{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T23:19:40Z","timestamp":1780355980326,"version":"3.54.1"},"reference-count":39,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2024,11,4]],"date-time":"2024-11-04T00:00:00Z","timestamp":1730678400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>This paper advances real-time cursor control for individuals with motor impairments through a novel brain\u2013computer interface (BCI) system based solely on motor imagery. We introduce an enhanced deep neural network (DNN) classifier integrated with a Four-Class Iterative Filtering (FCIF) technique for efficient preprocessing of neural signals. The underlying approach is the Four-Class Filter Bank Common Spatial Pattern (FCFBCSP) and it utilizes a customized filter bank for robust feature extraction, thereby significantly improving signal quality and cursor control responsiveness. Extensive testing under varied conditions demonstrates that our system achieves an average classification accuracy of 89.1% and response times of 663 milliseconds, illustrating high precision in feature discrimination. Evaluations using metrics such as Recall, Precision, and F1-Score confirm the system\u2019s effectiveness and accuracy in practical applications, making it a valuable tool for enhancing accessibility for individuals with motor disabilities.<\/jats:p>","DOI":"10.3390\/info15110702","type":"journal-article","created":{"date-parts":[[2024,11,4]],"date-time":"2024-11-04T10:57:20Z","timestamp":1730717840000},"page":"702","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Enhancing Real-Time Cursor Control with Motor Imagery and Deep Neural Networks for Brain\u2013Computer Interfaces"],"prefix":"10.3390","volume":"15","author":[{"given":"Srinath","family":"Akuthota","sequence":"first","affiliation":[{"name":"Department of Electronics & Communication Engineering, SR University, Warangal 506009, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7246-3165","authenticated-orcid":false,"given":"Ravi Chander","family":"Janapati","sequence":"additional","affiliation":[{"name":"Department of Electronics & Communication Engineering, SR University, Warangal 506009, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"K. Raj","family":"Kumar","sequence":"additional","affiliation":[{"name":"Department of Electronics & Communication Engineering, SR University, Warangal 506009, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9895-7606","authenticated-orcid":false,"given":"Vassilis C.","family":"Gerogiannis","sequence":"additional","affiliation":[{"name":"Department of Digital Systems, University of Thessaly, 41500 Larissa, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9964-4134","authenticated-orcid":false,"given":"Andreas","family":"Kanavos","sequence":"additional","affiliation":[{"name":"Department of Informatics, Ionian University, 49100 Corfu, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6506-9207","authenticated-orcid":false,"given":"Biswaranjan","family":"Acharya","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering AI, Marwadi University, Rajkot 360003, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5443-1613","authenticated-orcid":false,"given":"Foteini","family":"Grivokostopoulou","sequence":"additional","affiliation":[{"name":"Computer Technology Institute and Press \u201cDiophantus\u201d, 26504 Patras, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Usha","family":"Desai","sequence":"additional","affiliation":[{"name":"Department of Electronics & Communication Engineering, S.E.A. College of Engineering & Technology, Bengaluru 560049, India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,11,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1080\/2326263X.2019.1709260","article-title":"Demonstration of a Portable Intracortical Brain-Computer Interface","volume":"6","author":"Weiss","year":"2019","journal-title":"Brain-Comput. Interfaces"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Pan, K., Li, L., Zhang, L., Li, S., Yang, Z., and Guo, Y. (2022). A Noninvasive BCI System for 2D Cursor Control Using a Spectral-Temporal Long Short-Term Memory Network. Front. Comput. Neurosci., 16.","DOI":"10.3389\/fncom.2022.799019"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.cogr.2021.02.001","article-title":"A Survey on Robots Controlled by Motor Imagery Brain-Computer Interfaces","volume":"1","author":"Zhang","year":"2021","journal-title":"Cogn. Robot."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Akuthota, S., Rajkumar, K., and Ravichander, J. (2023, January 19\u201321). EEG based Motor Imagery BCI using Four Class Iterative Filtering & Four Class Filter Bank Common Spatial Pattern. Proceedings of the International Conference on Advances in Electronics, Communication, Computing and Intelligent Information Systems (ICAECIS), Bangalore, India.","DOI":"10.1109\/ICAECIS58353.2023.10170693"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Janapati, R., Dalal, V., Kumar, G.M., Anuradha, P., and Shekar, P.V.R. (2022). Web Interface Applications Controllers used by Autonomous EEG-BCI Technologies. Proceedings of the AIP Conference Proceedings, AIP Publishing.","DOI":"10.1063\/5.0081780"},{"key":"ref_6","unstructured":"Janapati, R., Dalal, V., and Sengupta, R. (2021, January 28\u201329). Advances in Experimental Paradigms for EEG-BCI. Proceedings of the 2nd International Conference on Recent Trends in Machine Learning, IoT, Smart Cities and Applications: ICMISC 2021, Hyderabad, India. Lecture Notes in Networks and Systems."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Janapati, R., Dalal, V., Sengupta, R., and Raja Shekar, P.V. (2021, January 7\u20138). Progression of EEG-BCI Classification Techniques: A Study. Proceedings of the Inventive Systems and Control, Coimbatore, India. Lecture Notes in Networks and Systems.","DOI":"10.1007\/978-981-16-1395-1_13"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"032019","DOI":"10.1088\/1757-899X\/981\/3\/032019","article-title":"Review on EEG-BCI Classification Techniques Advancements","volume":"981","author":"Janapati","year":"2020","journal-title":"IOP Conf. Ser. Mater. Sci. Eng."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"13439","DOI":"10.1007\/s00521-020-05026-y","article-title":"Brain-Computer Interface for Amyotrophic Lateral Sclerosis Patients using Deep Learning Network","volume":"34","author":"Ramakrishnan","year":"2022","journal-title":"Neural Comput. Appl."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Mathesul, S., Swain, D., Satapathy, S.K., Rambhad, A., Acharya, B., Gerogiannis, V.C., and Kanavos, A. (2023). COVID-19 Detection from Chest X-ray Images Based on Deep Learning Techniques. Algorithms, 16.","DOI":"10.3390\/a16100494"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"8133076","DOI":"10.1155\/2021\/8133076","article-title":"Deep Learning-Based Real-Time AI Virtual Mouse System Using Computer Vision to Avoid COVID-19 Spread","volume":"2021","author":"Shriram","year":"2021","journal-title":"J. Healthc. Eng."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"101765","DOI":"10.1016\/j.artmed.2019.101765","article-title":"Design and Development of Human Computer Interface Using Electrooculogram with Deep Learning","volume":"102","author":"Teng","year":"2020","journal-title":"Artif. Intell. Med."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"046082","DOI":"10.1088\/1741-2552\/ac0584","article-title":"Benefits of Deep Learning Classification of Continuous Noninvasive Brain\u2013Computer Interface Control","volume":"18","author":"Stieger","year":"2021","journal-title":"J. Neural Eng."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Schweihoff, J.F., Loshakov, M., Pavlova, I., K\u00fcck, L., Ewell, L.A., and Schwarz, M.K. (2021). DeepLabStream Enables Closed-Loop Behavioral Experiments using Deep Learning-based Markerless, real-time Posture Detection. Commun. Biol., 4.","DOI":"10.1038\/s42003-021-01654-9"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Alam, M.S., Kwon, K., Alam, M.A., Abbass, M.Y., Imtiaz, S.M., and Kim, N. (2020). Trajectory-Based Air-Writing Recognition Using Deep Neural Network and Depth Sensor. Sensors, 20.","DOI":"10.3390\/s20020376"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Tran, D.S., Ho, N.H., Yang, H.J., Baek, E.T., Kim, S.H., and Lee, G. (2020). Real-Time Hand Gesture Spotting and Recognition Using RGB-D Camera and 3D Convolutional Neural Network. Appl. Sci., 10.","DOI":"10.3390\/app10020722"},{"key":"ref_17","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":"2022","journal-title":"Appl. Intell."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"16252","DOI":"10.1109\/JSEN.2023.3281756","article-title":"Asynchronous Motor Imagery BCI and LiDAR-Based Shared Control System for Intuitive Wheelchair Navigation","volume":"23","author":"Choi","year":"2023","journal-title":"IEEE Sens. J."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Guerrero-Mendez, C.D., Blanco-D\u00edaz, C.F., Ruiz-Olaya, A.F., Lopez-Delis, A., Jaramillo-Isaza, S., Andrade, R.M., Souza, A.F.D., Delisle-Rodriguez, D., Frizera-Neto, A., and Bastos-Filho, T.F. (2023). EEG Motor Imagery Classification using Deep Learning Approaches in Na\u00efve BCI Users. Biomed. Phys. Eng. Express, 9.","DOI":"10.1088\/2057-1976\/acde82"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1080\/2326263X.2019.1671040","article-title":"Spatio-Temporal Analysis of Error-related Brain Activity in Active and Passive Brain\u2013Computer Interfaces","volume":"6","author":"Mousavi","year":"2019","journal-title":"Brain-Comput. Interfaces"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Al-Saegh, A., Dawwd, S.A., and Abdul-Jabbar, J.M. (2021). Deep Learning for Motor Imagery EEG-based Classification: A Review. Biomed. Signal Process. Control, 63.","DOI":"10.1016\/j.bspc.2020.102172"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"14681","DOI":"10.1007\/s00521-021-06352-5","article-title":"Deep Learning Techniques for Classification of Electroencephalogram (EEG) Motor Imagery (MI) Signals: A Review","volume":"35","author":"Altaheri","year":"2023","journal-title":"Neural Comput. Appl."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Savvopoulos, A., Kanavos, A., Mylonas, P., and Sioutas, S. (2018). LSTM Accelerator for Convolutional Object Identification. Algorithms, 11.","DOI":"10.3390\/a11100157"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Sukkar, M., Shukla, M., Kumar, D., Gerogiannis, V.C., Kanavos, A., and Acharya, B. (2024). Enhancing Pedestrian Tracking in Autonomous Vehicles by Using Advanced Deep Learning Techniques. Information, 15.","DOI":"10.3390\/info15020104"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Chaddad, A., Wu, Y., Kateb, R., and Bouridane, A. (2023). Electroencephalography Signal Processing: A Comprehensive Review and Analysis of Methods and Techniques. Sensors, 23.","DOI":"10.3390\/s23146434"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1271","DOI":"10.1007\/s10439-022-03053-5","article-title":"Wavelet Based Filters for Artifact Elimination in Electroencephalography Signal: A Review","volume":"50","author":"Daud","year":"2022","journal-title":"Ann. Biomed. Eng."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"100121","DOI":"10.1016\/j.dscb.2024.100121","article-title":"ODL-BCI: Optimal Deep Learning Model for Brain-computer Interface to Classify Students Confusion via Hyperparameter Tuning","volume":"13","author":"Miah","year":"2024","journal-title":"Brain Disord."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"\u0160kola, F., Tinkov\u00e1, S., and Liarokapis, F. (2019). Progressive Training for Motor Imagery Brain-Computer Interfaces Using Gamification and Virtual Reality Embodiment. Front. Hum. Neurosci., 13.","DOI":"10.3389\/fnhum.2019.00329"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Parashiva, P.K., and Vinod, A.P. (2022). Improving Direction Decoding Accuracy during Online Motor Imagery based Brain-Computer Interface using Error-related Potentials. Biomed. Signal Process. Control, 74.","DOI":"10.1016\/j.bspc.2022.103515"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Choi, J.W., Huh, S., and Jo, S. (2020). Improving Performance in Motor Imagery BCI-based Control Applications via Virtually Embodied Feedback. Comput. Biol. Med., 127.","DOI":"10.1016\/j.compbiomed.2020.104079"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1109\/THMS.2020.2983848","article-title":"A Usability Study of Low-Cost Wireless Brain-Computer Interface for Cursor Control Using Online Linear Model","volume":"50","author":"Abiri","year":"2020","journal-title":"IEEE Trans. Hum.-Mach. Syst."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Parikh, D., and George, K. (2020, January 4\u20137). Quadcopter Control in Three-Dimensional Space Using SSVEP and Motor Imagery-Based Brain-Computer Interface. Proceedings of the 11th IEEE Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON), Vancouver, BC, Canada.","DOI":"10.1109\/IEMCON51383.2020.9284924"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Guo, Y., Wang, M., Zheng, T., Li, Y., Wang, P., and Qin, X. (2020, January 13\u201316). NAO Robot Limb Control Method Based on Motor Imagery EEG. Proceedings of the International Symposium on Computer, Consumer and Control (IS3C), Taichung City, Taiwan.","DOI":"10.1109\/IS3C50286.2020.00141"},{"key":"ref_34","unstructured":"Reyhani-Masoleh, B., and Chau, T. (2019). Navigating in Virtual Reality using Thought: The Development and Assessment of a Motor Imagery based Brain-Computer Interface. arXiv."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Gao, C., Xia, M., Zhang, Z., Han, Y., and Gu, Y. (2022). Improving the Brain-Computer Interface Learning Process with Gamification in Motor Imagery: A Review. Gamification-Analysis, Design, Development and Ludification, IntechOpen.","DOI":"10.5772\/intechopen.105715"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"2503431","DOI":"10.1155\/2019\/2503431","article-title":"A Comparison between BCI Simulation and Neurofeedback for Forward\/Backward Navigation in Virtual Reality","volume":"2019","author":"Alchalabi","year":"2019","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Saichoo, T., Boonbrahm, P., and Punsawad, Y. (2022). Investigating User Proficiency of Motor Imagery for EEG-Based BCI System to Control Simulated Wheelchair. Sensors, 22.","DOI":"10.3390\/s22249788"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1080\/2326263X.2020.1716532","article-title":"Performance Comparison of a non-invasive P300-based BCI Mouse to a Head-Mouse for People with SCI","volume":"7","author":"Huggins","year":"2020","journal-title":"Brain-Comput. Interfaces"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Hossain, K.M., Islam, M.A., Hossain, S., Nijholt, A., and Ahad, M.A.R. (2022). Status of deep learning for EEG-based brain\u2013computer interface applications. Front. Comput. Neurosci., 16.","DOI":"10.3389\/fncom.2022.1006763"}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/15\/11\/702\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:28:30Z","timestamp":1760113710000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/15\/11\/702"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,4]]},"references-count":39,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2024,11]]}},"alternative-id":["info15110702"],"URL":"https:\/\/doi.org\/10.3390\/info15110702","relation":{},"ISSN":["2078-2489"],"issn-type":[{"value":"2078-2489","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,4]]}}}