A Feature Extraction and Selection Framework for Electrocorticography-Based Neural Activity Classification.
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 statistic...
| Publicado en: | Journal of Medical Systems Vol. 49; no. 1; pp. 1 - 14 |
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| Autores principales: | , , , |
| Formato: | diagnostic images equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
11/4/2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=189086793&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 189086793 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: 11/4/2025 vid: 49 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 189086793 189086793 189086793 10.1007/s10916-025-02288-8 189086793 ppf: 1 ppct: 13 formats: tig: atl: A Feature Extraction and Selection Framework for Electrocorticography-Based Neural Activity Classification. aug: au: Adanur, Resul Arslan, Ebubekir Enes Kutbay, Uğurhan Akşahin, Mehmet Feyzi affil: https://ror.org/054xkpr46 Department of Electrical and Electronics Engineering, Faculty of Engineering, Gazi University, 06570, Ankara, Turkey sug: subj: Neural Transmission Classification Neural Transmission Evaluation Electroencephalography Methods Signal Processing, Computer Assisted Task Performance and Analysis Visual Perception Evaluation Cerebral Cortex Physiopathology Epilepsy Physiopathology Models, Statistical Sensory Stimulation Face Perception Evaluation Housing Human Male Female Adult Middle Age Conceptual Framework Spectral Analysis Analysis of Variance Decision Trees Support Vector Machine Neural Networks (Computer) Long Short-Term Memory Experimental Studies Electrodes, Implanted Hospitals Washington Descriptive Statistics Preoperative Period Postoperative Period Tomography, X-Ray Computed Magnetic Resonance Imaging Adult: 19-44 years Middle Aged: 45-64 years Male Female ab: 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. pubtype: Academic Journal doctype: diagnostic images equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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