{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T23:05:23Z","timestamp":1789081523205,"version":"build-2803163510"},"reference-count":36,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T00:00:00Z","timestamp":1770768000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Brain Sciences"],"abstract":"<jats:p>Background: Traditional common spatial pattern (CSP) algorithms for upper limb neural rehabilitation face inherent challenges of overlapping cortical representations and frequency sensitivity, which hinder the decoding performance of motor imagery (MI) electroencephalogram (EEG) signals. Objective: To address these issues, this study adopts an improved discriminative filter bank CSP (DFBCSP) framework and applies it to the decoding of upper limb MI-EEG signals, achieving remarkable classification performance. Methods: EEG data were acquired from sixteen participants performing two-class (left upper limb flexion-extension vs. relaxing) and three-class (left upper limb flexion vs. right upper limb extension vs. relaxing) MI tasks. The acquired EEG data were then decomposed into nine distinct sub-bands, followed by the adoption of a mutual information-based feature selection strategy to optimize the feature sets. These optimized feature sets were subsequently input into three classification models, namely multilayer perceptron (MLP), support vector machine (SVM), and linear discriminant analysis (LDA), for MI task classification. Results: Experimental results demonstrate that the DFBCSP + MLP method significantly outperforms the traditional CSP approach. Specifically, it achieves an accuracy of 94.83% (Kappa coefficient: 0.890) in two-class MI tasks and 86.20% (Kappa coefficient: 0.775) in three-class MI tasks. Conclusion: The DFBCSP + MLP framework exhibits high robustness and provides a potential technical framework and theoretical basis for future research on the rehabilitation of patients with upper limb motor dysfunction.<\/jats:p>","DOI":"10.3390\/brainsci16020217","type":"journal-article","created":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T17:45:36Z","timestamp":1770831936000},"page":"217","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["EEG Feature Extraction and Classification for Upper Limb Flexion and Extension Motor Imagery Based on Discriminative Filter Bank Common Spatial Pattern"],"prefix":"10.3390","volume":"16","author":[{"given":"Yuqi","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Information Science and Technology, Nantong University, Nantong 226019, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4551-186X","authenticated-orcid":false,"given":"Xiaoyan","family":"Shen","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Nantong University, Nantong 226019, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,2,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Teng, J., Cho, S., and Lee, M.S. (2025). Tri-manual interaction in hybrid BCI-VR systems: Integrating gaze, EEG control for enhanced 3D object manipulation. Front. Neurorobotics, 19.","DOI":"10.3389\/fnbot.2025.1628968"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Kosmyna, N., Hauptmann, E., and Hmaidan, Y. (2023). A Brain-Controlled Quadruped Robot: A Proof-of-Concept Demonstration. Sensors, 24.","DOI":"10.3390\/s24010080"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1016\/j.apmr.2006.08.326","article-title":"Mental Practice with Motor Imagery: Evidence for Motor Recovery and Cortical Reorganization After Stroke","volume":"87","author":"Butler","year":"2006","journal-title":"Arch. Phys. Med. Rehabil."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Gruenwald, J., Znobishchev, A., KaPeller, C., Kamada, K., Scharinger, J., and Guger, C. (2019). Time-variant linear discriminant analysis improves hand gesture and finger movement decoding for invasive brain-Computer interfaces. Front. Neurosci., 13.","DOI":"10.3389\/fnins.2019.00901"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"103589","DOI":"10.1016\/j.infrared.2020.103589","article-title":"Improving classification performance of four class FNIRS-BCI using Mel Frequency Cepstral Coefficients (MFCC)","volume":"112","author":"Ghaffar","year":"2021","journal-title":"Infrared Phys. Technol."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Moraes, C.P.A., dos Santos, L.H., Fantinato, D.G., Neves, A., and Adali, T. (2024). Independent Vector Analysis for Feature Extraction in Motor Imagery Classification. Sensors, 24.","DOI":"10.3390\/s24165428"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"3106","DOI":"10.3390\/electronics14153106","article-title":"Post Hoc Event-Related Potential Analysis of Kinesthetic Motor Imagery-Based Brain-Computer Interface Control of Anthropomorphic Robotic Arms","volume":"14","author":"Spanos","year":"2025","journal-title":"Electronics"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"111438","DOI":"10.1016\/j.brainresbull.2025.111438","article-title":"Analysis of electroencephalography oscillation characteristics in spinal cord injury patients with neuropathic pain","volume":"229","author":"Wang","year":"2025","journal-title":"Brain Res. Bull."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"4824","DOI":"10.1007\/s10489-021-02622-w","article-title":"MIDNN- a classification approach for the EEG based motor imagery tasks using deep neural network","volume":"52","author":"Tiwari","year":"2021","journal-title":"Appl. Intell."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Z, Y., and Z, Q. (2021). Differential entropy feature signal extraction based on activation mode and its recognition in convolutional gated recurrent unit network. Front. Phys., 8.","DOI":"10.3389\/fphy.2020.629620"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2561","DOI":"10.12928\/telkomnika.v17i5.12805","article-title":"Energy extraction method for EEG channel selection","volume":"17","author":"Fauzi","year":"2019","journal-title":"TELKOMNIKA (Telecommun. Comput. Electron. Control)"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"49441","DOI":"10.1007\/s11042-023-17118-7","article-title":"Boosting motor imagery brain-computer interface classification using multiband and hybrid feature extraction","volume":"83","author":"Moufassih","year":"2023","journal-title":"Multimed. Tools Appl."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Al Shiam, A., Hassan, K.M., Islam, R., Almassri, A.M.M., Wagatsuma, H., and Molla, K.I. (2024). Motor Imagery Classification Using Effective Channel Selection of Multichannel EEG. Brain Sci., 14.","DOI":"10.3390\/brainsci14050462"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Al-Qazzaz, N.K., Aldoori, A.A., Ali, S.H.B.M., Ahmad, S.A., Mohammed, A.K., and Mohyee, M.I. (2023). EEG Signal Complexity Measurements to Enhance BCI-Based Stroke Patients\u2019 Rehabilitation. Sensors, 23.","DOI":"10.3390\/s23083889"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"056027","DOI":"10.1088\/1741-2552\/ad7a24","article-title":"Sensorimotor brain-computer interface performance depends on signal-to-noise ratio but not connectivity of the mu rhythm in a multiverse analysis of longitudinal data","volume":"21","author":"Kapralov","year":"2024","journal-title":"J. Neural Eng."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"877","DOI":"10.1007\/s11571-023-09940-4","article-title":"Tensor decomposition-based channel selection for motor imagery-based brain-computer interfaces","volume":"18","author":"Huang","year":"2023","journal-title":"Cogn. Neurodyn."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"3522","DOI":"10.4028\/www.scientific.net\/AMM.380-384.3522","article-title":"HHT-Based Selection of Optimal Time-Frequency Patterns for Motor Imagery","volume":"2617","author":"Gong","year":"2013","journal-title":"Appl. Mech. Mater."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"115652","DOI":"10.1016\/j.bbr.2025.115652","article-title":"An EEG-based imagined speech recognition using CSP-TP feature fusion for enhanced BCI communication","volume":"493","author":"Haresh","year":"2025","journal-title":"Behav. Brain Res."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.compbiomed.2018.09.021","article-title":"An adaptive CSP filter to investigate user independence in a 3-class MI-BCI paradigm","volume":"103","author":"Costa","year":"2018","journal-title":"Comput. Biol. Med."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1087","DOI":"10.1631\/FITEE.1800083","article-title":"Classifcation of EEG-based single-trial motor imagery tasks using a B-CSP method for BCI","volume":"20","author":"Tang","year":"2019","journal-title":"Front. Inform. Technol. Electron. Eng."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"108833","DOI":"10.1016\/j.jneumeth.2020.108833","article-title":"Improvement motor imagery EEG classification based on sparse common spatial pattern and regularized discriminant analysis","volume":"343","author":"Fu","year":"2020","journal-title":"J. Neurosci. Methods"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"016019","DOI":"10.1088\/1741-2552\/aaf046","article-title":"A penalized time-frequency band feature selection and classification procedure for improved motor intention decoding in multichannel EEG","volume":"16","author":"Peterson","year":"2019","journal-title":"J. Neural Eng."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Prattic\u00f2, D., and Lagan\u00e0, F. (2025). Infrared Thermographic Signal Analysis of Bioactive Edible Oils Using CNNs for Quality Assessment. Signals, 6.","DOI":"10.3390\/signals6030038"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"126381","DOI":"10.1016\/j.eswa.2025.126381","article-title":"A convolutional transformer network with adaptation learning modules for enhancing motor imagery classification","volume":"269","author":"Fu","year":"2025","journal-title":"Expert Syst. Appl."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"670","DOI":"10.1002\/ima.22823","article-title":"Optimal channel and frequency band-based feature selection for motor imagery electroencephalogram classification","volume":"33","author":"Meng","year":"2022","journal-title":"Int. J. Imaging Syst. Technol."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2455","DOI":"10.1007\/s11571-024-10099-9","article-title":"Brain state and dynamic transition patterns of motor imagery revealed by the bayes hidden markov model","volume":"18","author":"Liu","year":"2024","journal-title":"Cogn. Neurodynamics"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"747","DOI":"10.1007\/s12553-023-00770-2","article-title":"Classification based on sparse representations of attributes derived from empirical mode decomposition in a multiclass problem of motor imagery in EEG signals","volume":"13","author":"Gomes","year":"2023","journal-title":"Health Technol."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"2743","DOI":"10.18280\/ts.410547","article-title":"Two Class Motor Imagery EEG Signal Classification for BCI Using LDA and SVM","volume":"41","author":"Kanagaluru","year":"2024","journal-title":"Trait. Signal"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Kabir, H., Akhtar, N.I., Tasnim, N., Miah, A.S.M., Lee, H.-S., Jang, S.-W., and Shin, J. (2024). Exploring Feature Selection and Classification Techniques to Improve the Performance of an Electroencephalography-Based Motor Imagery Brain-Computer Interface System. Sensors, 24.","DOI":"10.3390\/s24154989"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"265","DOI":"10.2478\/jee-2025-0027","article-title":"Parametric optimisation of a pulmonary ventilator using the Taguchi method","volume":"76","year":"2025","journal-title":"J. Electr. Eng."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Wu, P., Fei, K., Chen, B., and Pan, L. (2025). MSEI-ENet: A Multi-Scale EEG-Inception Integrated Encoder Network for Motor Imagery EEG Decoding. Brain Sci., 15.","DOI":"10.3390\/brainsci15020129"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Cui, Y., Xie, S., Fu, Y., and Xie, X. (2023). Predicting Motor Imagery BCI Performance Based on EEG Microstate Analysis. Brain Sci., 13.","DOI":"10.3390\/brainsci13091288"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Kim, D.-H., Shin, D.-H., and Kam, T.-E. (2023). Bridging the BCI illiteracy gap: A subject-to-subject semantic style transfer for EEG-based motor imagery classification. Front. Hum. Neurosci., 17.","DOI":"10.3389\/fnhum.2023.1194751"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"056013","DOI":"10.1088\/1741-2552\/aace8c","article-title":"EEGNet: A compact convolutional neural network for EEG-based brain\u2013computer interfaces","volume":"15","author":"Lawhern","year":"2018","journal-title":"J. Neural Eng."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Wu, X., Chu, Y., Li, Q., Luo, Y., Zhao, Y., and Zhao, X. (2025). AMEEGNet: Attention-based multiscale EEGNet for effective motor imagery EEG decoding. Front. Neurorobotics, 19.","DOI":"10.3389\/fnbot.2025.1540033"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Prattic\u00f2, D., De Carlo, D., Silipo, G., and Lagan\u00e0, F. (2025). Hybrid FEM-AI Approach for Thermographic Monitoring of Biomedical Electronic Devices. Computers, 14.","DOI":"10.3390\/computers14090344"}],"container-title":["Brain Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2076-3425\/16\/2\/217\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,14]],"date-time":"2026-02-14T05:27:08Z","timestamp":1771046828000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2076-3425\/16\/2\/217"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,11]]},"references-count":36,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2026,2]]}},"alternative-id":["brainsci16020217"],"URL":"https:\/\/doi.org\/10.3390\/brainsci16020217","relation":{},"ISSN":["2076-3425"],"issn-type":[{"value":"2076-3425","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,11]]}}}