777
     [Google Scholar]
  207. SpülerM. RosenstielW. BogdanM. Online adaptation of a c-VEP Brain-computer Interface(BCI) based on error-related potentials and unsupervised learning.PLoS One2012712e5107710.1371/journal.pone.005107723236433
     [Google Scholar]
  208. SpülerM. A high-speed brain-computer interface (BCI) using dry EEG electrodes.PLoS One2017122e017240010.1371/journal.pone.017240028225794
     [Google Scholar]
  209. FragaS.M.F. Aceves-FernandezM.A. Pedraza-OrtegaJ.C. Ramos-ArreguinJ.M. Screen Task Experiments for EEG Signals Based on SSVEP Brain Computer Interface.Int. J. Adv. Res. (Indore)2018621718173210.21474/IJAR01/6612
     [Google Scholar]
  210. TrujilloL.T. StanfieldC.T. VelaR.D. The effect of electroencephalogram (EEG) reference choice on information-theoretic measures of the complexity and integration of EEG signals.Front. Neurosci.20171142510.3389/fnins.2017.0042528790884
     [Google Scholar]
  211. ZhangX. YaoL. KanhereS.S. LiuY. GuT. ChenK. MindID.Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.20182312310.1145/3264959
     [Google Scholar]
  212. StoberS. SterninA. OwenA.M. GrahnJ.A. Towards music imagery information retrieval: Introducing the OpenMIIR dataset of EEG recordings from music perception and imagination.In: ISMIR2015763769
     [Google Scholar]
  213. SimolaJ. TorniainenJ. MoisalaM. KivikangasM. KrauseC.M. Eye movement related brain responses to emotional scenes during free viewing.Front. Syst. Neurosci.201374110.3389/fnsys.2013.0004123970856
     [Google Scholar]
  214. KanogaS. NakanishiM. MitsukuraY. Assessing the effects of voluntary and involuntary eyeblinks in independent components of electroencephalogram.Neurocomputing2016193203210.1016/j.neucom.2016.01.057
     [Google Scholar]
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Current Trends in Feature Extraction and Classification Methodologies of Biomedical Signals
Curr. Med. Imaging 20, E090323214502 (2024); https://doi.org/10.2174/1573405619666230309103435
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  • Article Type:     Review Article
Keyword(s): Biomedical Signals; Classifier; Datasets; Feature extraction method; Feature transformation; Image processing

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