Abstract
This review investigates cutting-edge electroencephalography (EEG) signal processing techniques, focusing on noise reduction, artifact removal, and feature extraction. The study also explores emerging trends such as graph signal processing (GSP), deep learning-based methods, and real-time processing, highlighting their potential in enhancing EEG signal analysis accuracy and efficiency. This research extensively reviews state-of-the-art EEG signal processing techniques and advanced feature extraction methods. Approaches in time, frequency, and time-frequency domains are examined, with applications in cognitive neuroscience, brain–computer interfaces, and clinical diagnostics. The study also explores novel methods like GSP and deep learning, analyzing their impact on EEG signal analysis. The paper presents a comparative analysis of existing methodologies, identifying research gaps and future directions. It emphasizes the significance of GSP in exploring intricate brain networks and dynamic interactions. These findings enhance understanding of brain communication, offering insights into neurological disorders and cognitive functions. Advanced techniques showcased in this study address challenges related to non-stationary and noisy EEG signals, significantly improving accuracy and efficiency in EEG signal analysis. In summary, this review underscores the vital role of EEG signal processing in unraveling the complexities of the human brain. The study’s emphasis on robust algorithms and exploration of innovative methods advances EEG signal analysis. This research sets the stage for future developments, fostering progress in the field of EEG signal processing.




Similar content being viewed by others
Data availability
Publicly available data has been referred to in this paper.
References
Subasi A. Practical guide for biomedical signals analysis using machine learning techniques: A MATLAB based approach. Cambridge: Academic Press; 2019.
Beres AM. Time is of the essence: A review of electroencephalography (EEG) and event-related brain potentials (erps) in language research. Appl Psychophysiol Biofeedback. 2017;42:247–55.
Novik O, Smirnov F, Volgin M, Novik O, Smirnov F, Volgin M. Structures of the brain. In: Electromagnetic geophysical fields: precursors to earthquakes and tsunamis; impacts on the brain and heart. Cham: Springer; 2019. p. 69–89.
Das S, Tripathy D, Raheja JL. Real-time BCI system design to control arduino based speed controllable robot using EEG. Singapore: Springer; 2019.
Subasi A. EEG signal classification using wavelet feature extraction and a mixture of expert model. Expert Syst Appl. 2007;32(4):1084–93.
Ahmed MIB, Alotaibi S, Dash S, Nabil M, AlTurki AO. A review on machine learning approaches in identification of pediatric epilepsy. SN Comput Sci. 2022;3(6):437.
Mumtaz W, Ali SSA, Yasin MAM, Malik AS. A machine learning framework involving EEG-based functional connectivity to diagnose major depressive disorder (mdd). Med Biol Eng Comput. 2018;56:233–46.
Acharya UR, Oh SL, Hagiwara Y, Tan JH, Adeli H, Subha DP. Automated EEG-based screening of depression using deep convolutional neural network. Comput Methods Programs Biomed. 2018;161:103–13.
Anuragi A, Sisodia DS. Alcohol use disorder detection using EEG signal features and flexible analytical wavelet transform. Biomed Signal Process Control. 2019;52:384–93.
Mumtaz W, Kamel N, Ali SSA, Malik AS, et al. An EEG-based functional connectivity measure for automatic detection of alcohol use disorder. Artif Intell Med. 2018;84:79–89.
Yuvaraj R, Rajendra Acharya U, Hagiwara Y. A novel Parkinson’s disease diagnosis index using higher-order spectra features in EEG signals. Neural Comput Appl. 2018;30:1225–35.
Bairagi VK, Elgandelwar SM. Early diagnosis of Alzheimer disease using EEG signals: the role of pre-processing. Int J Biomed Eng Technol. 2023;41(4):317–39.
Ofner P, Müller-Putz GR. Movement target decoding from EEG and the corresponding discriminative sources: A preliminary study. In: 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE; 2015. p. 1468–1471
Zeng Y, Wu Q, Yang K, Tong L, Yan B, Shu J, Yao D. EEG-based identity authentication framework using face rapid serial visual presentation with optimized channels. Sensors. 2018;19(1):6.
Chen J, Mao Z, Yao W, Huang Y. EEG-based biometric identification with convolutional neural network. Multimedia Tools Appl. 2020;79:10655–75.
Wang Q, Zhao D, Wang Y, Hou X. Ensemble learning algorithm based on multi-parameters for sleep staging. Med Biol Eng Comput. 2019;57:1693–707.
Blanco JA, Vanleer AC, Calibo TK, Firebaugh SL. Single-trial cognitive stress classification using portable wireless electroencephalography. Sensors. 2019;19(3):499.
Babu NV, Kanaga EGM. Sentiment analysis in social media data for depression detection using artificial intelligence: a review. SN Comput Sci. 2022;3:1–20.
Kaur B, Singh D, Roy PP. Eyes open and eyes close activity recognition using EEG signals. In: International Conference on Cognitive Computing and Information Processing. Springer; 2017. p. 3–9
Saghafi A, Tsokos CP, Goudarzi M, Farhidzadeh H. Random eye state change detection in real-time using EEG signals. Expert Syst Appl. 2017;72:42–8.
Chen L-L, Zhao Y, Zhang J, Zou J-Z. Automatic detection of alertness/drowsiness from physiological signals using wavelet-based nonlinear features and machine learning. Expert Syst Appl. 2015;42(21):7344–55.
La Vaque T. The history of EEG Hans Berger: psychophysiologist. A historical vignette. J Neurother. 1999;3(2):1–9.
Bronzino JD, Peterson DR. Biomedical engineering fundamentals. Boca Raton: CRC Press; 2014.
Alarcao SM, Fonseca MJ. Emotions recognition using EEG signals: A survey. IEEE Trans Affect Comput. 2017;10(3):374–93.
Rahman MM, Sarkar AK, Hossain MA, Hossain MS, Islam MR, Hossain MB, Quinn JM, Moni MA. Recognition of human emotions using EEG signals: A review. Comput Biol Med. 2021;136: 104696.
Huang Z, Wang M. A review of electroencephalogram signal processing methods for brain-controlled robots. Cogn Robot. 2021;1:111–24.
Dadebayev D, Goh WW, Tan EX. EEG-based emotion recognition: Review of commercial EEG devices and machine learning techniques. J King Saud Univ Comput Inf Sci. 2022;34(7):4385–401.
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. 2001;64(6): 061907.
Andrzejak RG, Schindler K, Rummel C. Nonrandomness, nonlinear dependence, and nonstationarity of electroencephalographic recordings from epilepsy patients. Phys Rev E. 2012;86(4): 046206.
Pineda AM, Ramos FM, Betting LE, Campanharo AS. Quantile graphs for EEG-based diagnosis of Alzheimer’s disease. PLoS One. 2020;15(6): e0231169.
Shoeb AH. Application of machine learning to epileptic seizure onset detection and treatment. PhD thesis, Massachusetts Institute of Technology; 2009.
Koelstra S, Muhl C, Soleymani M, Lee J-S, Yazdani A, Ebrahimi T, Pun T, Nijholt A, Patras I. Deap: A database for emotion analysis; using physiological signals. IEEE Trans Affect Comput. 2011;3(1):18–31.
Zheng W-L, Lu B-L. Investigating critical frequency bands and channels for EEG-based emotion recognition with deep neural networks. IEEE Trans Auton Mental Dev. 2015;7(3):162–75.
Keirn ZA, Aunon JI. A new mode of communication between man and his surroundings. IEEE Trans Biomed Eng. 1990;37(12):1209–14.
PhysioBank P. Physionet: components of a new research resource for complex physiologic signals. Circulation. 2000;101(23):e215–20.
Chatterjee R, Maitra T, Islam SH, Hassan MM, Alamri A, Fortino G. A novel machine learning based feature selection for motor imagery EEG signal classification in Internet of medical things environment. Future Gener Comput Syst. 2019;98:419–434.
Zhang XL, Begleiter H, Porjesz B, Wang W, Litke A. Event related potentials during object recognition tasks. Brain Res Bull. 1995;38(6):531–8.
Ruiz-Gómez SJ, Gómez C, Poza J, Gutiérrez-Tobal GC, Tola-Arribas MA, Cano M, Hornero R. Automated multiclass classification of spontaneous EEG activity in Alzheimer’s disease and mild cognitive impairment. Entropy. 2018;20(1):35.
Bachmann M, Päeske L, Kalev K, Aarma K, Lehtmets A, Ööpik P, Lass J, Hinrikus H. Methods for classifying depression in single channel EEG using linear and nonlinear signal analysis. Comput Methods Programs Biomed. 2018;155:11–7.
Ofner P, Schwarz A, Pereira J, Müller-Putz GR. Movements of the same upper limb can be classified from low-frequency time-domain EEG signals. In: Proceedings of the Sixth International Brain-Computer Interface Meeting: BCI Past, Present, and Future (June 2016), 2016.
Akar SA, Kara S, Agambayev S, Bilgiç V. Nonlinear analysis of EEGs of patients with major depression during different emotional states. Comput Biol Med. 2015;67:49–60.
Öner M, Hu G. Analyzing one-channel EEG signals for detection of close and open eyes activities. In: 2013 Second IIAI International Conference on Advanced Applied Informatics. IEEE; 2013; p. 318–323
Khosla A, Khandnor P, Chand T. A comparative analysis of signal processing and classification methods for different applications based on EEG signals. Biocybern Biomed Eng. 2020;40(2):649–90.
Saha PK, Rahman MA, Alam MK, Ferdowsi A, Mollah MN. Common spatial pattern in frequency domain for feature extraction and classification of multichannel eeg signals. SN Comput Sci. 2021;2:1–11.
Nawaz R, Cheah KH, Nisar H, Yap VV. Comparison of different feature extraction methods for EEG-based emotion recognition. Biocybern Biomed Eng. 2020;40(3):910–26.
Zebari R, Abdulazeez A, Zeebaree D, Zebari D, Saeed J. A comprehensive review of dimensionality reduction techniques for feature selection and feature extraction. J Appl Sci Technol Trends. 2020;1(2):56–70.
Abe S. Feature selection and extraction. In: Support vector machines for pattern classification. London: Springer; 2010. p. 331–41.
Siuly S, Li Y, Zhang Y. EEG signal analysis and classification. IEEE Trans Neural Syst Rehabilit Eng. 2016;11:141–4.
Pawar D, Dhage S. Wavelet-based imagined speech classification using electroencephalography. Int J Biomed Eng Technol. 2022;38(3):215–24.
Graimann B, Allison B, Pfurtscheller G. Brain-computer interfaces: A gentle introduction. In: Brain-computer interfaces. Berlin: Springer; 2009. p. 1–27.
Al Ghayab HR, Li Y, Siuly S, Abdulla S. Epileptic EEG signal classification using optimum allocation based power spectral density estimation. IET Signal Process. 2018;12(6):738–47.
Martínez-Murcia FJ, Ortiz A, Morales-Ortega R, López P, Luque JL, Castillo-Barnes D, Segovia F, Illan IA, Ortega J, Ramirez J, et al., Periodogram connectivity of EEG signals for the detection of dyslexia. In: International Work-Conference on the Interplay Between Natural and Artificial Computation. Springer; 2019. p. 350–359
Furukawa K, Okutani K, Nagira K, Otsuka T, Itoyama K, Nakadai K, Okuno HG. Noise correlation matrix estimation for improving sound source localization by multirotor uav. In: 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE; 2013. p. 3943–3948.
Al-Fahoum AS, Al-Fraihat AA. Methods of EEG signal features extraction using linear analysis in frequency and time-frequency domains. ISRN Neurosci. 2014;2014: 730218.
Faust O, Acharya R, Allen AR, Lin C. Analysis of EEG signals during epileptic and alcoholic states using ar modeling techniques. Irbm. 2008;29(1):44–52.
Phadikar S, Sinha N, Ghosh R. Automatic eyeblink artifact removal from EEG signal using wavelet transform with heuristically optimized threshold. IEEE J Biomed Health Inform. 2020;25(2):475–84.
Borisagar KR, Thanki RM, Sedani BS. Fourier transform, short-time Fourier transform, and wavelet transform. In: Speech enhancement techniques for digital hearing aids. Cham: Springer; 2019. p. 63–74.
Stanković L, Daković M, Sejdić E. Introduction to graph signal processing. In: Vertex-frequency analysis of graph signals. Cham: Springer; 2019. p. 3–108.
Shuman DI, Narang SK, Frossard P, Ortega A, Vandergheynst P. The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE Signal Process Mag. 2013;30(3):83–98.
Hossain MS, Amin SU, Alsulaiman M, Muhammad G. Applying deep learning for epilepsy seizure detection and brain mapping visualization. ACM Trans Multimedia Comput Commun Appl (TOMM). 2019;15(1s):1–17.
Hosseini M-P, Hosseini A, Ahi K. A review on machine learning for EEG signal processing in bioengineering. IEEE Rev Biomed Eng. 2020;14:204–18.
Savadkoohi M, Oladunni T, Thompson L. A machine learning approach to epileptic seizure prediction using electroencephalogram (EEG) signal. Biocybern Biomed Eng. 2020;40(3):1328–41.
Rajaguru H, Prabhakar SK. Sparse pca and soft decision tree classifiers for epilepsy classification from EEG signals. In: 2017 International Conference of Electronics, Communication and Aerospace Technology (ICECA), vol. 1, pp. 581–584. IEEE, 2017.
Tripathi S, Acharya S, Sharma RD, Mittal S, Bhattacharya S. Using deep and convolutional neural networks for accurate emotion classification on deap dataset. In: Twenty-ninth IAAI conference, 2017.
Sharma G, Parashar A, Joshi AM. Dephnn: a novel hybrid neural network for electroencephalogram (EEG)-based screening of depression. Biomed Signal Process Control. 2021;66: 102393.
Dian JA, Colic S, Chinvarun Y, Carlen PL, Bardakjian BL. Identification of brain regions of interest for epilepsy surgery planning using support vector machines. In: 2015 37th annual international conference of the IEEE engineering in medicine and biology society (EMBC). IEEE; 2015. p. 6590–6593
Li M, Xu H, Liu X, Lu S. Emotion recognition from multichannel EEG signals using k-nearest neighbor classification. Technol Health Care. 2018;26(S1):509–19.
Siuly YL, Wen P. EEG signal classification based on simple random sampling technique with least square support vector machine. Int J Biomed Eng Technol. 2011;7(4):390–409.
Oktavia NY, Wibawa AD, Pane ES, Purnomo MH. Human emotion classification based on EEG signals using naïve bayes method. In: 2019 International Seminar on Application for Technology of Information and Communication (iSemantic). IEEE; 2019. p. 319–324.
Arora A, Lin J-J, Gasperian A, Maldjian J, Stein J, Kahana M, Lega B. Comparison of logistic regression, support vector machines, and deep learning classifiers for predicting memory encoding success using human intracranial EEG recordings. J Neural Eng. 2018;15(6): 066028.
Sunaryono D, Sarno R, Siswantoro J. Gradient boosting machines fusion for automatic epilepsy detection from EEG signals based on wavelet features. J King Saud Univ Comput Inf Sci. 2022;34(10):9591–607.
Atangana R, Tchiotsop D, Kenne G, Chanel L. EEG signal classification using lda and mlp classifier. Health Inf Int J. 2020;9(1):14–32.
Wang X, Wang Y, Liu D, Wang Y, Wang Z. Automated recognition of epilepsy from EEG signals using a combining space-time algorithm of cnn-lstm. Sci Reports. 2023;13(1):14876.
Ilias L, Askounis D, Psarras J. Multimodal detection of epilepsy with deep neural networks. Expert Syst Appl. 2023;213: 119010.
Mijwel MM. Artificial neural networks advantages and disadvantages. Mesop J Big Data. 2021;2021:29–31.
Abada R, Abubakar AM, Bilal MT. An overview on deep leaning application of big data. Mesop J Big Data. 2022;2022:31–5.
Defferrard M, Bresson X, Vandergheynst P. Convolutional neural networks on graphs with fast localized spectral filtering. In: Advances in neural information processing systems, vol. 29, 2016.
Niepert M, Ahmed M, Kutzkov K. Learning convolutional neural networks for graphs. In: International conference on machine learning. PMLR; 2016. p. 2014–2023.
Jang S, Moon S-E, Lee J-S. EEG-based video identification using graph signal modeling and graph convolutional neural network. In: 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE; 2018. p. 3066–3070.
Song T, Zheng W, Song P, Cui Z. EEG emotion recognition using dynamical graph convolutional neural networks. IEEE Trans Affect Comput. 2018;11(3):532–41.
Zhong P, Wang D, Miao C. EEG-based emotion recognition using regularized graph neural networks. IEEE Trans Affect Comput. 2020;13:1290–301.
Zhao Y, Dong C, Zhang G, Wang Y, Chen X, Jia W, Yuan Q, Xu F, Zheng Y. EEG-based seizure detection using linear graph convolution network with focal loss. Comput Methods Progr Biomed. 2021;208: 106277.
Covert IC, Krishnan B, Najm I, Zhan J, Shore M, Hixson J, Po MJ. Temporal graph convolutional networks for automatic seizure detection. In Machine Learning for Healthcare Conference. PMLR; 2019. p. 160–180.
Lun X, Jia S, Hou Y, Shi Y, Li Y. GCNs-net: a graph convolutional neural network approach for decoding time-resolved EEG motor imagery signals. arXiv preprint arXiv:2006.08924, 2020.
Author information
Authors and Affiliations
Contributions
Ramnivas Sharma: Original draft, Software, Review and editing of the paper, Formal analysis, and Results obtained. Hemant Kumar Meena: Supervised, cross-checked, and edited.
Corresponding author
Ethics declarations
Conflict of interest
No competing financial or interpersonal conflicts.
Ethical approval
Not involve any studies with animals or humans.
Informed onsent
Human participants are not involved.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Sharma, R., Meena, H.K. Emerging Trends in EEG Signal Processing: A Systematic Review. SN COMPUT. SCI. 5, 415 (2024). https://doi.org/10.1007/s42979-024-02773-w
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s42979-024-02773-w