Treffer: A Feature Extraction and Selection Framework for Electrocorticography-Based Neural Activity Classification.
Title:
A Feature Extraction and Selection Framework for Electrocorticography-Based Neural Activity Classification.
Authors:
Adanur R; Department of Electrical and Electronics Engineering, Faculty of Engineering, Gazi University, Ankara, 06570, Turkey. resuladanur@gazi.edu.tr., Arslan EE; Mayo Graduate School of Biomedical Sciences, Rochester, MN, 55905, USA., Kutbay U; Department of Electrical and Electronics Engineering, Faculty of Engineering, Gazi University, Ankara, 06570, Turkey., Akşahin MF; Department of Electrical and Electronics Engineering, Faculty of Engineering, Gazi University, Ankara, 06570, Turkey.
Source:
Journal of medical systems [J Med Syst] 2025 Nov 04; Vol. 49 (1), pp. 153. Date of Electronic Publication: 2025 Nov 04.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Kluwer Academic/Plenum Publishers Country of Publication: United States NLM ID: 7806056 Publication Model: Electronic Cited Medium: Internet ISSN: 1573-689X (Electronic) Linking ISSN: 01485598 NLM ISO Abbreviation: J Med Syst Subsets: MEDLINE
Imprint Name(s):
Publication: 1999- : New York, NY : Kluwer Academic/Plenum Publishers
Original Publication: New York, Plenum Press.
Original Publication: New York, Plenum Press.
MeSH Terms:
References:
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Wilke C, Drongelen Wv, Kohrman Mve, He B (2009) Identification of epileptogenic foci from causal analysis of ECoG interictal spike activity. Clinical Neurophysiology 120(8):1449–1456. https://doi.org/10.1016/j.clinph.2009.04.024. (PMID: 10.1016/j.clinph.2009.04.024196164742727575)
Yang T, Hakimian S, Schwartz TH (2014) Intraoperative ElectroCorticoGraphy (ECog): indications, techniques, and utility in epilepsy surgery. Epileptic Disorders 16(3):271–279. https://doi.org/10.1684/epd.2014.0675. (PMID: 10.1684/epd.2014.067525204010)
Formaggio, E., Storti, S.F., Tramontano, V., Casarin, A., Bertoldo, A., Fiaschi, A., Talacchi, A., Sala, F., Toffolo, G.M. ve Manganotti, P., Frequency and time-frequency analysis of intraoperative ECoG during awake brain stimulation, Frontiers in Neuroengineering, 6 (2013). https://doi.org/10.3389/fneng.2013.00001 .
Rossel O, Schlosser–Perrin F, Duffau H, Matsumoto R, Mandonnet E, ve Bonnetblanc F (2023) Short-range axono-cortical evoked-potentials in brain tumor surgery: Waveform characteristics as markers of direct connectivity. Clinical Neurophysiology 153:189–201. https://doi.org/10.1016/j.clinph.2023.05.011. (PMID: 10.1016/j.clinph.2023.05.01137353389)
Zhang X, Xiong Q, Dai Y, Xu X, ve Song G (2020) An ECoG-Based Binary Classification of BCI Using Optimized Extreme Learning Machine, Complexity, 2020,1. Complexity 2020(1):2913019. https://doi.org/10.1155/2020/2913019. (PMID: 10.1155/2020/2913019)
Kapeller C, Ogawa H, Schalk G, Kunii N, Coon WG, Scharinger J, Guger C, ve Kamada K (2018) Real-time detection and discrimination of visual perception using electrocorticographic signals. Journal of Neural Engineering 15(3):036001. https://doi.org/10.1088/1741-2552/aaa9f6. (PMID: 10.1088/1741-2552/aaa9f6293597115927827)
Kapeller, C., Grunwald, J., Kamada, K., Ogawa, H., Fukuyama, S., Sanada, T., Pruckl, R. ve Guger, C., Online Detection of Real-World Faces in ECoG Signals, 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 7–10 Oct. 2018 2018: 115–118. https://doi.org/10.1109/SMC.2018.00030 .
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Yang J, Singh H, Hines EL, Schlaghecken F, Iliescu DD, Leeson MS, ve Stocks NG (2012) Channel selection and classification of electroencephalogram signals: An artificial neural network and genetic algorithm-based approach. Artificial Intelligence in Medicine 55(2):117–126. https://doi.org/10.1016/j.artmed.2012.02.001. (PMID: 10.1016/j.artmed.2012.02.00122503644)
Deng, X., Li, D., Mi, J., Gao, F., Chen, Q., Wang, J. ve Liu, R., Motor Imagery ECoG Signal Classification Using Sparse Representation with Elastic Net Constraint, 2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS), 25–27 May 2018 2018: 44–49. https://doi.org/10.1109/DDCLS.2018.8515907 .
Rathipriya, N., Deepajothi, S. ve Rajendran, T., Classification of motor imagery ecog signals using support vector machine for brain computer interface, 2013 Fifth International Conference on Advanced Computing (ICoAC), 18–20 Dec. 2013 2013: 63–66. https://doi.org/10.1109/ICoAC.2013.6921928 .
Miller, K.J., Schalk, G., Hermes, D., Ojemann, J.G. ve Rao, R.P.N., Spontaneous Decoding of the Timing and Content of Human Object Perception from Cortical Surface Recordings Reveals Complementary Information in the Event-Related Potential and Broadband Spectral Change, PLOS Computational Biology, 12,1 (2016) e1004660. https://doi.org/10.1371/journal.pcbi.1004660 . (PMID: 10.1371/journal.pcbi.1004660268208994731148)
Burg, J.P., Maximum Entropy Spectral Analysis, PhD Thesis, Stanford University, 1975.
Grossmann A, ve Morlet J (1984) Decomposition of Hardy Functions into Square Integrable Wavelets of Constant Shape. SIAM Journal on Mathematical Analysis 15(4):723–736. https://doi.org/10.1137/0515056. (PMID: 10.1137/0515056)
Stoica, P. ve Moses, R.L., Spectral Analysis of Signals, 1–2, Pearson Prentice Hall, 2005.
Proakis JG, ve Manolakis DG (1996) Digital Signal Processing, 3rd edn. Prentice Hall, pp 925–927.
Ekaputri, C., Widadi, R. ve Rizal, A., EEG Signal Classification for Alcoholic and Non-Alcoholic Person using Multilevel Wavelet Packet Entropy and Support Vector Machine, 2020 8th International Conference on Information and Communication Technology (ICoICT), 24–26 June 2020 2020: 1–4. https://doi.org/10.1109/ICoICT49345.2020.9166233 .
Shaban, M., Cahoon, S., Khan, F. ve Polk, M., Exploiting the Differential Wavelet Domain of Resting-State EEG Using a Deep-CNN for Screening Parkinson’s Disease, 2021 IEEE Symposium Series on Computational Intelligence (SSCI), 5–7 Dec. 2021 2021: 1–5. https://doi.org/10.1109/SSCI50451.2021.9660178 .
Rana, M.S., Fattah, S.A. ve Saquib, M., STOW-Net: Spatio-Temporal Operation Based Deep Learning Network for Classifying Wavelet Transformed Motor Imagery EEG Signals, TENCON 2023–2023 IEEE Region 10 Conference (TENCON), 31 Oct.-3 Nov. 2023 2023: 860–863. https://doi.org/10.1109/TENCON58879.2023.10322450 .
Raj, C.R.M. ve Harsha, A., Study on wavelet spectral band based EEG compression, 2016 International Conference on Data Science and Engineering (ICDSE), 23–25 Aug. 2016 2016: 1–5. https://doi.org/10.1109/ICDSE.2016.7823955 .
Shannon CE (1948) A mathematical theory of communication. The Bell System Technical Journal 27(3):379–423. https://doi.org/10.1002/j.1538-7305.1948.tb01338.x. (PMID: 10.1002/j.1538-7305.1948.tb01338.x)
Hjorth B (1970) EEG analysis based on time domain properties. Electroencephalography and Clinical Neurophysiology 29(3):306–310. https://doi.org/10.1016/0013-4694(70)90143-4. (PMID: 10.1016/0013-4694(70)90143-44195653)
Fisher RA (1918) The Correlation between Relatives on the Supposition of Mendelian Inheritance. Transactions of the Royal Society of Edinburgh 52(2):399–433. https://doi.org/10.1017/S0080456800012163. (PMID: 10.1017/S0080456800012163)
Mehmood RM, Du Rve, Lee HJ (2017) Optimal Feature Selection and Deep Learning Ensembles Method for Emotion Recognition From Human Brain EEG Sensors. IEEE Access 5:14797–14806. https://doi.org/10.1109/ACCESS.2017.2724555. (PMID: 10.1109/ACCESS.2017.2724555)
Salami, A., Ghassemi, F. ve Moradi, M.H., A Criterion to Evaluate Feature Vectors Based on ANOVA Statistical Analysis, 2017 24th National and 2nd International Iranian Conference on Biomedical Engineering (ICBME), 30 Nov.-1 Dec. 2017 2017: 14–15. https://doi.org/10.1109/ICBME.2017.8430266 .
Nurrahma ve Yusuf, R., Comparing Different Supervised Machine Learning Accuracy on Analyzing COVID-19 Data using ANOVA Test, 2020 6th International Conference on Interactive Digital Media (ICIDM), 14–15 Dec. 2020 2020: 1–6. https://doi.org/10.1109/ICIDM51048.2020.9339676 .
Kumar, B.J., Naveen, H., Kumar, B.P., Sharma, S.S. ve Villegas, J., Logistic regression for polymorphic malware detection using ANOVA F-test, 2017 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS), 17–18 March 2017 2017: 1–5. https://doi.org/10.1109/ICIIECS.2017.8275880 .
Di Leo G, ve Sardanelli F (2020) Statistical significance: p value, 0.05 threshold, and applications to radiomics-reasons for a conservative approach. Eur Radiol Exp 4(1):18. https://doi.org/10.1186/s41747-020-0145-y. (PMID: 10.1186/s41747-020-0145-y321574897064671)
van der Maaten L, ve Hinton G (2008) Viualizing data using t-SNE. Journal of Machine Learning Research 9:2579–2605.
Quinlan JR (1990) Decision trees and decision-making. IEEE Transactions on Systems, Man, and Cybernetics 20(2):339–346. https://doi.org/10.1109/21.52545. (PMID: 10.1109/21.52545)
Cortes C, ve Vapnik V (1995) Support-vector networks. Machine Learning 20(3):273–297. https://doi.org/10.1007/BF00994018. (PMID: 10.1007/BF00994018)
Schmidhuber J (2015) Deep learning in neural networks: An overview. Neural Networks 61:85–117. https://doi.org/10.1016/j.neunet.2014.09.003. (PMID: 10.1016/j.neunet.2014.09.00325462637)
Hochreiter S, ve Schmidhuber J, Memory Long Short-Term (1997) Neural Computation 9(8):1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735. (PMID: 10.1162/neco.1997.9.8.17359377276)
Beleites C, Salzer Rve, Sergo V (2013) Validation of soft classification models using partial class memberships: An extended concept of sensitivity & co. applied to grading of astrocytoma tissues. Chemometrics and Intelligent Laboratory Systems 122:12–22. https://doi.org/10.1016/j.chemolab.2012.12.003. (PMID: 10.1016/j.chemolab.2012.12.003)
Kim JS, Im CH, Jung YJ, Kim EY, Lee SK, Chung CK (2010) Localization and propagation analysis of ictal source rhythm by electrocorticography. NeuroImage 52(4):1279–1288. https://doi.org/10.1016/j.neuroimage.2010.04.240. (PMID: 10.1016/j.neuroimage.2010.04.24020420923)
Wilke C, Drongelen Wv, Kohrman Mve, He B (2009) Identification of epileptogenic foci from causal analysis of ECoG interictal spike activity. Clinical Neurophysiology 120(8):1449–1456. https://doi.org/10.1016/j.clinph.2009.04.024. (PMID: 10.1016/j.clinph.2009.04.024196164742727575)
Yang T, Hakimian S, Schwartz TH (2014) Intraoperative ElectroCorticoGraphy (ECog): indications, techniques, and utility in epilepsy surgery. Epileptic Disorders 16(3):271–279. https://doi.org/10.1684/epd.2014.0675. (PMID: 10.1684/epd.2014.067525204010)
Formaggio, E., Storti, S.F., Tramontano, V., Casarin, A., Bertoldo, A., Fiaschi, A., Talacchi, A., Sala, F., Toffolo, G.M. ve Manganotti, P., Frequency and time-frequency analysis of intraoperative ECoG during awake brain stimulation, Frontiers in Neuroengineering, 6 (2013). https://doi.org/10.3389/fneng.2013.00001 .
Rossel O, Schlosser–Perrin F, Duffau H, Matsumoto R, Mandonnet E, ve Bonnetblanc F (2023) Short-range axono-cortical evoked-potentials in brain tumor surgery: Waveform characteristics as markers of direct connectivity. Clinical Neurophysiology 153:189–201. https://doi.org/10.1016/j.clinph.2023.05.011. (PMID: 10.1016/j.clinph.2023.05.01137353389)
Zhang X, Xiong Q, Dai Y, Xu X, ve Song G (2020) An ECoG-Based Binary Classification of BCI Using Optimized Extreme Learning Machine, Complexity, 2020,1. Complexity 2020(1):2913019. https://doi.org/10.1155/2020/2913019. (PMID: 10.1155/2020/2913019)
Kapeller C, Ogawa H, Schalk G, Kunii N, Coon WG, Scharinger J, Guger C, ve Kamada K (2018) Real-time detection and discrimination of visual perception using electrocorticographic signals. Journal of Neural Engineering 15(3):036001. https://doi.org/10.1088/1741-2552/aaa9f6. (PMID: 10.1088/1741-2552/aaa9f6293597115927827)
Kapeller, C., Grunwald, J., Kamada, K., Ogawa, H., Fukuyama, S., Sanada, T., Pruckl, R. ve Guger, C., Online Detection of Real-World Faces in ECoG Signals, 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 7–10 Oct. 2018 2018: 115–118. https://doi.org/10.1109/SMC.2018.00030 .
Elghrabawy, A. ve Wahed, M.A., Prediction of five-class finger flexion using ECoG signals, 2012 Cairo International Biomedical Engineering Conference (CIBEC), 20–22 Dec. 2012 2012: 1–5. https://doi.org/10.1109/CIBEC.2012.6473300 .
Yang J, Singh H, Hines EL, Schlaghecken F, Iliescu DD, Leeson MS, ve Stocks NG (2012) Channel selection and classification of electroencephalogram signals: An artificial neural network and genetic algorithm-based approach. Artificial Intelligence in Medicine 55(2):117–126. https://doi.org/10.1016/j.artmed.2012.02.001. (PMID: 10.1016/j.artmed.2012.02.00122503644)
Deng, X., Li, D., Mi, J., Gao, F., Chen, Q., Wang, J. ve Liu, R., Motor Imagery ECoG Signal Classification Using Sparse Representation with Elastic Net Constraint, 2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS), 25–27 May 2018 2018: 44–49. https://doi.org/10.1109/DDCLS.2018.8515907 .
Rathipriya, N., Deepajothi, S. ve Rajendran, T., Classification of motor imagery ecog signals using support vector machine for brain computer interface, 2013 Fifth International Conference on Advanced Computing (ICoAC), 18–20 Dec. 2013 2013: 63–66. https://doi.org/10.1109/ICoAC.2013.6921928 .
Miller, K.J., Schalk, G., Hermes, D., Ojemann, J.G. ve Rao, R.P.N., Spontaneous Decoding of the Timing and Content of Human Object Perception from Cortical Surface Recordings Reveals Complementary Information in the Event-Related Potential and Broadband Spectral Change, PLOS Computational Biology, 12,1 (2016) e1004660. https://doi.org/10.1371/journal.pcbi.1004660 . (PMID: 10.1371/journal.pcbi.1004660268208994731148)
Burg, J.P., Maximum Entropy Spectral Analysis, PhD Thesis, Stanford University, 1975.
Grossmann A, ve Morlet J (1984) Decomposition of Hardy Functions into Square Integrable Wavelets of Constant Shape. SIAM Journal on Mathematical Analysis 15(4):723–736. https://doi.org/10.1137/0515056. (PMID: 10.1137/0515056)
Stoica, P. ve Moses, R.L., Spectral Analysis of Signals, 1–2, Pearson Prentice Hall, 2005.
Proakis JG, ve Manolakis DG (1996) Digital Signal Processing, 3rd edn. Prentice Hall, pp 925–927.
Ekaputri, C., Widadi, R. ve Rizal, A., EEG Signal Classification for Alcoholic and Non-Alcoholic Person using Multilevel Wavelet Packet Entropy and Support Vector Machine, 2020 8th International Conference on Information and Communication Technology (ICoICT), 24–26 June 2020 2020: 1–4. https://doi.org/10.1109/ICoICT49345.2020.9166233 .
Shaban, M., Cahoon, S., Khan, F. ve Polk, M., Exploiting the Differential Wavelet Domain of Resting-State EEG Using a Deep-CNN for Screening Parkinson’s Disease, 2021 IEEE Symposium Series on Computational Intelligence (SSCI), 5–7 Dec. 2021 2021: 1–5. https://doi.org/10.1109/SSCI50451.2021.9660178 .
Rana, M.S., Fattah, S.A. ve Saquib, M., STOW-Net: Spatio-Temporal Operation Based Deep Learning Network for Classifying Wavelet Transformed Motor Imagery EEG Signals, TENCON 2023–2023 IEEE Region 10 Conference (TENCON), 31 Oct.-3 Nov. 2023 2023: 860–863. https://doi.org/10.1109/TENCON58879.2023.10322450 .
Raj, C.R.M. ve Harsha, A., Study on wavelet spectral band based EEG compression, 2016 International Conference on Data Science and Engineering (ICDSE), 23–25 Aug. 2016 2016: 1–5. https://doi.org/10.1109/ICDSE.2016.7823955 .
Shannon CE (1948) A mathematical theory of communication. The Bell System Technical Journal 27(3):379–423. https://doi.org/10.1002/j.1538-7305.1948.tb01338.x. (PMID: 10.1002/j.1538-7305.1948.tb01338.x)
Hjorth B (1970) EEG analysis based on time domain properties. Electroencephalography and Clinical Neurophysiology 29(3):306–310. https://doi.org/10.1016/0013-4694(70)90143-4. (PMID: 10.1016/0013-4694(70)90143-44195653)
Fisher RA (1918) The Correlation between Relatives on the Supposition of Mendelian Inheritance. Transactions of the Royal Society of Edinburgh 52(2):399–433. https://doi.org/10.1017/S0080456800012163. (PMID: 10.1017/S0080456800012163)
Mehmood RM, Du Rve, Lee HJ (2017) Optimal Feature Selection and Deep Learning Ensembles Method for Emotion Recognition From Human Brain EEG Sensors. IEEE Access 5:14797–14806. https://doi.org/10.1109/ACCESS.2017.2724555. (PMID: 10.1109/ACCESS.2017.2724555)
Salami, A., Ghassemi, F. ve Moradi, M.H., A Criterion to Evaluate Feature Vectors Based on ANOVA Statistical Analysis, 2017 24th National and 2nd International Iranian Conference on Biomedical Engineering (ICBME), 30 Nov.-1 Dec. 2017 2017: 14–15. https://doi.org/10.1109/ICBME.2017.8430266 .
Nurrahma ve Yusuf, R., Comparing Different Supervised Machine Learning Accuracy on Analyzing COVID-19 Data using ANOVA Test, 2020 6th International Conference on Interactive Digital Media (ICIDM), 14–15 Dec. 2020 2020: 1–6. https://doi.org/10.1109/ICIDM51048.2020.9339676 .
Kumar, B.J., Naveen, H., Kumar, B.P., Sharma, S.S. ve Villegas, J., Logistic regression for polymorphic malware detection using ANOVA F-test, 2017 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS), 17–18 March 2017 2017: 1–5. https://doi.org/10.1109/ICIIECS.2017.8275880 .
Di Leo G, ve Sardanelli F (2020) Statistical significance: p value, 0.05 threshold, and applications to radiomics-reasons for a conservative approach. Eur Radiol Exp 4(1):18. https://doi.org/10.1186/s41747-020-0145-y. (PMID: 10.1186/s41747-020-0145-y321574897064671)
van der Maaten L, ve Hinton G (2008) Viualizing data using t-SNE. Journal of Machine Learning Research 9:2579–2605.
Quinlan JR (1990) Decision trees and decision-making. IEEE Transactions on Systems, Man, and Cybernetics 20(2):339–346. https://doi.org/10.1109/21.52545. (PMID: 10.1109/21.52545)
Cortes C, ve Vapnik V (1995) Support-vector networks. Machine Learning 20(3):273–297. https://doi.org/10.1007/BF00994018. (PMID: 10.1007/BF00994018)
Schmidhuber J (2015) Deep learning in neural networks: An overview. Neural Networks 61:85–117. https://doi.org/10.1016/j.neunet.2014.09.003. (PMID: 10.1016/j.neunet.2014.09.00325462637)
Hochreiter S, ve Schmidhuber J, Memory Long Short-Term (1997) Neural Computation 9(8):1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735. (PMID: 10.1162/neco.1997.9.8.17359377276)
Beleites C, Salzer Rve, Sergo V (2013) Validation of soft classification models using partial class memberships: An extended concept of sensitivity & co. applied to grading of astrocytoma tissues. Chemometrics and Intelligent Laboratory Systems 122:12–22. https://doi.org/10.1016/j.chemolab.2012.12.003. (PMID: 10.1016/j.chemolab.2012.12.003)
Contributed Indexing:
Keywords: Classification; ECoG; Electrocorticography; Machine learning; Neural activity; Signal processing
Entry Date(s):
Date Created: 20251104 Date Completed: 20251104 Latest Revision: 20251104
Update Code:
20251104
DOI:
10.1007/s10916-025-02288-8
PMID:
41186779
Database:
MEDLINE
Weitere Informationen
Electrocorticography (ECoG) signals provide a valuable window into neural activity, yet their complex structure makes reliable classification challenging. This study addresses the problem by proposing a feature-selective framework that integrates multiple feature extraction techniques with statistical feature selection to improve classification performance. Power spectral density, wavelet-based features, Shannon entropy, and Hjorth parameters were extracted from ECoG signals obtained during a visual task. The most informative features were then selected using analysis of variance (ANOVA), and classification was performed with several machine learning methods, including decision trees, support vector machines, neural networks, and long short-term memory (LSTM) networks. Experimental results show that the proposed framework achieves high accuracy across individual patients as well as the combined dataset, with clear separability between classes confirmed through t-SNE visualization. In addition, analysis of selected features highlights the prominent role of electrodes located near the visual cortex, providing insights into the spatial distribution of neural activity.
(© 2025. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)
Declarations. Human Ethics and Consent to Participate: Not applicable. Clinical Trial Number: Not applicable. Competing interests: The authors declare no competing interests.