{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T03:41:48Z","timestamp":1780544508164,"version":"3.54.1"},"reference-count":44,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2025,12,12]],"date-time":"2025-12-12T00:00:00Z","timestamp":1765497600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Bisa Research Grant of KEIMYUNG UNIVERSITY","award":["20240421"],"award-info":[{"award-number":["20240421"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Biomimetics"],"abstract":"<jats:p>Electroencephalography (EEG)-based brain\u2013computer interface (BCI) mimics the brain\u2019s intrinsic information-processing mechanisms by translating neural oscillations into actionable commands. In motor imagery (MI) BCI, imagined movements evoke characteristic patterns over the sensorimotor cortex, forming a biomimetic channel through which internal motor intentions are decoded. However, this biomimetic interaction is highly vulnerable to signal degradation, particularly in mobile or low-resource environments where low sampling frequencies obscure these MI-related oscillations. To address this limitation, we propose a robust MI classification framework that integrates spatial, spectral, and temporal dynamics through a filter bank common spatial pattern with time segmentation (FBCSP-TS). This framework classifies motor imagery tasks into four classes (left hand, right hand, foot, and tongue), segments EEG signals into overlapping time domains, and extracts frequency-specific spatial features across multiple subbands. Segment-level predictions are combined via soft voting, reflecting the brain\u2019s distributed integration of information and enhancing resilience to transient noise and localized artifacts. Experiments performed on BCI Competition IV datasets 2a (250 Hz) and 1 (100 Hz) demonstrate that FBCSP-TS outperforms CSP and FBCSP. A paired t-test confirms that accuracy at 110 Hz is not significantly different from that at 250 Hz (p &lt; 0.05), supporting the robustness of the proposed framework. Optimal temporal parameters (window length = 3.5 s, moving length = 0.5 s) further stabilize transient-signal capture and improve SNR. External validation yielded a mean accuracy of 0.809 \u00b1 0.092 and Cohen\u2019s kappa of 0.619 \u00b1 0.184, confirming strong generalizability. By preserving MI-relevant neural patterns under degraded conditions, this framework advances practical, biomimetic BCI suitable for wearable and real-world deployment.<\/jats:p>","DOI":"10.3390\/biomimetics10120832","type":"journal-article","created":{"date-parts":[[2025,12,12]],"date-time":"2025-12-12T11:13:33Z","timestamp":1765538013000},"page":"832","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Robust Motor Imagery\u2013Brain\u2013Computer Interface Classification in Signal Degradation: A Multi-Window Ensemble Approach"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-9356-5793","authenticated-orcid":false,"given":"Dong-Geun","family":"Lee","sequence":"first","affiliation":[{"name":"Department of Medical Informatics, Keimyung University School of Medicine, 1095, Dalgubeol-daero, Dalseo-gu, Daegu 42601, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6817-5793","authenticated-orcid":false,"given":"Seung-Bo","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Medical Informatics, Keimyung University School of Medicine, 1095, Dalgubeol-daero, Dalseo-gu, Daegu 42601, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,12,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"190","DOI":"10.1016\/j.ins.2019.06.008","article-title":"Comparative analysis of features extracted from EEG spatial, spectral and temporal domains for binary and multiclass motor imagery classification","volume":"502","author":"Lee","year":"2019","journal-title":"Inf. Sci."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Sinha, G.R., Subudhi, B., Fan, C.-P., and Nisar, H. (2023). Cognitive task and workload classification using EEG signal. Cognitive Sensing Technologies and Applications, The Institution of Engineering and Technology.","DOI":"10.1049\/PBCE135E"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"031001","DOI":"10.1088\/1741-2552\/ab0ab5","article-title":"Deep learning for electroencephalogram (EEG) classification tasks: A review","volume":"16","author":"Craik","year":"2019","journal-title":"J. Neural Eng."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1016\/j.neucom.2014.09.078","article-title":"SVM-based brain\u2013machine interface for controlling a robot arm through four mental tasks","volume":"151","author":"Hortal","year":"2015","journal-title":"Neurocomputing"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TNSRE.2010.2076364","article-title":"An SSVEP BCI to control a hand orthosis for persons with tetraplegia","volume":"19","author":"Ortner","year":"2010","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"599","DOI":"10.1038\/sc.2012.14","article-title":"On the feasibility of using motor imagery EEG-based brain\u2013computer interface in chronic tetraplegics for assistive robotic arm control: A clinical test and long-term post-trial follow-up","volume":"50","author":"Onose","year":"2012","journal-title":"Spinal Cord."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Palumbo, A., Gramigna, V., Calabrese, B., and Ielpo, N. (2021). Motor-imagery EEG-based BCIs in wheelchair movement and control: A systematic literature review. Sensors, 21.","DOI":"10.36227\/techrxiv.14916537"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Saibene, A., Caglioni, M., Corchs, S., and Gasparini, F. (2023). EEG-based BCIs on motor imagery paradigm using wearable technologies: A systematic review. Sensors, 23.","DOI":"10.20944\/preprints202302.0096.v1"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Attallah, O., Abougharbia, J., Tamazin, M., and Nasser, A.A. (2020). A BCI system based on motor imagery for assisting people with motor deficiencies in the limbs. Brain Sci., 10.","DOI":"10.3390\/brainsci10110864"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"400","DOI":"10.1109\/TNSRE.2018.2790359","article-title":"Toward drowsiness detection using non-hair-bearing EEG-based brain-computer interfaces","volume":"26","author":"Wei","year":"2018","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"6165","DOI":"10.1109\/ACCESS.2022.3142311","article-title":"A combined projection for remote control of a vehicle based on movement imagination: A single trial brain computer interface study","volume":"10","author":"Hekmatmanesh","year":"2022","journal-title":"IEEE Access"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"30503","DOI":"10.1007\/s11042-019-7695-0","article-title":"Combination of discrete wavelet packet transform with detrended fluctuation analysis using customized mother wavelet with the aim of an imagery-motor control interface for an exoskeleton","volume":"78","author":"Hekmatmanesh","year":"2019","journal-title":"Multimed. Tools Appl."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3699732","article-title":"LGL-BCI: A Motor-Imagery-Based Brain-Computer Interface with Geometric Learning","volume":"9","author":"Lu","year":"2025","journal-title":"Proc. ACM Interact. Mob. Wearable Ubiquitous Technol."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Gorjup, G., Vrabi\u010d, R., Stoyanov, S.P., Andersen, M.\u00d8., and Manoonpong, P. (2018, January 13\u201316). Development of a real-time motor-imagery-based EEG brain-machine interface. Proceedings of the 25th International Conference on Neural Information Processing (ICONIP 2018), Siem Reap, Cambodia.","DOI":"10.1007\/978-3-030-04239-4_55"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"0096","DOI":"10.34133\/hds.0096","article-title":"Recent progress in wearable brain\u2013computer interface (BCI) devices based on electroencephalogram (EEG) for medical applications: A review","volume":"3","author":"Zhang","year":"2023","journal-title":"Health Data Sci."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Germano, D., Sciaraffa, N., Ronca, V., Giorgi, A., Trulli, G., Borghini, G., Di Flumeri, G., Babiloni, F., and Aric\u00f2, P. (2023). Unsupervised detection of covariate shift due to changes in EEG headset position: Towards an effective out-of-lab use of passive brain\u2013computer interface. Appl. Sci., 13.","DOI":"10.3390\/app132312800"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"031005","DOI":"10.1088\/1741-2552\/aab2f2","article-title":"A review of classification algorithms for EEG-based brain\u2013computer interfaces: A 10 year update","volume":"15","author":"Lotte","year":"2018","journal-title":"J. Neural Eng."},{"key":"ref_18","first-page":"14","article-title":"Recent applications of EEG-based brain-computer-interface in the medical field","volume":"12","author":"Liu","year":"2025","journal-title":"Mil. Med. Res."},{"key":"ref_19","first-page":"2515412","article-title":"Towards a fast and robust MI-BCI: Online adaptation of stimulus paradigm and classification model","volume":"73","author":"Wang","year":"2024","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"407","DOI":"10.1046\/j.1440-1819.2000.00729.x","article-title":"Low sampling rate induces high correlation dimension on electroencephalograms from healthy subjects","volume":"54","author":"Jing","year":"2000","journal-title":"Psychiatry Clin. Neurosci."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"9882","DOI":"10.1109\/JSEN.2018.2872835","article-title":"Impact of sampling rate on wearable-based fall detection systems based on machine learning models","volume":"18","author":"Liu","year":"2018","journal-title":"IEEE Sens. J."},{"key":"ref_22","unstructured":"Landavazo, B., and Nandikolla, V.K. (2018, January 9\u201315). Brain-computer interface application in robotic gripper control. Proceedings of the ASME 2018 International Mechanical Engineering Congress and Exposition, Pittsburgh, PA, USA. V04AT06A012."},{"key":"ref_23","unstructured":"Brunner, C., Leeb, R., M\u00fcller-Putz, G.R., Schl\u00f6gl, A., and Pfurtscheller, G. (2025, June 15). BCI Competition 2008\u2013Graz Data Set A. Available online: https:\/\/lampx.tugraz.at\/~bci\/database\/001-2014\/description.pdf."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"539","DOI":"10.1016\/j.neuroimage.2007.01.051","article-title":"The non-invasive Berlin brain\u2013computer interface: Fast acquisition of effective performance in untrained subjects","volume":"37","author":"Blankertz","year":"2007","journal-title":"Neuroimage"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"111450","DOI":"10.1016\/j.chaos.2021.111450","article-title":"Truncation thresholds based empirical mode decomposition approach for classification performance of motor imagery BCI systems","volume":"152","author":"Dagdevir","year":"2021","journal-title":"Chaos Soliton Fract."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Gramfort, A., Luessi, M., Larson, E., Engemann, D.A., Strohmeier, D., Brodbeck, C., Goj, R., Jas, M., Brooks, T., and Parkkonen, L. (2013). MEG and EEG data analysis with MNE-Python. Front. Neuroinform., 7.","DOI":"10.3389\/fnins.2013.00267"},{"key":"ref_27","unstructured":"(1908). The Probable Error of a Mean, Oxford University Press."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Tibrewal, N., Leeuwis, N., and Alimardani, M. (2022). Classification of motor imagery EEG using deep learning increases performance in inefficient BCI users. PLoS ONE, 17.","DOI":"10.1371\/journal.pone.0268880"},{"key":"ref_29","unstructured":"Wang, Y., Gao, S., and Gao, X. (2006, January 17\u201318). Common spatial pattern method for channel selection in motor imagery based brain-computer interface. Proceedings of the 2005 IEEE Engineering in Medicine and Biology Annual Conference, Shanghai, China."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1109\/MSP.2008.4408441","article-title":"Optimizing spatial filters for robust EEG single-trial analysis","volume":"25","author":"Blankertz","year":"2007","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"2274","DOI":"10.1109\/TBME.2006.883649","article-title":"Combined optimization of spatial and temporal filters for improving brain-computer interfacing","volume":"53","author":"Dornhege","year":"2006","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_32","unstructured":"Ang, K.K., Chin, Z.Y., Zhang, H., and Guan, C. (2008, January 1\u20138). Filter bank common spatial pattern (FBCSP) in brain-computer interface. Proceedings of the 2008 IEEE International Joint Conference on Neural Networks (IJCNN), Hong Kong, China."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Miltiadous, A., Tzimourta, K.D., Giannakeas, N., Tsipouras, M.G., Afrantou, T., Ioannidis, P., and Tzallas, A.T. (2021). Alzheimer\u2019s disease and frontotemporal dementia: A robust classification method of EEG signals and a comparison of validation methods. Diagnostics, 11.","DOI":"10.3390\/diagnostics11081437"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"2130002","DOI":"10.1142\/S0129065721300023","article-title":"Machine learning algorithms and statistical approaches for Alzheimer\u2019s disease analysis based on resting-state EEG recordings: A systematic review","volume":"31","author":"Tzimourta","year":"2021","journal-title":"Int. J. Neural Syst."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1023\/A:1009715923555","article-title":"A tutorial on support vector machine for pattern recognition","volume":"2","author":"Burges","year":"1998","journal-title":"Data Min. Knowl. Discov."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Awad, M., and Khanna, R. (2015). Efficient Learning Machines: Support Vector Machines for Classification, Springer.","DOI":"10.1007\/978-1-4302-5990-9"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Varshney, Y.V., and Khan, A. (2022). Imagined speech classification using six phonetically distributed words. Front. Signal Process., 2.","DOI":"10.3389\/frsip.2022.760643"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"3609","DOI":"10.1016\/j.neucom.2011.06.026","article-title":"A review of optimization methodologies in support vector machines","volume":"74","author":"Sun","year":"2011","journal-title":"Neurocomputing"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Narayan, Y., Gautam, D., Lakhwani, D., and Kakkar, R. (2025, January 6\u20138). BCI based EEG Signals classification using Machine Learning Approach. Proceedings of the 2025 IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), Gwalior, India.","DOI":"10.1109\/IATMSI64286.2025.10985151"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"12001","DOI":"10.1007\/s00521-023-08336-z","article-title":"Multi-classification for EEG motor imagery signals using data evaluation-based auto-selected regularized FBCSP and convolutional neural network","volume":"35","author":"An","year":"2023","journal-title":"Neural Comput. Appl."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"45605","DOI":"10.1109\/ACCESS.2022.3165197","article-title":"State-of-the-art versus deep learning: A comparative study of motor imagery decoding techniques","volume":"10","author":"George","year":"2022","journal-title":"IEEE Access"},{"key":"ref_42","first-page":"2578","article-title":"Visualizing data using t-SNE","volume":"9","author":"Hinton","year":"2008","journal-title":"J. Mach. Learn. Res."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Kobayashi, N., and Ino, M. (2025). Parameter optimization of 3D convolutional neural network for dry-EEG motor imagery brain-machine interface. Front. Neurosci., 19.","DOI":"10.3389\/fnins.2025.1469244"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"17521","DOI":"10.1007\/s11042-020-08675-2","article-title":"A combination of CSP-based method with soft margin SVM classifier and generalized RBF kernel for imagery-based brain computer interface applications","volume":"79","author":"Hekmatmanesh","year":"2020","journal-title":"Multimed. Tools Appl."}],"container-title":["Biomimetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2313-7673\/10\/12\/832\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,12]],"date-time":"2025-12-12T11:37:56Z","timestamp":1765539476000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2313-7673\/10\/12\/832"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,12]]},"references-count":44,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["biomimetics10120832"],"URL":"https:\/\/doi.org\/10.3390\/biomimetics10120832","relation":{},"ISSN":["2313-7673"],"issn-type":[{"value":"2313-7673","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,12]]}}}