Recent Approaches on Classification and Feature Extraction of EEG Signal: A Review

Abstract Objective: Electroencephalography (EEG) has an influential role in neuroscience and commercial applications. Most of the tools available for EEG signal analysis use machine learning to extract the required information. So, the study of robust techniques for feature extraction and classification is an important thing to understand the practical use of EEG. The paper aims that if there is any special tool for a particular task. Which feature domain or classifier has a significant role in EEG signal analysis? Approach: It presents a detailed report of the current trend for bio-electrical signals classification focusing on various classifiers’ advantages and disadvantages. This study includes literature from 2000 to 2021 with a brief description of EEG signal origin and advancement in classification techniques. Results: Randomly used classifiers for EEG signal can be categorized into five classes, namely Linear Classifiers, Nearest Neighbor Classifiers, Nonlinear Bayesian Classifiers, Neural Networks, and Combinations of Classifiers. Approximately 40% of studies use Support Vector Machine, Nearest Neighbor, and their combination with others. For specific tasks, particular classifiers are recommended in the survey. Features can be defined into four categories, namely TDFs, FDFs, TFDFs, and statistical features, where 39% of studies used TFDFs. Multi-domains features are preferred when the required information cannot be obtained from one domain. Significance: The paper summarizes the recent approaches for feature extraction and classification of EEG signals. It describes the brain waves with their classification, related behavior, and task with the physiological correlation. The comparative analysis of different classifiers, toolbox, the channel used, accuracy, and the number of subjects from various studies can help the practitioners choose a suitable classifier. Furthermore, future directions can cope up with the relevant problems and can lead to accurate classification.

[1]  M. Sivaram,et al.  A novel method of motor imagery classification using eeg signal , 2020, Artif. Intell. Medicine.

[2]  Hiram Ponce,et al.  A vision-based approach for fall detection using multiple cameras and convolutional neural networks: A case study using the UP-Fall detection dataset , 2019, Comput. Biol. Medicine.

[3]  Abdulhamit Subasi,et al.  EEG signal classification using PCA, ICA, LDA and support vector machines , 2010, Expert Syst. Appl..

[4]  Xu Huang,et al.  Feature extraction and classification of EEG signal for different brain control machine , 2016, 2016 3rd International Conference on Electrical Engineering and Information Communication Technology (ICEEICT).

[5]  Yu Zhang,et al.  Multi-kernel extreme learning machine for EEG classification in brain-computer interfaces , 2018, Expert Syst. Appl..

[6]  James R. G. Carrie,et al.  Computer Methods for Detecting and Classifying EEG Spikes and Sharp Waves , 1975 .

[7]  Ah Chung Tsoi,et al.  Nonlinear considerations in EEG signal classification , 1997, IEEE Trans. Signal Process..

[8]  Tanmay T. Verlekar,et al.  Automatic Classification of Gait Impairments Using a Markerless 2D Video-Based System , 2018, Sensors.

[9]  P. Nunez.  Representation of evoked potentials by Fourier-Bessel expansions. , 1973, IEEE transactions on bio-medical engineering.

[10]  Bilin Shao,et al.  A survey of research hotspots and frontier trends of recommendation systems from the perspective of knowledge graph , 2021, Expert Syst. Appl..

[11]  Fei Wang,et al.  A Novel Simplified Convolutional Neural Network Classification Algorithm of Motor Imagery EEG Signals Based on Deep Learning , 2020, Applied Sciences.

[12]  Akshaya R. Mane,et al.  Review paper on Feature Extraction Methods for EEG Signal Analysis , 2015 .

[13]  Arif Mahmood,et al.  Internal Emotion Classification Using EEG Signal With Sparse Discriminative Ensemble , 2019, IEEE Access.

[14]  Weidong Min,et al.  Detection of Human Falls on Furniture Using Scene Analysis Based on Deep Learning and Activity Characteristics , 2018, IEEE Access.

[15]  P. M. Pradhan,et al.  A machine learning approach to distinguish Parkinson’s disease (PD) patient’s with shuffling gait from older adults based on gait signals using 3D motion analysis , 2018, International Journal of Engineering & Technology.

[16]  Bernard Saltzberg,et al.  A New Approach, to Signal Analysis in Electroencephalography , 1957 .

[17]  Amjed S. Al-Fahoum,et al.  Methods of EEG Signal Features Extraction Using Linear Analysis in Frequency and Time-Frequency Domains , 2014, ISRN neuroscience.

[18]  John S. Barlow,et al.  Autocorrelation and Crosscorrelation Analysis in Electroencephalography , 1959 .

[19]  M Congedo,et al.  A review of classification algorithms for EEG-based brain–computer interfaces , 2007, Journal of neural engineering.

[20]  Ales Procházka,et al.  Bayesian classification and analysis of gait disorders using image and depth sensors of Microsoft Kinect , 2015, Digit. Signal Process..

[21]  Yongtian He,et al.  Deep learning for electroencephalogram (EEG) classification tasks: a review , 2019, Journal of neural engineering.

[22]  Karan Veer,et al.  A technique for classification and decomposition of muscle signal for control of myoelectric prostheses based on wavelet statistical classifier , 2015 .

[23]  Riccardo Poli,et al.  Towards the automated localisation of targets in rapid image-sifting by collaborative brain-computer interfaces , 2017, PloS one.

[24]  Yu Song,et al.  EEG based emotion recognition using fusion feature extraction method , 2020, Multimedia Tools and Applications.

[25]  J. C. Principe.  The Year in Review [T-BME issues in 2004] , 2005 .

[26]  Hany Hazfiza Manap,et al.  Parkinsonian Gait Motor Impairment Detection Using Decision Tree , 2013, 2013 European Modelling Symposium.

[27]  B. B. Zaidan,et al.  A Systematic Review for Human EEG Brain Signals Based Emotion Classification, Feature Extraction, Brain Condition, Group Comparison , 2018, Journal of Medical Systems.

[28]  M. Fernández-Del-Olmo,et al.  Walking on a treadmill improves the stride length-cadence relationship in individuals with Parkinson's disease. , 2019, Gait & posture.

[29]  R. Srinivasan,et al.  Removal of ocular artifacts from EEG using an efficient neural network based adaptive filtering technique , 1999, IEEE Signal Processing Letters.

[30]  Amir Hussain,et al.  A novel multi-modal machine learning based approach for automatic classification of EEG recordings in dementia , 2019, Neural Networks.

[31]  Turker Tekin Erguzel,et al.  Analysis of Time - Frequency EEG Feature Extraction Methods for Mental Task Classification , 2017, Int. J. Comput. Intell. Syst..

[32]  John Atkinson,et al.  Improving BCI-based emotion recognition by combining EEG feature selection and kernel classifiers , 2016, Expert Syst. Appl..

[33]  Rana Fayyaz Ahmad,et al.  Feature extraction and classification for EEG signals using wavelet transform and machine learning techniques , 2015, Australasian Physical & Engineering Sciences in Medicine.

[34]  N. Birbaumer,et al.  BCI2000: a general-purpose brain-computer interface (BCI) system , 2004, IEEE Transactions on Biomedical Engineering.

[35]  G.F. Inbar,et al.  Feature selection for the classification of movements from single movement-related potentials , 2002, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[36]  Gert Pfurtscheller,et al.  Automatic differentiation of multichannel EEG signals , 2001, IEEE Transactions on Biomedical Engineering.

[37]  Rupak K Banerjee,et al.  Pulsatile arterial wall-blood flow interaction with wall pre-stress computed using an inverse algorithm , 2015, Biomedical engineering online.

[38]  Bao-Liang Lu,et al.  Emotional state classification from EEG data using machine learning approach , 2014, Neurocomputing.

[39]  K. Müller,et al.  Spatio-Spectral Filters for Robust Classification of Single Trial EEG , 2022 .

[40]  J. L. Poage,et al.  Pattern classification applied to electroencephalographs , 1969 .

[41]  Areej Al-Wabil,et al.  Review and Classification of Emotion Recognition Based on EEG Brain-Computer Interface System Research: A Systematic Review , 2017 .

[42]  Alan S. Gevins,et al.  Classifier-Directed Signal Processing in Brain Research , 1986, IEEE Transactions on Biomedical Engineering.

[43]  Joseph J. Liang,et al.  Mathematical classification of evoked potential waveforms , 1989, IEEE Trans. Syst. Man Cybern..

[44]  Yusuf Uzzaman Khan,et al.  Brain-computer interface for single-trial eeg classification for wrist movement imagery using spatial filtering in the gamma band , 2010 .

[45]  M. Gioulis,et al.  Gait analysis and clinical correlations in early Parkinson's disease. , 2017, Functional neurology.

[46]  Emmanuel Ifeachor,et al.  A new microcomputer-based online ocular artefact removal (OAR) system , 1986 .

[47]  T.M. McGinnity,et al.  Comparative Analysis of Spectral Approaches to Feature Extraction for EEG-Based Motor Imagery Classification , 2008, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[48]  A. S. Rodionov,et al.  Comparison of linear, nonlinear and feature selection methods for EEG signal classification , 2004, International Conference on Actual Problems of Electron Devices Engineering, 2004. APEDE 2004..

[49]  Aamir Saeed Malik,et al.  Classification of EEG Signals Based on Pattern Recognition Approach , 2017, Front. Comput. Neurosci..

[50]  Yan Li,et al.  Designing a robust feature extraction method based on optimum allocation and principal component analysis for epileptic EEG signal classification , 2015, Comput. Methods Programs Biomed..

[51]  M. Houston.  Some aspects of: A College Health Service , 1942 .

[52]  Wolfram Burgard,et al.  An online system for tracking the performance of Parkinson's patients , 2017, 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[53]  Alireza Sadeghian,et al.  Online Detection of Broken Rotor Bars in Induction Motors by Wavelet Packet Decomposition and Artificial Neural Networks , 2009, IEEE Transactions on Instrumentation and Measurement.

[54]  Arantza Illarramendi,et al.  Using Kinect to classify Parkinson’s disease stages related to severity of gait impairment , 2018, BMC Bioinformatics.

[55]  Faculdade de Engenharia,et al.  Emerging Technologies in Data Mining and Information Security , 2019, Advances in Intelligent Systems and Computing.

[56]  M Congedo,et al.  A review of classification algorithms for EEG-based brain–computer interfaces: a 10 year update , 2018, Journal of neural engineering.

[57]  Sung-Yong Lee,et al.  Augmented Feedback Using Visual Cues for Movement Smoothness during Gait Performance of Individuals with Parkinson's Disease , 2012 .

[58]  Hongzhi Qi,et al.  EEG feature comparison and classification of simple and compound limb motor imagery , 2013, Journal of NeuroEngineering and Rehabilitation.

[59]  Anton Popov,et al.  Electroencephalograms during Mental Arithmetic Task Performance , 2019, Data.

[60]  Klaus-Robert Müller,et al.  Spatio-spectral filters for improving the classification of single trial EEG , 2005, IEEE Transactions on Biomedical Engineering.

[61]  She-Xiang Jiang,et al.  A Quantum Mechanics-Based Framework for EEG Signal Feature Extraction and Classification , 2022, IEEE Transactions on Emerging Topics in Computing.

[62]  Izabela Rejer,et al.  Independent Component Analysis for EEG Data Preprocessing - Algorithms Comparison , 2013, CISIM.

[63]  Zhiyong Wang,et al.  Convolutional 3D Attention Network for Video Based Freezing of Gait Recognition , 2018, 2018 Digital Image Computing: Techniques and Applications (DICTA).

[64]  Babak Taati,et al.  Vision-based assessment of parkinsonism and levodopa-induced dyskinesia with pose estimation , 2017, Journal of NeuroEngineering and Rehabilitation.

[65]  Jie Li,et al.  A Prior Neurophysiologic Knowledge Free Tensor-Based Scheme for Single Trial EEG Classification , 2009, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[66]  Yan Li,et al.  Clustering technique-based least square support vector machine for EEG signal classification , 2011, Comput. Methods Programs Biomed..

[67]  Walter J. Freeman,et al.  Classification of EEG Spatial Patterns with a Tree-Structured Methodology: CART , 1986, IEEE Transactions on Biomedical Engineering.

[68]  Hui Xiong,et al.  Unsupervised EEG feature extraction based on echo state network , 2019, Inf. Sci..

[69]  Turker Tuncer,et al.  A new stable nonlinear textural feature extraction method based EEG signal classification method using substitution Box of the Hamsi hash function: Hamsi pattern , 2021 .

[70]  Elif Derya Übeyli,et al.  Multiclass Support Vector Machines for EEG-Signals Classification , 2007, IEEE Trans. Inf. Technol. Biomed..

[71]  Sven Nomm,et al.  An Alternative Approach to Distinguish Movements of Parkinson Disease Patients , 2016 .

[72]  Hee-Cheol Kim,et al.  A Validation Study of Freezing of Gait (FoG) Detection and Machine-Learning-Based FoG Prediction Using Estimated Gait Characteristics with a Wearable Accelerometer , 2018, Sensors.

[73]  Joseph D. Bronzino,et al.  Quantitative Analysis of the EEG - General Concepts and Animal Studies , 1984, IEEE Transactions on Biomedical Engineering.

[74]  Irene Cheng,et al.  Body Movement Monitoring for Parkinson’s Disease Patients Using A Smart Sensor Based Non-Invasive Technique , 2018, 2018 IEEE 20th International Conference on e-Health Networking, Applications and Services (Healthcom).

[75]  Taha Khan,et al.  Motion Cue Analysis for Parkinsonian Gait Recognition , 2013, The open biomedical engineering journal.

[76]  Martin Schätz,et al.  Motion tracking and gait feature estimation for recognising Parkinson’s disease using MS Kinect , 2015, BioMedical Engineering OnLine.

[77]  A. C. Sanderson,et al.  Hierarchical modeling of EEG signals , 1980, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[78]  S. Panda,et al.  Multivariate Statistical Data Analysis- Principal Component Analysis (PCA) - , 2017 .

[79]  B. Sheela Rani,et al.  Feasibility of spectral domain techniques for the classification of non-stationary signals , 2020 .

[80]  C. Bigan,et al.  Time-frequency analysis of short segments of biomedical data , 2000 .

[81]  Nooritawati Md Tahir,et al.  Parkinson Disease gait classification based on machine learning approach , 2012 .

[82]  Daniel Rivero,et al.  Automatic feature extraction using genetic programming: An application to epileptic EEG classification , 2011, Expert Syst. Appl..

[83]  B. Rama Murthy,et al.  EEG Signal with Feature Extraction using SVM and ICA Classifiers , 2014 .

[84]  C.W. Anderson,et al.  Comparison of linear, nonlinear, and feature selection methods for EEG signal classification , 2003, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[85]  Elif Derya Übeyli,et al.  Adaptive neuro-fuzzy inference system for classification of EEG signals using wavelet coefficients , 2005, Journal of Neuroscience Methods.

[86]  Emad A. Awada,et al.  Wavelet-Based Feature Extraction for the Analysis of EEG Signals Associated with Imagined Fists and Feet Movements , 2014, Comput. Inf. Sci..

[87]  Tieshan Li,et al.  Efficient feature extraction framework for EEG signals classification , 2016, 2016 Seventh International Conference on Intelligent Control and Information Processing (ICICIP).

[88]  Homayoun Nikookar,et al.  Construction of Optimum Wavelet Packets for Multi-Carrier Based Spectrum Pooling Systems , 2010, Wirel. Pers. Commun..

[89]  S. Park,et al.  TDAT-time domain analysis tool for EEG analysis , 1990, IEEE Transactions on Biomedical Engineering.

[90]  Saman K. Halgamuge,et al.  Classification of Parkinson's Disease Gait Using Spatial-Temporal Gait Features , 2015, IEEE Journal of Biomedical and Health Informatics.

[91]  Ling Guan,et al.  Video Analysis of Human Gait and Posture to Determine Neurological Disorders , 2008, EURASIP J. Image Video Process..