Login Register Cart 0
Generic placeholder image

Current Medical Imaging

Editor-in-Chief

ISSN (Print): 1573-4056
ISSN (Online): 1875-6603

Back
Journal Home About Journal Editorial Board Journal Insight Current Issue Volumes/Issues
Subscribe
Review Article

Current Trends in Feature Extraction and Classification Methodologies of Biomedical Signals

Author(s): Sachin Kumar, Karan Veer* and Sanjeev Kumar

Volume 20, 2024

Published on: 02 May, 2023

Article ID: e090323214502 Pages: 15

DOI: 10.2174/1573405619666230309103435

open_access

Open Access Journals Promotions 2
conference banner
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.

Keywords: Feature extraction method, Feature transformation, Classifier, Datasets, Biomedical Signals, Image processing.

[1]
Marchionini G. Information seeking in electronic environments. Cambridge university press 1997.
[2]
Yilmaz T, Foster R, Hao Y. Detecting vital signs with wearable wireless sensors. Sensors (Basel) 2010; 10(12): 10837-62.
[http://dx.doi.org/10.3390/s101210837] [PMID: 22163501]
[3]
Dey N, Ashour AS. Sources localization and DOAE techniques of moving multiple sources. In: Direction of arrival estimation and localization of multi-speech sources. Springer 2018; pp. 23-34.
[http://dx.doi.org/10.1007/978-3-319-73059-2_3]
[4]
Dey N, Ashour AS. Computing in medical image analysis. In: Soft computing based medical image analysis. Elsevier 2018; pp. 3-11.
[http://dx.doi.org/10.1016/B978-0-12-813087-2.00025-7]
[5]
Elhayatmy G, Dey N, Ashour AS. Internet of things based wireless body area network in healthcare. In: Internet of things and big data analytics toward next-generation intelligence. Springer 2018; pp. 3-20.
[http://dx.doi.org/10.1007/978-3-319-60435-0_1]
[6]
Kumar S, Veer K, Kumar S. A spider tool-based qualitative analysis of machine learning for wrist pulse analysis. Netw Model Anal Health Inform Bioinform 2022; 11(1): 19.
[http://dx.doi.org/10.1007/s13721-022-00361-7] [PMID: 34849327]
[7]
Pooja SKP, Pahuja SK, Veer K. Recent approaches on classification and feature extraction of eeg signal: a review. Robotica 2022; 40(1): 77-101.
[http://dx.doi.org/10.1017/S0263574721000382]
[8]
Ghaderi F. Signal processing techniques for extracting signals with periodic structure: Applications to biomedical signals. Cardiff University 2010.
[9]
Odinaka I C. Identifying humans by the shape of their heartbeats and materials by their X-ray scattering profiles. McKelvey School of Engineering Theses ( Dissertations 8 2014.
[10]
Haraldsson H, Edenbrandt L, Ohlsson M. Detecting acute myocardial infarction in the 12-lead ECG using Hermite expansions and neural networks. Artif Intell Med 2004; 32(2): 127-36.
[http://dx.doi.org/10.1016/j.artmed.2004.01.003] [PMID: 15364096]
[11]
Dey N, Ashour AS. Direction of arrival estimation and localization of multi-speech sources. Springer 2018; xiv: p. 53.
[http://dx.doi.org/10.1007/978-3-319-73059-2]
[12]
Jiminez Gonzalez A. Antenatal foetal monitoring through abdominal phonogram recordings: A single-channel independent component approach. University of Southampton 2010.
[13]
Veer K. A technique for classification and decomposition of muscle signal for control of myoelectric prostheses based on wavelet statistical classifier. Measurement 2015; 60: 283-91.
[http://dx.doi.org/10.1016/j.measurement.2014.10.023]
[14]
Athavale Y R. Pattern classification of time-series signals using Fisher kernels and support vector machines. Thesis 2010.
[15]
Guo C, Hou Z, Zeng Z. Advances in Neural Networks–ISNN 2013. Springer 2013.
[16]
Kamel M, Campilho A. Image analysis and recognition 6th International Conference, ICIAR 2009. Halifax, Canada. Springer Science & Business Media 2009.July 6-8, 2009; 5627
[17]
Wu Y. Advances in computer, communication, control and automation. Springer 2012.
[http://dx.doi.org/10.1007/978-3-642-25541-0]
[18]
Huang D-S, Bevilacqua V, Figueroa JC, Premaratne P. Intelligent computing theories. 9th International Conference, ICIC 2013. Nanning, China. Springer 2013.July 28-31, 2013; 7995.
[19]
Geva AB. Feature extraction and state identification in biomedical signals using hierarchical fuzzy clustering. Med Biol Eng Comput 1998; 36(5): 608-14.
[http://dx.doi.org/10.1007/BF02524432] [PMID: 10367446]
[20]
Gibson S, Judy JW, Marković D. Technology-aware algorithm design for neural spike detection, feature extraction, and dimensionality reduction. IEEE Trans Neural Syst Rehabil Eng 2010; 18(5): 469-78.
[http://dx.doi.org/10.1109/TNSRE.2010.2051683] [PMID: 20525534]
[21]
James CJ, Hesse CW. Independent component analysis for biomedical signals. Physiol Meas 2005; 26(1): R15-39.
[http://dx.doi.org/10.1088/0967-3334/26/1/R02] [PMID: 15742873]
[22]
Li D, Pedrycz W, Pizzi NJ. Fuzzy wavelet packet based feature extraction method and its application to biomedical signal classification. IEEE Trans Biomed Eng 2005; 52(6): 1132-9.
[http://dx.doi.org/10.1109/TBME.2005.848377] [PMID: 15977743]
[23]
Preece SJ, Goulermas JY, Kenney LPJ, Howard D. A comparison of feature extraction methods for the classification of dynamic activities from accelerometer data. IEEE Trans Biomed Eng 2009; 56(3): 871-9.
[http://dx.doi.org/10.1109/TBME.2008.2006190] [PMID: 19272902]
[24]
Elhaj FA, Salim N, Harris AR, Swee TT, Ahmed T. Arrhythmia recognition and classification using combined linear and nonlinear features of ECG signals. Comput Methods Programs Biomed 2016; 127: 52-63.
[http://dx.doi.org/10.1016/j.cmpb.2015.12.024] [PMID: 27000289]
[25]
Friesen GM, Jannett TC, Jadallah MA, Yates SL, Quint SR, Nagle HT. A comparison of the noise sensitivity of nine QRS detection algorithms. IEEE Trans Biomed Eng 1990; 37(1): 85-98.
[http://dx.doi.org/10.1109/10.43620] [PMID: 2303275]
[26]
Merone M, Soda P, Sansone M, Sansone C. ECG databases for biometric systems: A systematic review. Expert Syst Appl 2017; 67: 189-202.
[http://dx.doi.org/10.1016/j.eswa.2016.09.030]
[27]
Ghorbani Afkhami R, Azarnia G, Tinati MA. Cardiac arrhythmia classification using statistical and mixture modeling features of ECG signals. Pattern Recognit Lett 2016; 70: 45-51.
[http://dx.doi.org/10.1016/j.patrec.2015.11.018]
[28]
Ince T, Kiranyaz S, Gabbouj M. A generic and robust system for automated patient-specific classification of ECG signals. IEEE Trans Biomed Eng 2009; 56(5): 1415-26.
[http://dx.doi.org/10.1109/TBME.2009.2013934] [PMID: 19203885]
[29]
Dutta S, Chatterjee A, Munshi S. Identification of ECG beats from cross-spectrum information aided learning vector quantization. Measurement 2011; 44(10): 2020-7.
[http://dx.doi.org/10.1016/j.measurement.2011.08.014]
[30]
Nunez PL, Srinivasan R. Electric fields of the brain: the neurophysics of EEG. USA: Oxford University Press 2006.
[http://dx.doi.org/10.1093/acprof:oso/9780195050387.001.0001]
[31]
Bonnel J, Khademi A, Krishnan S, Ioana C. Small bowel image classification using cross-co-occurrence matrices on wavelet domain. Biomed Signal Process Control 2009; 4(1): 7-15.
[http://dx.doi.org/10.1016/j.bspc.2008.07.002]
[32]
Xiaoli Li , Krishnan S, Ngok-Wah Ma . A wavelet-PCA-based fingerprinting scheme for peer-to-peer video file sharing. IEEE Trans Inf Forensics Security 2010; 5(3): 365-73.
[http://dx.doi.org/10.1109/TIFS.2010.2051255]
[33]
Chen G, Krishnan S. Small bowel image classification using dual tree complex wavelet-based cross co-occurrence features and canonical discriminant analysis 2015 International Conference on Advances in Computing, Communications and Informatics (ICACCI). 2174-9.
[http://dx.doi.org/10.1109/ICACCI.2015.7275938]
[34]
Turnip A, Junaidi E. Removal artifacts from EEG signal using independent component analysis and principal component analysis 2014 2nd International Conference on Technology, Informatics, Management, Engineering & Environment. 296-302.
[http://dx.doi.org/10.1109/TIME-E.2014.7011635]
[35]
Lugger K, Flotzinger D, Schlögl A, Pregenzer M, Pfurtscheller G. Feature extraction for on-line EEG classification using principal components and linear discriminants. Med Biol Eng Comput 1998; 36(3): 309-14.
[http://dx.doi.org/10.1007/BF02522476] [PMID: 9747570]
[36]
Martis RJ, Acharya UR, Min LC. ECG beat classification using PCA, LDA, ICA and Discrete Wavelet Transform. Biomed Signal Process Control 2013; 8(5): 437-48.
[http://dx.doi.org/10.1016/j.bspc.2013.01.005]
[37]
Wang JS, Chiang WC, Hsu YL, Yang YTC. ECG arrhythmia classification using a probabilistic neural network with a feature reduction method. Neurocomputing 2013; 116: 38-45.
[http://dx.doi.org/10.1016/j.neucom.2011.10.045]
[38]
Kuzilek J, Kremen V, Soucek F, Lhotska L. Independent component analysis and decision trees for ECG holter recording de-noising. PLoS One 2014; 9(6): e98450.
[http://dx.doi.org/10.1371/journal.pone.0098450] [PMID: 24905359]
[39]
Aggarwal V, Patterh MS. Quality controlled ECG compression using Discrete Cosine transform (DCT) and Laplacian Pyramid (LP) In: 2009 International Multimedia, Signal Processing and Communication Technologies. 2009; p. 12.
[http://dx.doi.org/10.1109/MSPCT.2009.5164162]
[40]
Abdul-Latif AA, Cosic I, Kumar DK, Polus B, Da Costa C. Power changes of EEG signals associated with muscle fatigue: the root mean square analysis of EEG bands Proceedings of the 2004 Intelligent Sensors, Sensor Networks and Information Processing Conference. 531-4.
[http://dx.doi.org/10.1109/ISSNIP.2004.1417517]
[41]
Veer K. Wavelet transform to recognize muscular: Force relationship using SEMG signals. Proc Natl Acad Sci, India, Sect A Phys Sci 2016; 86(1): 103-12.
[http://dx.doi.org/10.1007/s40010-015-0245-x]
[42]
Sengthipphany T, Tretriluxana S, Chitsakul K. 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). 1-5.
[http://dx.doi.org/10.1109/ECTICon.2015.7207074]
[43]
Stantic D, Jo J. Detecting abnormal ECG signals utilising wavelet transform and standard deviation In: Proceedings of World Academy of Science, Engineering and Technology. 2012; 71: p. 208.
[44]
Hayashi H, Furui A, Kurita Y, Tsuji T. A variance distribution model of surface EMG signals based on inverse gamma distribution. IEEE Trans Biomed Eng 2017; 64(11): 2672-81.
[http://dx.doi.org/10.1109/TBME.2017.2657121] [PMID: 28129146]
[45]
Pang B, et al. Advanced EMD method using variance characterization for PPG with motion artifact. In: 2016 IEEE Biomedical Circuits and Systems Conference (BioCAS). 2016; pp. 196-9.
[http://dx.doi.org/10.1109/BioCAS.2016.7833765]
[46]
Wairagkar M, Hayashi Y, Nasuto S. Movement intention detection from autocorrelation of EEG for BCI International conference on brain informatics and health. 212-21.
[http://dx.doi.org/10.1007/978-3-319-23344-4_21]
[47]
Zoughi T, Boostani R. Analyzing autocorrelation fluctuation of EEG signal for estimating depth of anesthesia 2010 18th Iranian Conference on Electrical Engineering. 24-9.
[http://dx.doi.org/10.1109/IRANIANCEE.2010.5507110]
[48]
Krishnan S. Adaptive signal processing techniques for analysis of knee joint vibroarthrographic signals. Thesis University of Calgary 1999.
[49]
Hosseinzadeh D, Krishnan S. Gaussian mixture modeling of keystroke patterns for biometric applications. IEEE Trans Syst Man, Cybern Part C 2008; 38(6): 816-26.
[http://dx.doi.org/10.1109/TSMCC.2008.2001696]
[50]
Nallapareddy H, Krishnan S, Kolios M. Parametric analysis of ultrasound backscatter signals for monitoring cancer cell structural changes during cancer treatment. Can Acoust 2007; 35(2): 47-54.
[51]
Athavale Y, Krishnan S, Hosseinizadeh P, Guergachi A. Identifying the potential for failure of businesses in the technology, pharmaceutical and banking sectors using kernel-based machine learning methods 2009 IEEE International Conference on Systems, Man and Cybernetics. 1073-7.
[http://dx.doi.org/10.1109/ICSMC.2009.5345982]
[52]
Asefi H, Ghoraani B, Ye A, Krishnan S. Audio scene analysis using parametric signal features 2011 24th Canadian Conference on Electrical and Computer Engineering (CCECE). 922-5.
[http://dx.doi.org/10.1109/CCECE.2011.6030593]
[53]
Shokrollahi M, Krishnan S, Kumar D, Arjunan S. Chin EMG analysis for REM sleep behavior disorders In: 2012 ISSNIP Biosignals and Biorobotics Conference: Biosignals and Robotics for Better and Safer Living (BRC). 2012; pp. 1-4.
[http://dx.doi.org/10.1109/BRC.2012.6222189]
[54]
Tabatabaei TS, Krishnan S, Guergachi A. Emotion recognition using novel speech signal features 2007 IEEE International Symposium on Circuits and Systems. 345-8.
[http://dx.doi.org/10.1109/ISCAS.2007.378460]
[55]
Shokrollahi E, Krishnan S, Nanthakumar K. Transfer function estimation of the right ventricle of canine heart. World Congress on Medical Physics and Biomedical Engineering. September 7-12, 2009; Munich, Germany. 2009; 1588-91.
[http://dx.doi.org/10.1007/978-3-642-03882-2_421]
[56]
Hosseinzadeh D, Krishnan S. Combining vocal source and MFCC features for enhanced speaker recognition performance using GMMs 2007 IEEE 9th Workshop on Multimedia Signal Processing. 365-8.
[http://dx.doi.org/10.1109/MMSP.2007.4412892]
[57]
Umapathy K, Ghoraani B, Krishnan S. Audio signal processing using time-frequency approaches: coding, classification, fingerprinting, and watermarking. EURASIP J Adv Signal Process 2010; 2010: 1-28.
[58]
Umapathy K, Krishnan S. A signal classification approach using time-width vs frequency band sub-energy distributions IEEE Int Conf Acous, Speech, Sig Proce. 5: 477.
[http://dx.doi.org/10.1109/ICASSP.2005.1416344]
[59]
Mirzaei A, Ayatollahi A, Vavadi H. Statistical analysis of epileptic activities based on histogram and wavelet-spectral entropy. J Biomed Sci Eng 2011; 4(3): 207-13.
[http://dx.doi.org/10.4236/jbise.2011.43029]
[60]
Aishwarya R, Prabhu M, Sumithra G, Anusiya M. Feature extraction for EMG based prostheses control. ICTACT J soft Comput 2013; 3(2): 472-7.
[61]
Halder B, Mitra S, Mitra M. Detection and identification of ECG waves by histogram approach 2016 2nd International Conference on Control, Instrumentation, Energy & Communication (CIEC). 168-72.
[http://dx.doi.org/10.1109/CIEC.2016.7513749]
[62]
Du S, Vuskovic M. Temporal vs. spectral approach to feature extraction from prehensile EMG signals Proceedings of the 2004 IEEE Int Conf Inform Reuse Integ. 344-50.
[63]
Vysata O, Kukal J, Prochazka A, Pazdera L, Valis M. Age-related changes in the energy and spectral composition of EEG. Neurophysiology 2012; 44(1): 63-7.
[http://dx.doi.org/10.1007/s11062-012-9268-y]
[64]
Altay YA, Kremlev AS. Analysis and systematization of noise arising by long-term recording of ECG signal 2018 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus). 1053-7.
[http://dx.doi.org/10.1109/EIConRus.2018.8317271]
[65]
Frigo M, Johnson SG. FFTW: An adaptive software architecture for the FFT Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP’98 (Cat No 98CH36181). 3: 1381-4.
[http://dx.doi.org/10.1109/ICASSP.1998.681704]
[66]
Lyons R, Lyons R. dsp tips & tricks - the sliding DFT. IEEE Signal Process Mag 2003; 20(2): 74-80.
[http://dx.doi.org/10.1109/MSP.2003.1184347]
[67]
Bahaz M, Benzid R. Efficient algorithm for baseline wander and powerline noise removal from ECG signals based on discrete Fourier series. Australas Phys Eng Sci Med 2018; 41(1): 143-60.
[http://dx.doi.org/10.1007/s13246-018-0623-1] [PMID: 29404852]
[68]
Burgess AP. Towards a unified understanding of event-related changes in the EEG: the firefly model of synchronization through cross-frequency phase modulation. PLoS One 2012; 7(9): e45630.
[http://dx.doi.org/10.1371/journal.pone.0045630] [PMID: 23049827]
[69]
Dokur Z, Ölmez T, Yazgan E. Comparison of discrete wavelet and Fourier transforms for ECG beat classification. Electron Lett 1999; 35(18): 1502-4.
[70]
Ranjeet K, Kumar A, Pandey RK. ECG signal compression using different techniques International Conference on Advances in Computing, Communication and Control. 231-41.
[71]
do Vale Madeiro JP, Cortez PC, Monteiro Filho JMDS, Brayner ARA. Developments and Applications for ECG Signal Processing: Modeling, Segmentation, and Pattern Recognition. Academic Press 2018.
[72]
Seats Kevin J, Lawrence Jesse F, Prieto German A. Improved ambient noise correlation functions using Welch′ s method. Geophys J Int 2012; 188(2): 513-23.
[73]
Faust O, Acharya RU, Allen AR, Lin CM. Analysis of EEG signals during epileptic and alcoholic states using AR modeling techniques. IRBM 2008; 29(1): 44-52.
[http://dx.doi.org/10.1016/j.rbmret.2007.11.003]
[74]
Veer K. Spectral and mathematical evaluation of electromyography signals for clinical use. Int J Biomath 2016; 9(6): 1650094.
[http://dx.doi.org/10.1142/S1793524516500947]
[75]
Hosseinzadeh D, Krishnan S. On the use of complementary spectral features for speaker recognition. EURASIP J Adv Signal Process 2007; 2008(1): 258184.
[http://dx.doi.org/10.1155/2008/258184]
[76]
Karpagachelvi S, Arthanari M, Sivakumar M. ECG feature extraction techniques-a survey approach. arXiv Prepr 2010; arXiv1005.0957.
[77]
Klingspor M. Hilbert transform: Mathematical theory and applications to signal processing. 2015.
[78]
Sahoo JP, Das MK, Ari S, Behera S. Autocorrelation and Hilbert transform-based QRS complex detection in ECG signal. International Journal of Signal and Imaging Systems Engineering 2014; 7(1): 52-8.
[http://dx.doi.org/10.1504/IJSISE.2014.057939]
[79]
Umapathy K, Krishnan S, Parsa V, Jamieson D. Time-frequency modeling and classification of pathological voices Proceedings of the Second Joint 24th Annual Conference and the Annual Fall Meeting of the Biomedical Engineering Society][Engineering in Medicine and Biology. 1: 116-7.
[http://dx.doi.org/10.1109/IEMBS.2002.1134413]
[80]
Learned RE, Willsky AS. A wavelet packet approach to transient signal classification. Appl Comput Harmon Anal 1995; 2(3): 265-78.
[http://dx.doi.org/10.1006/acha.1995.1019]
[81]
Farooq T, Guergachi A, Krishnan S. Chaotic time series prediction using knowledge based Green’s kernel and least-squares support vector machines 2007 IEEE International Conference on Systems, Man and Cybernetics. 373-8.
[http://dx.doi.org/10.1109/ICSMC.2007.4414023]
[82]
Sewell M. The Fisher kernel: A brief review. RN 2011; 11(06): 6.
[83]
Tian Y, He L, Li Z, Wu W, Zhang W-Q, Liu J. Speaker verification using Fisher vector The 9th International Symposium on Chinese Spoken Language Processing. 419-22.
[http://dx.doi.org/10.1109/ISCSLP.2014.6936620]
[84]
Thayilchira S, Krishnan S. Detection of linear chirp and non-linear chirp interferences in a spread spectrum signal by using Hough-Radon transform 2002 IEEE International Conference on Acoustics, Speech, and Signal Processing. 4: IV–4181.
[85]
Sugavaneswaran L, Umapathy K, Krishnan S. Exploiting the ambiguity domain for non-stationary biomedical signal classification 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology. 1934-7.
[http://dx.doi.org/10.1109/IEMBS.2010.5627723]
[86]
Wan V, Renals S. Evaluation of kernel methods for speaker verification and identification 2002 IEEE International Conference on Acoustics, Speech, and Signal Processing. 1: I–669.
[87]
MacIsaac D, Parker PA, Scott RN. The short-time Fourier transform and muscle fatigue assessment in dynamic contractions. J Electromyogr Kinesiol 2001; 11(6): 439-49.
[http://dx.doi.org/10.1016/S1050-6411(01)00021-9] [PMID: 11738956]
[88]
Yang J, Krishnan S. Wavelet packets-based speech enhancement for hearing aids application. Can Acoust 2005; 33(3): 66-7.
[89]
Ergin S, Uysal AK, Gunal ES, Gunal S, Gulmezoglu MB. ECG based biometric authentication using ensemble of features 2014 9th Iberian Conference on Information Systems and Technologies (CISTI). 1-6.
[http://dx.doi.org/10.1109/CISTI.2014.6877089]
[90]
Gunal S, Edizkan R. Use of novel feature extraction technique with subspace classifiers for speech recognition IEEE International Conference on Pervasive Services. 80-3.
[http://dx.doi.org/10.1109/PERSER.2007.4283894]
[91]
Cai S, Yang S, Zheng F, Lu M, Wu Y, Krishnan S. Knee joint vibration signal analysis with matching pursuit decomposition and dynamic weighted classifier fusion. Comput Math Methods Med. 2013; 2013.
[92]
Subasi A. EEG signal classification using wavelet feature extraction and a mixture of expert model. Expert Syst Appl 2007; 32(4): 1084-93.
[http://dx.doi.org/10.1016/j.eswa.2006.02.005]
[93]
Hazarika N, Chen JZ, Tsoi AC, Sergejew A. Classification of EEG signals using the wavelet transform. Signal Processing 1997; 59(1): 61-72.
[http://dx.doi.org/10.1016/S0165-1684(97)00038-8]
[94]
Umapathy K, Krishnan S, Masse S, Hu X, Dorian P, Nanthakumar K. Optimizing cardiac resuscitation outcomes using wavelet analysis. In: 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 2009; pp. 6761-4.
[95]
Foomany FH, et al. Wavelet-based markers of ventricular fibrillation in optimizing human cardiac resuscitation 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology. 2001-4.
[http://dx.doi.org/10.1109/IEMBS.2010.5627841]
[96]
Afatmirni E, Nanthakumar K, Masse S, et al. Predicting refibrillation from pre-shock waveforms in optimizing cardiac resuscitation. 2011 Annu Int Conf IEEE Eng Med Biol Soc. 251-4.
[97]
Saikia A, Kakoty NM, Hazarika SM. Wavelet selection for EMG based grasp recognition through CWT International Conference on Advances in Computing and Communications. 119-29.
[http://dx.doi.org/10.1007/978-3-642-22714-1_13]
[98]
Subasi A, Ahmed A, Alickovic E. Effect of flash stimulation for migraine detection using decision tree classifiers. Procedia Comput Sci 2018; 140: 223-9.
[http://dx.doi.org/10.1016/j.procs.2018.10.332]
[99]
Berman A. Complete positivity. Linear Algebra and its Applications 1988; 1(107): 57-63.
[100]
Martis RJ, Acharya UR, Mandana KM, Ray AK, Chakraborty C. Cardiac decision making using higher order spectra. Biomed Signal Process Control 2013; 8(2): 193-203.
[http://dx.doi.org/10.1016/j.bspc.2012.08.004]
[101]
Raj S, Ray KC. ECG signal analysis using DCT-based DOST and PSO optimized SVM. IEEE Trans Instrum Meas 2017; 66(3): 470-8.
[http://dx.doi.org/10.1109/TIM.2016.2642758]
[102]
Baudet A, Morisset C, d’Athis P, et al. Cross-talk correction method for knee kinematics in gait analysis using principal component analysis (PCA): a new proposal. PLoS One 2014; 9(7): e102098.
[http://dx.doi.org/10.1371/journal.pone.0102098] [PMID: 25003974]
[103]
Subasi A, Ismail Gursoy M. EEG signal classification using PCA, ICA, LDA and support vector machines. Expert Syst Appl 2010; 37(12): 8659-66.
[http://dx.doi.org/10.1016/j.eswa.2010.06.065]
[104]
Barry RJ, De Blasio FM. EEG frequency PCA in EEG-ERP dynamics. Psychophysiology 2018; 55(5): e13042.
[http://dx.doi.org/10.1111/psyp.13042] [PMID: 29226962]
[105]
Bakir C. Classification of ECG signals with the dimension reduction methods. J Math Stat Sci 2007; 353-63.
[106]
Martis RJ, Acharya UR, Lim CM, Suri JS. Characterization of ECG beats from cardiac arrhythmia using discrete cosine transform in PCA framework. Knowl Base Syst 2013; 45: 76-82.
[http://dx.doi.org/10.1016/j.knosys.2013.02.007]
[107]
Ceylan R, Özbay Y, Karlik B. A novel approach for classification of ECG arrhythmias: Type-2 fuzzy clustering neural network. Expert Syst Appl 2009; 36(3): 6721-6.
[http://dx.doi.org/10.1016/j.eswa.2008.08.028]
[108]
Gandhi T, Panigrahi BK, Anand S. A comparative study of wavelet families for EEG signal classification. Neurocomputing 2011; 74(17): 3051-7.
[http://dx.doi.org/10.1016/j.neucom.2011.04.029]
[109]
Shlens J. A tutorial on independent component analysis. arXiv Prepr 2014; arXiv1404.2986.
[110]
Martis RJ, Acharya UR, Prasad H, Chua CK, Lim CM. Automated detection of atrial fibrillation using Bayesian paradigm. Knowl Base Syst 2013; 54: 269-75.
[http://dx.doi.org/10.1016/j.knosys.2013.09.016]
[111]
Pooja KV, Veer K, Pahuja SK. Gender based assessment of gait rhythms during dual-task in Parkinson’s disease and its early detection. Biomed Signal Process Control 2022; 72: 103346.
[http://dx.doi.org/10.1016/j.bspc.2021.103346]
[112]
Wan V, Renals S. Speaker verification using sequence discriminant support vector machines. IEEE Trans Speech Audio Process 2005; 13(2): 203-10.
[http://dx.doi.org/10.1109/TSA.2004.841042]
[113]
Nie F, Wang Z, Wang R, Wang Z, Li X. Towards robust discriminative projections learning via non-greedy -norm minmax. IEEE Trans Pattern Anal Mach Intell 2021; 43(6): 2086-100.
[http://dx.doi.org/10.1109/TPAMI.2019.2961877] [PMID: 31880539]
[114]
Ye Q, Huang P, Zhang Z, Zheng Y, Fu L, Yang W. Multiview learning with robust double-sided twin SVM. IEEE Trans Cybern 2021.
[115]
Yu Y, Fu L, Cheng Y, Ye Q. 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 2022; 243: 108350.
[http://dx.doi.org/10.1016/j.knosys.2022.108350]
[116]
Yan H, Fu L, Qi Y, Cheng L, Ye Q, Yu DJ. Learning a robust classifier for short-term traffic state prediction. Knowl Base Syst 2022; 242: 108368.
[http://dx.doi.org/10.1016/j.knosys.2022.108368]
[117]
Fu L, Li Z, Ye Q, et al. Learning robust discriminant subspace based on joint L2, p-and L2, s-norm distance metrics. IEEE Trans neural networks Learn Syst 2022; 33(1): 130-44.
[118]
Sugiyama M. Dimensionality Reduction of Multimodal Labeled Data by Local Fisher Discriminant Analysis. J Mach Learn Res 2007; 8(5): 1027-61.
[119]
Lai Z, Xu Y, Yang J, Shen L, Zhang D. Rotational invariant dimensionality reduction algorithms. IEEE Trans Cybern 2017; 47(11): 3733-46.
[http://dx.doi.org/10.1109/TCYB.2016.2578642] [PMID: 27390196]
[120]
Yan C, Chang X, Luo M, et al. Self-weighted robust LDA for multiclass classification with edge classes. ACM Trans Intell Syst Technol 2021; 12(1): 1-19.
[http://dx.doi.org/10.1145/3418284]
[121]
Wang J, Wang L, Nie F, Li X. A novel formulation of trace ratio linear discriminant analysis. IEEE Trans Neural Networks Learn Syst 2022; 33(10): 5568-78.
[122]
Ye Q, Li Z, Fu L, Zhang Z, Yang W, Yang G. Nonpeaked discriminant analysis for data representation. IEEE Trans Neural Netw Learn Syst 2019; 30(12): 3818-32.
[http://dx.doi.org/10.1109/TNNLS.2019.2944869] [PMID: 31725389]
[123]
Zhao Y, Han J, Chen Y, et al. Improving generalization based on l1-norm regularization for EEG-based motor imagery classification. Front Neurosci 2018; 12: 272.
[http://dx.doi.org/10.3389/fnins.2018.00272] [PMID: 29867307]
[124]
Ding L. 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. 2009; pp. 1922-5.
[125]
Rahimi A, Xu J, Wang L. -Norm regularization in volumetric imaging of cardiac current sources. Comput Math Methods Med. 2013; 2013.
[126]
Giarré L, Argenti F. Mixed ℓ 2 and ℓ 1 -norm regularization for adaptive detrending with ARMA modeling. J Franklin Inst 2018; 355(3): 1493-511.
[http://dx.doi.org/10.1016/j.jfranklin.2017.12.009]
[127]
Zhang Z, Dong J, Luo X, Choi KS, Wu X. Heartbeat classification using disease-specific feature selection. Comput Biol Med 2014; 46: 79-89.
[http://dx.doi.org/10.1016/j.compbiomed.2013.11.019] [PMID: 24529208]
[128]
Specht DF. Probabilistic neural networks for classification, mapping, or associative memory IEEE international conference on neural networks 1988; 1(24): 525-32.
[http://dx.doi.org/10.1109/ICNN.1988.23887]
[129]
Shaw L, Bagha S. Online EMG signal analysis for diagnosis of neuromuscular diseases by using PCA and PNN. Int J Eng Sci Technol 2012; 4(10): 4453-9.
[130]
Wu T, Yang B, Sun H. EEG classification based on artificial neural network in brain computer interface. In: Life system modeling and intelligent computing. Springer 2010; pp. 154-62.
[http://dx.doi.org/10.1007/978-3-642-15853-7_19]
[131]
Hsu WY. Fuzzy Hopfield neural network clustering for single-trial motor imagery EEG classification. Expert Syst Appl 2012; 39(1): 1055-61.
[http://dx.doi.org/10.1016/j.eswa.2011.07.106]
[132]
Richhariya B, Tanveer M. EEG signal classification using universum support vector machine. Expert Syst Appl 2018; 106: 169-82.
[http://dx.doi.org/10.1016/j.eswa.2018.03.053]
[133]
Alkan A, Günay M. Identification of EMG signals using discriminant analysis and SVM classifier. Expert Syst Appl 2012; 39(1): 44-7.
[http://dx.doi.org/10.1016/j.eswa.2011.06.043]
[134]
Bablani A, Edla DR, Dodia S. Classification of EEG data using k-nearest neighbor approach for concealed information test. Procedia Comput Sci 2018; 143: 242-9.
[http://dx.doi.org/10.1016/j.procs.2018.10.392]
[135]
Venkatesan C, Karthigaikumar P, Varatharajan R. A novel LMS algorithm for ECG signal preprocessing and KNN classifier based abnormality detection. Multimedia Tools Appl 2018; 77(8): 10365-74.
[http://dx.doi.org/10.1007/s11042-018-5762-6]
[136]
Sayadi O, Shamsollahi MB. A model-based Bayesian framework for ECG beat segmentation. Physiol Meas 2009; 30(3): 335-52.
[http://dx.doi.org/10.1088/0967-3334/30/3/008] [PMID: 19242046]
[137]
Gutta S, Cheng Q. Joint feature extraction and classifier design for ECG-based biometric recognition. IEEE J Biomed Health Inform 2016; 20(2): 460-8.
[http://dx.doi.org/10.1109/JBHI.2015.2402199] [PMID: 25680220]
[138]
Derya Übeyli E. Recurrent neural networks employing Lyapunov exponents for analysis of ECG signals. Expert Syst Appl 2010; 37(2): 1192-9.
[http://dx.doi.org/10.1016/j.eswa.2009.06.022]
[139]
Ibrahimy MI, Ahsan MR, Khalifa OO. Design and performance analysis of artificial neural network for hand motion detection from EMG signals. World Appl Sci J 2013; 23(6): 751-8.
[140]
Chen Y, Zhang S. Research on EEG classification with neural networks based on the levenberg-marquardt algorithm ICICA. 195-202.
[http://dx.doi.org/10.1007/978-3-642-34041-3_29]
[141]
Turnip A, Hong K-S, Ge SS. Backpropagation neural networks training for single trial EEG classification Proceedings of the 29th Chinese Control Conference. Beijing, China. 2010; pp. 2462-67.
[142]
Yadav D, Yadav S, Veer K. A comprehensive assessment of brain computer interfaces: Recent trends and challenges. J Neurosci Methods 2020; 346: 108918.
[http://dx.doi.org/10.1016/j.jneumeth.2020.108918] [PMID: 32853592]
[143]
Mar T, Zaunseder S, Martínez JP, Llamedo M, Poll R. Optimization of ECG classification by means of feature selection. IEEE Trans Biomed Eng 2011; 58(8): 2168-77.
[http://dx.doi.org/10.1109/TBME.2011.2113395] [PMID: 21317067]
[144]
Li H, Yuan D, Ma X, Cui D, Cao L. Genetic algorithm for the optimization of features and neural networks in ECG signals classification. Sci Rep 2017; 7(1): 41011.
[http://dx.doi.org/10.1038/srep41011] [PMID: 28139677]
[145]
Özbay Y, Ceylan R, Karlik B. Integration of type-2 fuzzy clustering and wavelet transform in a neural network based ECG classifier. Expert Syst Appl 2011; 38(1): 1004-10.
[http://dx.doi.org/10.1016/j.eswa.2010.07.118]
[146]
Rajput K, Veer K. SEMG based recognition of hand motions for lower limb prostheses. Curr Signal Transduct Ther 2022; 17(1): 75-81.
[http://dx.doi.org/10.2174/1574362416666210618113305]
[147]
Mankar VR. EMG signal noise removal using neural netwoks. In: Advances in Applied Electromyography. IntechOpen 2011. Preprint
[148]
Veer K. A flexible approach for segregating physiological signals. Measurement 2016; 87: 21-6.
[http://dx.doi.org/10.1016/j.measurement.2016.03.017]
[149]
Haseena HH, Mathew AT, Paul JK. Fuzzy clustered probabilistic and multi layered feed forward neural networks for electrocardiogram arrhythmia classification. J Med Syst 2011; 35(2): 179-88.
[http://dx.doi.org/10.1007/s10916-009-9355-9] [PMID: 20703571]
[150]
Tantawi MM, Revett K, Salem A, Tolba MF. Fiducial feature reduction analysis for electrocardiogram (ECG) based biometric recognition. J Intell Inf Syst 2013; 40(1): 17-39.
[http://dx.doi.org/10.1007/s10844-012-0214-7]
[151]
Tantawi MM, Revett K, Salem AB, Tolba MF. A wavelet feature extraction method for electrocardiogram (ECG)-based biometric recognition. Signal Image Video Process 2015; 9(6): 1271-80.
[http://dx.doi.org/10.1007/s11760-013-0568-5]
[152]
Seera M, Lim CP, Liew WS, Lim E, Loo CK. Classification of electrocardiogram and auscultatory blood pressure signals using machine learning models. Expert Syst Appl 2015; 42(7): 3643-52.
[http://dx.doi.org/10.1016/j.eswa.2014.12.023]
[153]
De Gaetano A, Panunzi S, Rinaldi F, Risi A, Sciandrone M. A patient adaptable ECG beat classifier based on neural networks. Appl Math Comput 2009; 213(1): 243-9.
[http://dx.doi.org/10.1016/j.amc.2009.03.013]
[154]
Specht DF. A general regression neural network. IEEE Trans Neural Netw 1991; 2(6): 568-76.
[http://dx.doi.org/10.1109/72.97934] [PMID: 18282872]
[155]
Li P, Wang Y, He J, et al. High-performance personalized heartbeat classification model for long-term ECG signal. IEEE Trans Biomed Eng 2017; 64(1): 78-86.
[http://dx.doi.org/10.1109/10.650355] [PMID: 27046844]
[156]
Sudalaimani C, Sivakumaran N, Elizabeth TT, Rominus VS. Automated detection of the preseizure state in EEG signal using neural networks. Biocybern Biomed Eng 2019; 39(1): 160-75.
[http://dx.doi.org/10.1016/j.bbe.2018.11.007]
[157]
Subasi A. Classification of EMG signals using PSO optimized SVM for diagnosis of neuromuscular disorders. Comput Biol Med 2013; 43(5): 576-86.
[http://dx.doi.org/10.1016/j.compbiomed.2013.01.020] [PMID: 23453053]
[158]
Özbay Y, Tezel G. A new method for classification of ECG arrhythmias using neural network with adaptive activation function. Digit Signal Process 2010; 20(4): 1040-9.
[http://dx.doi.org/10.1016/j.dsp.2009.10.016]
[159]
Cancelliere R, Gemello R. Efficient training of Time Delay Neural Networks for sequential patterns. Neurocomputing 1996; 10(1): 33-42.
[http://dx.doi.org/10.1016/0925-2312(95)00044-5]
[160]
Nejadgholi I, Moradi MH, Abdolali F. Using phase space reconstruction for patient independent heartbeat classification in comparison with some benchmark methods. Comput Biol Med 2011; 41(6): 411-9.
[http://dx.doi.org/10.1016/j.compbiomed.2011.04.003] [PMID: 21536263]
[161]
Wei Jiang , Seong Kong G. Block-based neural networks for personalized ECG signal classification. IEEE Trans Neural Netw 2007; 18(6): 1750-61.
[http://dx.doi.org/10.1109/TNN.2007.900239] [PMID: 18051190]
[162]
Jewajinda Y, Chongstitvatana P. A parallel genetic algorithm for adaptive hardware and its application to ECG signal classification. Neural Comput Appl 2013; 22(7-8): 1609-26.
[http://dx.doi.org/10.1007/s00521-012-0963-9]
[163]
Kutlu Y, Kuntalp D. A multi-stage automatic arrhythmia recognition and classification system. Comput Biol Med 2011; 41(1): 37-45.
[http://dx.doi.org/10.1016/j.compbiomed.2010.11.003] [PMID: 21183163]
[164]
Yu SN, Chou KT. Selection of significant independent components for ECG beat classification. Expert Syst Appl 2009; 36(2): 2088-96.
[http://dx.doi.org/10.1016/j.eswa.2007.12.016]
[165]
Edla DR, Ansari MF, Chaudhary N, Dodia S. Classification of facial expressions from eeg signals using wavelet packet transform and svm for wheelchair control operations. Procedia Comput Sci 2018; 132: 1467-76.
[http://dx.doi.org/10.1016/j.procs.2018.05.081]
[166]
Alonso-Atienza F, Morgado E, Fernández-Martínez L, García-Alberola A, Rojo-Álvarez JL. Detection of life-threatening arrhythmias using feature selection and support vector machines. IEEE Trans Biomed Eng 2014; 61(3): 832-40.
[http://dx.doi.org/10.1109/TBME.2013.2290800] [PMID: 24239968]
[167]
Rahman QA, Tereshchenko LG, Kongkatong M, Abraham T, Abraham MR, Shatkay H. Utilizing ECG-based heartbeat classification for hypertrophic cardiomyopathy identification. IEEE Trans Nanobiosci 2015; 14(5): 505-12.
[http://dx.doi.org/10.1109/TNB.2015.2426213] [PMID: 25915962]
[168]
Trigo JD, Alesanco A, Martínez I, García J. A review on digital ECG formats and the relationships between them. IEEE Trans Inf Technol Biomed 2012; 16(3): 432-44.
[http://dx.doi.org/10.1109/TITB.2011.2176955] [PMID: 22128009]
[169]
Tavakoli M, Benussi C, Alhais Lopes P, Osorio LB, de Almeida AT. Robust hand gesture recognition with a double channel surface EMG wearable armband and SVM classifier. Biomed Signal Process Control 2018; 46: 121-30.
[http://dx.doi.org/10.1016/j.bspc.2018.07.010]
[170]
Lin CW, Wang JS, Chung PC. Mining physiological conditions from heart rate variability analysis. IEEE Comput Intell Mag 2010; 5(1): 50-8.
[http://dx.doi.org/10.1109/MCI.2009.935309]
[171]
Fayn J. A classification tree approach for cardiac ischemia detection using spatiotemporal information from three standard ECG leads. IEEE Trans Biomed Eng 2011; 58(1): 95-102.
[http://dx.doi.org/10.1109/TBME.2010.2071872] [PMID: 20813629]
[172]
Schetinin V, Jakaite L. Classification of newborn EEG maturity with Bayesian averaging over decision trees. Expert Syst Appl 2012; 39(10): 9340-7.
[http://dx.doi.org/10.1016/j.eswa.2012.02.184]
[173]
Aydemir O, Kayikcioglu T. Decision tree structure based classification of EEG signals recorded during two dimensional cursor movement imagery. J Neurosci Methods 2014; 229: 68-75.
[http://dx.doi.org/10.1016/j.jneumeth.2014.04.007] [PMID: 24751647]
[174]
Gokgoz E, Subasi A. Comparison of decision tree algorithms for EMG signal classification using DWT. Biomed Signal Process Control 2015; 18: 138-44.
[http://dx.doi.org/10.1016/j.bspc.2014.12.005]
[175]
Martis RJ, Acharya UR, Prasad H, Chua CK, Lim CM, Suri JS. Application of higher order statistics for atrial arrhythmia classification. Biomed Signal Process Control 2013; 8(6): 888-900.
[http://dx.doi.org/10.1016/j.bspc.2013.08.008]
[176]
Li T, Zhou M. ECG classification using wavelet packet entropy and random forests. Entropy (Basel) 2016; 18(8): 285.
[http://dx.doi.org/10.3390/e18080285]
[177]
Margaux P, Emmanuel M, Sébastien D, Olivier B, Jérémie M. Objective and subjective evaluation of online error correction during P300-based spelling Adv Human-Computer Interact 2012; 2012: 578255.
[http://dx.doi.org/10.1155/2012/578295]
[178]
Jovic A, Bogunovic N. Electrocardiogram analysis using a combination of statistical, geometric, and nonlinear heart rate variability features. Artif Intell Med 2011; 51(3): 175-86.
[http://dx.doi.org/10.1016/j.artmed.2010.09.005] [PMID: 20980134]
[179]
Abawajy JH, Kelarev AV, Chowdhury M. Multistage approach for clustering and classification of ECG data. Comput Methods Programs Biomed 2013; 112(3): 720-30.
[http://dx.doi.org/10.1016/j.cmpb.2013.08.002] [PMID: 24095570]
[180]
Goldberger AL, Amaral LAN, Glass L, et al. PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation 2000; 101(23): E215-20.
[http://dx.doi.org/10.1161/01.CIR.101.23.e215] [PMID: 10851218]
[181]
Sapsanis C, Georgoulas G, Tzes A, Lymberopoulos D. Improving EMG based classification of basic hand movements using EMD 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). 5754-7.
[http://dx.doi.org/10.1109/EMBC.2013.6610858]
[182]
Khushaba RN, Kodagoda S, Takruri M, Dissanayake G. Toward improved control of prosthetic fingers using surface electromyogram (EMG) signals. Expert Syst Appl 2012; 39(12): 10731-8.
[http://dx.doi.org/10.1016/j.eswa.2012.02.192]
[183]
Khushaba RN, Kodagoda S. Electromyogram (EMG) feature reduction using mutual components analysis for multifunction prosthetic fingers control 2012 12th International Conference on Control Automation Robotics & Vision (ICARCV). 1534-9.
[http://dx.doi.org/10.1109/ICARCV.2012.6485374]
[184]
Khushaba RN, Kodagoda S, Liu D, Dissanayake G. Muscle computer interfaces for driver distraction reduction. Comput Methods Programs Biomed 2013; 110(2): 137-49.
[http://dx.doi.org/10.1016/j.cmpb.2012.11.002] [PMID: 23290462]
[185]
Al-Timemy AH, Khushaba RN, Bugmann G, Escudero J. Improving the performance against force variation of EMG controlled multifunctional upper-limb prostheses for transradial amputees. IEEE Trans Neural Syst Rehabil Eng 2016; 24(6): 650-61.
[http://dx.doi.org/10.1109/TNSRE.2015.2445634] [PMID: 26111399]
[186]
Khushaba RN, Al-Timemy A, Kodagoda S, Nazarpour K. Combined influence of forearm orientation and muscular contraction on EMG pattern recognition. Expert Syst Appl 2016; 61: 154-61.
[http://dx.doi.org/10.1016/j.eswa.2016.05.031]
[187]
Ngeo JG, Tamei T, Shibata T. Continuous and simultaneous estimation of finger kinematics using inputs from an EMG-to-muscle activation model. J Neuroeng Rehabil 2014; 11(1): 122.
[http://dx.doi.org/10.1186/1743-0003-11-122] [PMID: 25123024]
[188]
Du Y, Wenguang J, Wentao W, Geng W. CapgMyo: a high density surface electromyography database for gesture recognition.
[189]
Schalk G, McFarland DJ, Hinterberger T, Birbaumer N, Wolpaw JR. BCI2000: a general-purpose brain-computer interface (BCI) system. IEEE Trans Biomed Eng 2004; 51(6): 1034-43.
[http://dx.doi.org/10.1109/TBME.2004.827072] [PMID: 15188875]
[190]
Kroupi E, Vesin J-M, Ebrahimi T. Phase-amplitude coupling between eeg and eda while experiencing multimedia content 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction. 865-70.
[http://dx.doi.org/10.1109/ACII.2013.162]
[191]
Sykacek P, Roberts SJ. Adaptive classification by variational Kalman filtering. In: Advances in Neural Information Processing Systems. 2003; pp. 753-60.
[192]
Andrzejak RG, Lehnertz K, Mormann F, Rieke C, David P, Elger CE. 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 Topics 2001; 64(6): 061907.
[http://dx.doi.org/10.1103/PhysRevE.64.061907] [PMID: 11736210]
[193]
Dean DA II, Goldberger AL, Mueller R, et al. Scaling up scientific discovery in sleep medicine: the National Sleep Research Resource. Sleep 2016; 39(5): 1151-64.
[http://dx.doi.org/10.5665/sleep.5774] [PMID: 27070134]
[194]
Cho H, Ahn M, Ahn S, Kwon M, Jun SC. EEG datasets for motor imagery brain–computer interface. Gigascience 2017; 6(7): 1-8.
[http://dx.doi.org/10.1093/gigascience/gix034] [PMID: 28472337]
[195]
Luciw MD, Jarocka E, Edin BB. Multi-channel EEG recordings during 3,936 grasp and lift trials with varying weight and friction. Sci Data 2014; 1(1): 140047.
[http://dx.doi.org/10.1038/sdata.2014.47] [PMID: 25977798]
[196]
Kaya M, Binli MK, Ozbay E, Yanar H, Mishchenko Y. A large electroencephalographic motor imagery dataset for electroencephalographic brain computer interfaces. Sci Data 2018; 5(1): 180211.
[http://dx.doi.org/10.1038/sdata.2018.211] [PMID: 30325349]
[197]
Blankertz B, Müller KR, Curio G, et al. The BCI competition 2003: progress and perspectives in detection and discrimination of EEG single trials. IEEE Trans Biomed Eng 2004; 51(6): 1044-51.
[http://dx.doi.org/10.1109/TBME.2004.826692] [PMID: 15188876]
[198]
Bhatt RB, Gopal M. FRCT: fuzzy-rough classification trees. Pattern Anal Appl 2008; 11(1): 73-88.
[http://dx.doi.org/10.1007/s10044-007-0080-z]
[199]
Schirrmeister RT, Springenberg JT, Fiederer LDJ, et al. Deep learning with convolutional neural networks for EEG decoding and visualization. Hum Brain Mapp 2017; 38(11): 5391-420.
[http://dx.doi.org/10.1002/hbm.23730] [PMID: 28782865]
[200]
Saddique SM, Siddiqui LH. EEG based brain computer interface. J Softw 2009; 4(6): 550-4.
[http://dx.doi.org/10.4304/jsw.4.6.550-554]
[201]
Koelstra S, Muhl C, Soleymani M, et al. Deap: A database for emotion analysis; using physiological signals. IEEE Trans Affect Comput 2012; 3(1): 18-31.
[http://dx.doi.org/10.1109/T-AFFC.2011.15]
[202]
Yadava M, Kumar P, Saini R, Roy PP, Prosad Dogra D. Analysis of EEG signals and its application to neuromarketing. Multimedia Tools Appl 2017; 76(18): 19087-111.
[http://dx.doi.org/10.1007/s11042-017-4580-6]
[203]
Duan R-N, Zhu J-Y, Lu B-L. Differential entropy feature for EEG-based emotion classification 2013 6th International IEEE/EMBS Conference on Neural Engineering (NER). 81-4.
[http://dx.doi.org/10.1109/NER.2013.6695876]
[204]
Zheng WL, Liu W, Lu Y, Lu BL, Cichocki A. Emotionmeter: A multimodal framework for recognizing human emotions. IEEE Trans Cybern 2019; 49(3): 1110-22.
[http://dx.doi.org/10.1109/TCYB.2018.2797176] [PMID: 29994384]
[205]
Soleymani M, Lichtenauer J, Pun T, Pantic M. A multimodal database for affect recognition and implicit tagging. IEEE Trans Affect Comput 2012; 3(1): 42-55.
[http://dx.doi.org/10.1109/T-AFFC.2011.25]
[206]
Chavarriaga R, Millan JR. Learning from EEG error-related potentials in noninvasive brain-computer interfaces. IEEE Trans Neural Syst Rehabil Eng 2010; 18(4): 381-8.
[http://dx.doi.org/10.1109/TNSRE.2010.2053387] [PMID: 20570777]
[207]
Spüler M, Rosenstiel W, Bogdan M. Online adaptation of a c-VEP Brain-computer Interface(BCI) based on error-related potentials and unsupervised learning. PLoS One 2012; 7(12): e51077.
[http://dx.doi.org/10.1371/journal.pone.0051077] [PMID: 23236433]
[208]
Spüler M. A high-speed brain-computer interface (BCI) using dry EEG electrodes. PLoS One 2017; 12(2): e0172400.
[http://dx.doi.org/10.1371/journal.pone.0172400] [PMID: 28225794]
[209]
Fraga SMF, Aceves-Fernandez MA, Pedraza-Ortega JC, Ramos-Arreguin JM. Screen Task Experiments for EEG Signals Based on SSVEP Brain Computer Interface. Int J Adv Res (Indore) 2018; 6(2): 1718-32.
[http://dx.doi.org/10.21474/IJAR01/6612]
[210]
Trujillo LT, Stanfield CT, Vela RD. The effect of electroencephalogram (EEG) reference choice on information-theoretic measures of the complexity and integration of EEG signals. Front Neurosci 2017; 11: 425.
[http://dx.doi.org/10.3389/fnins.2017.00425] [PMID: 28790884]
[211]
Zhang X, Yao L, Kanhere SS, Liu Y, Gu T, Chen K. MindID. Proc ACM Interact Mob Wearable Ubiquitous Technol 2018; 2(3): 1-23.
[http://dx.doi.org/10.1145/3264959]
[212]
Stober S, Sternin A, Owen AM, Grahn JA. Towards music imagery information retrieval: Introducing the OpenMIIR dataset of EEG recordings from music perception and imagination. In: ISMIR. 2015; pp. 763-9.
[213]
Simola J, Torniainen J, Moisala M, Kivikangas M, Krause CM. Eye movement related brain responses to emotional scenes during free viewing. Front Syst Neurosci 2013; 7: 41.
[http://dx.doi.org/10.3389/fnsys.2013.00041] [PMID: 23970856]
[214]
Kanoga S, Nakanishi M, Mitsukura Y. Assessing the effects of voluntary and involuntary eyeblinks in independent components of electroencephalogram. Neurocomputing 2016; 193: 20-32.
[http://dx.doi.org/10.1016/j.neucom.2016.01.057]

Rights & Permissions Print Cite
Article Metrics
32
2
Wayfinder Image
Open Access Journals Promotions
© 2024 Bentham Science Publishers | Privacy Policy