Prediction of gait intention from pre-movement EEG signals: a feasibility study
Open accessAbstract
Background: Prediction of Gait intention from pre-movement Electroencephalography (EEG) signals is a vital step in developing a real-time Brain-computer Interface (BCI) for a proper neuro-rehabilitation system. In that respect, this paper investigates the feasibility of a fully predictive methodology to detect the intention to start and stop a gait cycle by utilizing EEG signals obtained before the event occurrence. Methods: An eight-channel, custom-made, EEG system with electrodes placed around the sensorimotor cortex was used to acquire EEG data from six healthy subjects and two amputees. A discrete wavelet transform-based method was employed to capture event related information in alpha and beta bands in the time-frequency domain. The Hjorth parameters, namely activity, mobility, and complexity, were extracted as features while a two-sample unpaired Wilcoxon test was used to get rid of redundant features for better classification accuracy. The feature set thus obtained was then used to classify between ’walk vs. stop’ and ’rest vs. start’ classes using support vector machine (SVM) classifier with RBF kernel in a ten-fold cross-validation scheme. Results: Using a fully predictive intention detection system, 76.41±4.47% accuracy, 72.85±7.48% sensitivity, and 79.93±5.50% specificity were achieved for ’rest vs. start’ classification. While for ’walk vs. stop’ classification, the obtained mean accuracy, sensitivity, and specificity were 74.12±4.12%, 70.24±6.45%, and 77.78±7.01% respectively. Overall average True Positive Rate achieved by this methodology was 72.06±8.27% with 1.45 False Positives/min. Conclusion: Extensive simulations and resulting classification results show that it is possible to achieve statistically similar intention detection accuracy using either only pre-movement EEG features or trans-movement EEG features. The classifier performance shows the potential of the proposed methodology to predict human movement intention exclusively from the pre-movement EEG signal to be applied in real-life prosthetic and neuro-rehabilitation systems.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Independent Component Analysis (ICA) performs blind source separation extracting statistically independent components, isolates noise sources from physiological signals, and is used for EEG denoising and motor imagery feature extraction.
“| Independent Component | Blind source separation extracting | Isolate noise sources from | EEG denoising and motor imagery feature |”
From Recent advances in intelligent wearable systems: from multiscale biomechanical features towards human motion intent prediction (2026) · cites this paper as [259]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Delorme, A., & Makeig, S. (2004) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2003.10.009 · Cited by 24,371 · see citing works
A Simple Sequentially Rejective Multiple Test Procedure
(1979) · Scandinavian Journal of Statistics · doi:10.2307/4615733 · Cited by 21,526 · see citing works
A training algorithm for optimal margin classifiers
Boser, B. E., Guyon, I. M., & Vapnik, V. N. (1992) · Proceedings of the fifth annual workshop on Computational learning theory · doi:10.1145/130385.130401 · Cited by 11,992 · see citing works
Event-related EEG/MEG synchronization and desynchronization: basic principles
Pfurtscheller, G., & Lopes da Silva, F. H. (1999) · Clinical Neurophysiology · doi:10.1016/s1388-2457(99)00141-8 · Cited by 7,328 · see citing works
Frequency Dependence of Signal Power and Spatial Reach of the Local Field Potential
Leski, S., Lindén, H., Tetzlaff, T., Pettersen, K. H., & Einevoll, G. T. (2013) · PubMed Central · doi:10.18154/rwth-conv-084442 · Cited by 3,662 · see citing works
Kornhuber, H. H., & Deecke, L. (1965) · Pflügers Archiv - European Journal of Physiology · doi:10.1007/bf00412364 · Cited by 1,902 · see citing works
What is the Bereitschaftspotential?
Shibasaki, H., & Hallett, M. (2006) · Clinical Neurophysiology · doi:10.1016/j.clinph.2006.04.025 · Cited by 1,169 · see citing works
Chaumon, M., Bishop, D., & Busch, N. A. (2015) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2015.02.025 · Cited by 833 · see citing works
Independent EEG Sources Are Dipolar
Delorme, A., Palmer, J., Onton, J., Oostenveld, R., & Makeig, S. (2012) · PLoS ONE · doi:10.1371/journal.pone.0030135 · Cited by 810 · see citing works
Pfurtscheller, G., & Aranibar, A. (1979) · Electroencephalography and Clinical Neurophysiology · doi:10.1016/0013-4694(79)90063-4 · Cited by 758 · cited 7× · see citing works
“Abbreviations EEG: Electroencephalography; BCI: Brain computer interface; SVM: Support vector machine; TPR: True positive rate; EMG: Electromyography; BMI: Brainmachine interfaces; ERP: Event-related potentials; MRCP: Movement-related cortical potential; ERD/ERS: Event-related synchronization/ desynchronization; BP: B…”
BackgroundInfluentialEMG profiles during normal human walking: stride-to-stride and inter-subject variability
Winter, D. A., & Yack, H. J. (1987) · Electroencephalography and Clinical Neurophysiology · doi:10.1016/0013-4694(87)90003-4 · Cited by 681 · see citing works
Brain-Machine Interfaces: From Basic Science to Neuroprostheses and Neurorehabilitation
Lebedev, M., & Nicolelis, M. A. L. (2017) · Physiological Reviews · doi:10.1152/physrev.00027.2016 · Cited by 655 · see citing works
Real-time modeling and 3D visualization of source dynamics and connectivity using wearable EEG
Mullen, T. R., Kothe, C. A., Chi, Y. M., Ojeda, A., Kerth, T., Makeig, S., Cauwenberghs, G., & Jung, T. (2013) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2013.6609968 · Cited by 391 · see citing works
A HIGH-SPEED BRAIN SPELLER USING STEADY-STATE VISUAL EVOKED POTENTIALS
Nakanishi, M., Wang, Y., Wang, Y.-T., Mitsukura, Y., & Jung, T. (2014) · International Journal of Neural Systems · doi:10.1142/s0129065714500191 · Cited by 372 · see citing works
Event-related synchronization of mu rhythm in the EEG over the cortical hand area in man
Pfurtscheller, G., & Neuper, C. (1994) · Neuroscience Letters · doi:10.1016/0304-3940(94)90127-9 · Cited by 371 · see citing works
Evaluation of Artifact Subspace Reconstruction for Automatic EEG Artifact Removal
Chang, C.-Y., Hsu, S.-H., Pion-Tonachini, L., & Jung, T. (2018) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2018.8512547 · Cited by 351 · see citing works
Nam, K. Y., Kim, H. J., Kwon, B. S., Park, J., Lee, H. J., & Yoo, A. (2017) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/s12984-017-0232-3 · Cited by 302 · see citing works
Wagner, J., Solis‐Escalante, T., Grieshofer, P., Neuper, C., Muller-Putz, G. R., & Scherer, R. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.08.019 · Cited by 296 · see citing works
27th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
(2005) · IEEE Transactions on NanoBioscience · doi:10.1109/tnb.2005.845677 · Cited by 288 · see citing works
A Novel EEG Feature Extraction Method Using Hjorth Parameter
Oh, S.-H., Lee, Y., & Kim, H.-N. (2014) · International Journal of Electronics and Electrical Engineering · doi:10.12720/ijeee.2.2.106-110 · Cited by 264 · see citing works
Emotion recognition from EEG signals by using multivariate empirical mode decomposition
Mert, A., & Akan, A. (2016) · Pattern Analysis and Applications · doi:10.1007/s10044-016-0567-6 · Cited by 243 · see citing works
Detection of self-paced reaching movement intention from EEG signals
Lew, E., Chavarriaga, R., Silvoni, S., & Millán, J. D. R. (2012) · Frontiers in Neuroengineering · doi:10.3389/fneng.2012.00013 · Cited by 232 · see citing works
Xu, R., Jiang, N., Lin, C., Mrachacz-Kersting, N., Dremstrup, K., & Farina, D. (2014) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2013.2294203 · Cited by 219 · see citing works
Prediction of human voluntary movement before it occurs
Bai, O., Rathi, V., Lin, P., Huang, D., Battapady, H., Fei, D.-Y., Schneider, L. D., Houdayer, E., Chen, X., & Hallett, M. (2010) · Clinical Neurophysiology · doi:10.1016/j.clinph.2010.07.010 · Cited by 219 · see citing works
Self-citationHistorical Notes on the Wilcoxon Unpaired Two-Sample Test
Kruskal, W. (1957) · Journal of the American Statistical Association · doi:10.1080/01621459.1957.10501395 · Cited by 202 · see citing works
Brain-computer interface controlled robotic gait orthosis
Do, A. H., Wang, P. T., King, C., Chun, S., & Nenadić, Z. (2013) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/1743-0003-10-111 · Cited by 190 · see citing works
Shakeel, A., Navid, M. S., Anwar, M. N., Mazhar, S., Jochumsen, M., & Niazi, I. K. (2015) · Computational and Mathematical Methods in Medicine · doi:10.1155/2015/346217 · Cited by 150 · see citing works
Jiang, N., Gizzi, L., Mrachacz‐Kersting, N., Dremstrup, K., & Farina, D. (2014) · Clinical Neurophysiology · doi:10.1016/j.clinph.2014.05.003 · Cited by 144 · see citing works
Bai, O., Lin, P. T., Vorbach, S., Jiang, L., Furlani, S., & Hallett, M. (2007) · Clinical Neurophysiology · doi:10.1016/j.clinph.2007.08.025 · Cited by 122 · see citing works
Self-citationCheron, G. A., Duvinage, M., Saedeleer, C. D., Castermans, T., Bengoetxea, A., Petieau, M., Seetharaman, K., Hoellinger, T., Dan, B., Dutoit, T., et al. (2012) · Neural Plasticity · doi:10.1155/2012/375148 · Cited by 113 · see citing works
Contreras-Vidal, J. L., & Grossman, R. G. (2013) · doi:10.1109/embc.2013.6609816 · Cited by 97 · see citing works
Modeling brain dynamic state changes with adaptive mixture independent component analysis
Hsu, S.-H., Pion-Tonachini, L., Palmer, J. A., Miyakoshi, M., Makeig, S., & Jung, T. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.08.001 · Cited by 93 · see citing works
Shirota, C., Simon, A. M., & Kuiken, T. (2015) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/s12984-015-0067-8 · Cited by 89 · see citing works
Feasibility of measuring event Related Desynchronization with electroencephalography during walking
Severens, M., Nienhuis, B., Desain, P., & Duysens, J. (2012) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2012.6346537 · Cited by 88 · see citing works
Recovery of motor function after stroke
Sharma, N., & Cohen, L. G. (2010) · Developmental Psychobiology · doi:10.1002/dev.20508 · Cited by 88 · see citing works
Seizure lateralization in scalp EEG using Hjorth parameters
Cecchin, T., Ranta, R., Koessler, L., Caspary, O., Vespignani, H., & Maillard, L. G. (2009) · Clinical Neurophysiology · doi:10.1016/j.clinph.2009.10.033 · Cited by 88 · see citing works
Intention detection of gait initiation using EMG and kinematic data
Wentink, E. C., Beijen, S. I., Hermens, H. J., Rietman, J. S., & Veltink, P. H. (2012) · Gait & Posture · doi:10.1016/j.gaitpost.2012.07.013 · Cited by 78 · see citing works
Sburlea, A., Montesano, L., & Minguez, J. (2015) · Journal of Neural Engineering · doi:10.1088/1741-2560/12/3/036007 · Cited by 76 · see citing works
Li, J., Ji, H., Cao, L., Zang, D., GU, R., Xia, B., & Wu, Q. (2014) · International Journal of Neural Systems · doi:10.1142/s0129065714500142 · Cited by 72 · see citing works
Detecting intention to walk in stroke patients from pre-movement EEG correlates
Sburlea, A., Montesano, L., de la Cuerda, R. C., Diego, I. M. A., Miangolarra-Page, J., & Minguez, J. (2015) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/s12984-015-0087-4 · Cited by 65 · see citing works
Single-trial classification of gait and point movement preparation from human EEG
Velu, P. D., & de Sa, V. D. (2013) · Frontiers in Neuroscience · doi:10.3389/fnins.2013.00084 · Cited by 60 · see citing works
Recursive independent component analysis for online blind source separation
Akhtar, M. T., Jung, T., Makeig, S., & Cauwenberghs, G. (2012) · IEEE International Symposium on Circuits and Systems proceedings · doi:10.1109/iscas.2012.6271896 · Cited by 57 · see citing works
Advances in Computational Intelligence
Batyrshin, I., & Potosí, S. M. 2. S. L. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-37798-3 · Cited by 56 · see citing works
Jensen, W., Andersen, O. K., & Akay, M. (2014) · Biosystems & biorobotics · doi:10.1007/978-3-319-08072-7 · Cited by 55 · see citing works
López‐Larraz, E., Antelis, J. M., Montesano, L., Gil-Agudo, A. M., & Mínguez, J. (2012) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2012.6346299 · Cited by 51 · see citing works
EEG-Based Detection of Starting and Stopping During Gait Cycle
Hortal, E., Úbeda, A., Iáñez, E., Azorín, J. M., & Fernández, E. (2016) · International Journal of Neural Systems · doi:10.1142/s0129065716500295 · Cited by 47 · see citing works
Shahnaz, C., Shoaib-Bin-Masud, & Hasan, S. M. S. (2016) · IEEE Region 10 Conference (TENCON) · doi:10.1109/tencon.2016.7848034 · Cited by 44 · see citing works
Online recursive independent component analysis for real-time source separation of high-density EEG
Hsu, S.-H., Mullen, T. R., Jung, T., & Cauwenberghs, G. (2014) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2014.6944462 · Cited by 35 · see citing works
Movement artefact removal from NIRS signal using multi-channel IMU data
Siddiquee, M. R., Marquez, J. S., Atri, R., Ramon, R., Perry Mayrand, R., & Bai, O. (2018) · BioMedical Engineering OnLine · doi:10.1186/s12938-018-0554-9 · Cited by 33 · see citing works
Self-citationReal-time EEG Source-mapping Toolbox (REST): Online ICA and source localization
Pion-Tonachini, L., Hsu, S.-H., Makeig, S., Jung, T., & Cauwenberghs, G. (2015) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2015.7319299 · Cited by 32 · see citing works
Discriminative Manifold Learning Based Detection of Movement-Related Cortical Potentials
Lin, C., Wang, B.-H., Jiang, N., Xu, R., Mrachacz-Kersting, N., & Farina, D. (2016) · IEEE transactions on neural systems and rehabilitation engineering · doi:10.1109/tnsre.2016.2531118 · Cited by 32 · see citing works
Application of the Stockwell Transform to Electroencephalographic Signal Analysis during Gait Cycle
Ortíz, M., Rodríguez-Ugarte, M., Iáñez, E., & Azorín, J. M. (2017) · Frontiers in Neuroscience · doi:10.3389/fnins.2017.00660 · Cited by 26 · see citing works
An EEG-Based Design for the Online Detection of Movement Intention
Ibáñez, J., Serrano, J. I., del Castillo, M. D., Barrios, L., Gallego, J. A., & Rocon, E. (2011) · International Work-Conference on Artificial and Natural Neural Networks · doi:10.1007/978-3-642-21501-8_46 · Cited by 24 · see citing works
Towards a non-invasive brain-machine interface system to restore gait function in humans
Presacco, A., Forrester, L., & Contreras-Vidal, J. L. (2011) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/iembs.2011.6091136 · Cited by 22 · see citing works
Advances in Computational Intelligence
Greco, S., Yager, R. R., Benedetto Matarazzo, A., Fedrizzi, M., Coletti, G., & Bouchon‐Meunier, B. (2012) · Communications in computer and information science · doi:10.1007/978-3-642-31718-7 · Cited by 21 · see citing works
Antelis, J. M., Montesano, L., Ramos‐Murguialday, A., Birbaumer, N., & Mínguez, J. (2012) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2012.6346868 · Cited by 19 · see citing works
Classification of mental task EEG records using Hjorth parameters
Türk, Ö., Şeker, M., Akpolat, V., & Özerdem, M. S. (2017) · Signal Processing and Communications Applications Conference (SIU) · doi:10.1109/siu.2017.7960608 · Cited by 19 · see citing works
Topography of mobility and complexity parameters of the EEG in Alzheimer's Disease
Martín‐Loeches, M., García–Trapero, J., Gil, P., & Rubia, F. J. (1991) · Biological Psychiatry · doi:10.1016/0006-3223(91)90181-k · Cited by 17 · see citing works
Savić, A. M., Lontis, R., Jiang, N., Popović, M., Farina, D., Dremstrup, K., & Mrachacz‐Kersting, N. (2014) · Biosystems & biorobotics · doi:10.1007/978-3-319-08072-7_98 · Cited by 17 · see citing works
Hasan, S. M. S., Siddiquee, M. R., & Bai, O. (2019) · 2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA) · doi:10.1109/icmla.2019.00242 · Cited by 13 · see citing works
Self-citationSmart Data-Driven Optimization of Powered Prosthetic Ankles Using Surface Electromyography
Atri, R., Marquez, J. S., Leung, C., Siddiquee, M. R., Murphy, D. P., Gorgey, A. S., Lovegreen, W. T., Fei, D.-Y., & Bai, O. (2018) · Sensors · doi:10.3390/s18082705 · Cited by 11 · see citing works
Self-citationSensor Fusion in Human Cyber Sensor System for Motion Artifact Removal from NIRS Signal
Siddiquee, M. R., Xue, T., Marquez, J. S., Atri, R., Ramon, R., Mayrand, R. P., Leung, C., & Bai, O. (2019) · Conference on Human System Interactions · doi:10.1109/hsi47298.2019.8942617 · Cited by 11 · see citing works
Self-citationShrestha, S., Morshed, S. A., Pradhananga, N., & Lv, X. (2020) · ASCE Construction Research Congress (CRC) · doi:10.1061/9780784482872.053 · Cited by 11 · see citing works
Ortíz, M., Ianez, E., Rodríguez-Ugarte, M., & Azorín, J. M. (2017) · IEEE RO-MAN · doi:10.1109/roman.2017.8172286 · Cited by 6 · see citing works
Morshed, S. A., Lv, X., & Tanvir, R. B. (2020) · ASCE Construction Research Congress (CRC) · doi:10.1061/9780784482865.046 · Cited by 5 · see citing works
Exploration of Metrics for Leg Length Discrepancy Using a Wearable Gait Analysis System
Marquez, J. S., Atri, R., & Bai, O. (2018) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2018.8513290 · Cited by 3 · see citing works
Self-citationA New Formulation for Cost-Sensitive Two Group Support Vector Machine with Multiple Error Rate
Najafi, A., & Nedaie, A. (2018) · Journal of industrial and systems engineering. · Cited by 1 · see citing works
Flagged first, then by citations
Research works printed but not linked: 1
- [11]Kornhuber HH Deecke L Changes in the brain potential in voluntary movements and passive movements in man: readiness potential and reafferent potentials, Pflugers Archiv fur die gesamte Physiologie des Menschen und der Tiere 1965 284 1 17 14341490
Cited by
Sorted by citations
Samal, P., & Hashmi, M. F. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-023-10690-2 · Cited by 163 · see citing works
Shin, U., Ding, C., Zhu, B., Vyza, Y., Trouillet, A., Revol, E. C. M., Lacour, S. P., & Shoaran, M. (2022) · IEEE Journal of Solid-State Circuits · doi:10.1109/jssc.2022.3204508 · Cited by 124 · see citing works
A Survey on Security and Privacy Issues in Modern Healthcare Systems: Attacks and DefensesPreprint
Newaz, A. I., Sikder, A. K., Rahman, M. A., & Uluagac, A. S. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2005.07359 · Cited by 61 · see citing works
Emotion Recognition Based on EEG Using Generative Adversarial Nets and Convolutional Neural Network
Pan, B., & Zheng, W. (2021) · Computational and Mathematical Methods in Medicine · doi:10.1155/2021/2520394 · Cited by 55 · see citing works
Kolaghassi, R., Al-Hares, M. K., & Sirlantzis, K. (2021) · IEEE Access · doi:10.1109/access.2021.3104464 · Cited by 55 · see citing works
Prediction of gait trajectories based on the Long Short Term Memory neural networks
Zaroug, A., Garofolini, A., Lai, D. T. H., Mudie, K., & Begg, R. (2021) · PLOS ONE · doi:10.1371/journal.pone.0255597 · Cited by 48 · see citing works
Research of intent recognition in rehabilitation robots: a systematic review
Luo, S., Meng, Q., Li, S., & Yu, H. (2023) · Disability and Rehabilitation Assistive Technology · doi:10.1080/17483107.2023.2170477 · Cited by 48 · see citing works
Neural Correlates of Freezing of Gait in Parkinson's Disease: An Electrophysiology Mini-Review
Marquez, J. S., Hasan, S. M. S., Siddiquee, M. R., Luca, C. C., Mishra, V. R., Mari, Z. K., & Bai, O. (2020) · Frontiers in Neurology · doi:10.3389/fneur.2020.571086 · Cited by 46 · see citing works
Self-citationTsai, B.-Y., Diddi, S. V. S., Ko, L., Wang, S., Chang, C.-Y., & Jung, T. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3174528 · Cited by 32 · see citing works
Human motion prediction for intelligent construction: A review
Xia, X., Zhou, T., Du, J. K., & Li, N. (2022) · Automation in Construction · doi:10.1016/j.autcon.2022.104497 · Cited by 31 · see citing works
A multi-modal modified feedback self-paced BCI to control the gait of an avatar
Alchalabi, B., Faubert, J., & Labbé, D. R. (2021) · Journal of Neural Engineering · doi:10.1088/1741-2552/abee51 · Cited by 25 · see citing works
Asynchronous Prediction of Human Gait Intention in a Pseudo Online Paradigm Using Wavelet Transform
Hasan, S. M. S., Siddiquee, M. R., & Bai, O. (2020) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2020.2998778 · Cited by 22 · see citing works
Self-citationCoordinating human-robot collaboration by EEG-based human intention prediction and vigilance control
Lyu, J., Maÿe, A., Görner, M., Ruppel, P., Engel, A. K., & Zhang, J. (2022) · Frontiers in Neurorobotics · doi:10.3389/fnbot.2022.1068274 · Cited by 19 · see citing works
Low limb prostheses and complex human prosthetic interaction: A systematic literature review
Domínguez-Ruiz, A., López-Caudana, E. O., Lugo‐González, E., Espinosa-García, F. J., Ambrocio-Delgado, R., García, U. D., López-Gutiérrez, R., Alfaro-Ponce, M., & Ponce, P. (2023) · Frontiers in Robotics and AI · doi:10.3389/frobt.2023.1032748 · Cited by 18 · see citing works
Exploring Imagined Movement for Brain–Computer Interface Control: An fNIRS and EEG Review
Finnis, R., Mehmood, A., Holle, H., & Iqbal, J. (2025) · Brain Sciences · doi:10.3390/brainsci15091013 · Cited by 16 · see citing works
Dong, R., Zhang, X., Li, H., Masengo, G., Zhu, A., Shi, X., & He, C. (2024) · Frontiers in Neuroscience · doi:10.3389/fnins.2023.1305850 · Cited by 15 · see citing works
Janyalikit, T., & Ratanamahatana, C. A. (2022) · IEEE Access · doi:10.1109/access.2022.3167703 · Cited by 14 · see citing works
Li, C., Xu, Y., Feng, T., Wang, M., Zhang, X., Zhang, L., Cheng, R., Chen, W., Chen, W., & Zhang, S. (2025) · Frontiers in Neuroscience · doi:10.3389/fnins.2025.1532099 · Cited by 11 · see citing works
Enhancement of Movement Intention Detection Using EEG Signals Responsive to Emotional Music Stimulus
Hasan, S. M. S., Siddiquee, M., Marquez, J., & Bai, O. (2022) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2020.3025004 · Cited by 10 · see citing works
Self-citationEEG Dynamics of Locomotion and Balancing: Solution to Neuro-Rehabilitation
Khajuria, A., Sharma, R., & Joshi, D. (2022) · Clinical EEG and Neuroscience · doi:10.1177/15500594221123690 · Cited by 11 · see citing works
Quiles, V., Ferrero, L., Iáñez, E., Ortiz, M., Cano, J. M., & Azorín, J. M. (2022) · Applied Sciences · doi:10.3390/app12010415 · Cited by 10 · see citing works
Integration of Inertial Sensors in a Lower Limb Robotic Exoskeleton
Calle-Sigüencia, J., Callejas-Cuervo, M., & García-Reino, S. (2022) · Sensors · doi:10.3390/s22124559 · Cited by 10 · see citing works
Hasan, S. M. S., Marquez, J. S., Siddiquee, M. R., Fei, D.-Y., & Bai, O. (2021) · IEEE Access · doi:10.1109/access.2021.3075253 · Cited by 10 · see citing works
Self-citationChen, S., Peng, C., Yang, B., Lin, J., Zhou, L., Jiang, Z., Liu, Z., Liu, Y., & Tang, L. (2026) · npj Artificial Intelligence · doi:10.1038/s44387-026-00083-5 · Cited by 9 · see citing works
Kritikos, I., Makrypidis, A., Alevizopoulos, A. G., Alevizopoulos, G. A., & Koutsouris, D. (2023) · Virtual Worlds · doi:10.3390/virtualworlds2020011 · Cited by 9 · see citing works
A Method for Real-Time Prediction of Gait Events Based on Walking Speed Estimation
Chen, W., Chang, L., Fang, X., & Zhang, Q. (2024) · IEEE Sensors Journal · doi:10.1109/jsen.2024.3469250 · Cited by 9 · see citing works
Yen, Y.-L., Ye, S.-K., Liang, J. N., & Lee, Y. (2023) · Gait & Posture · doi:10.1016/j.gaitpost.2023.08.009 · Cited by 9 · see citing works
Kim, H., Kim, Y., Miyakoshi, M., Stapornchaisit, S., Yoshimura, N., & Koike, Y. (2021) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2021.767477 · Cited by 8 · see citing works
Towards Prosthesis Control: Identification of Locomotion Activities through EEG-Based Measurements
Zafar, S., Maqbool, H. F., Ashraf, M. I., Malik, D. J., Abideen, Z. U., Ali, W., Taborri, J., & Rossi, S. (2024) · Robotics · doi:10.3390/robotics13090133 · Cited by 7 · see citing works
Wang, Y., Fang, Y., Huang, W., Liu, Z., Zhao, C., Du, M., & Zhu, L. (2025) · IEEE Access · doi:10.1109/access.2025.3550500 · Cited by 7 · see citing works
Wang, C., Li, X., Guo, Y., Zhang, R., & Chen, W. (2022) · Medicine in Novel Technology and Devices · doi:10.1016/j.medntd.2022.100165 · Cited by 7 · see citing works
Lu, Y., Wang, H., Niu, J., Lü, Z., Liu, C., & Feng, N. (2024) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2024.107001 · Cited by 6 · see citing works
Emerging Non-invasive Brain–Computer Interface Technologies and Their Clinical Applications
Stevenson, C., Chang, Y., He, C., Phang, C., Su, C.-H., Lin, R.-W., & Ko, L. (2022) · Lecture notes in networks and systems · doi:10.1007/978-981-19-2940-3_19 · Cited by 6 · see citing works
Lazcano-Herrera, A. G., Fuentes-Aguilar, R. Q., Ramírez-Morales, A. O., & Alfaro-Ponce, M. (2023) · IEEE Access · doi:10.1109/access.2023.3328254 · Cited by 5 · see citing works
Decoding Brain Signals to Classify Gait Direction Anticipation
Vaghei, Y., Park, E. J., & Arzanpour, S. (2022) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc48229.2022.9871566 · Cited by 5 · see citing works
Research on two-class and four-class action recognition based on EEG signals
Chang, Y., Wang, L., Zhao, Y., Liu, M., & Zhang, J. (2023) · Mathematical Biosciences & Engineering · doi:10.3934/mbe.2023455 · Cited by 4 · see citing works
Ding, F., Dong, L., & Yu, Y. (2024) · WRC Symposium on Advanced Robotics and Automation (WRC SARA) · doi:10.1109/wrcsara64167.2024.10685749 · Cited by 3 · see citing works
VMD-WSST: A Combined BCI Algorithm to Predict Self-paced Gait Intention*
Hasan, S. M. S., & Bai, O. (2021) · IEEE International Conference on Systems, Man and Cybernetics · doi:10.1109/smc52423.2021.9658856 · Cited by 4 · see citing works
Self-citationAguiar, D. P., Filho, P. C. D. N., Moreira, A. G., Alves, G. C. V. D. M., Hotta, G. H., & Júnior, F. F. U. S. (2022) · Brazilian Journal Of Pain · doi:10.5935/2595-0118.20220040-en · Cited by 3 · see citing works
Yen, Y.-L., & Lee, Y.-J. (2025) · Gait & Posture · doi:10.1016/j.gaitpost.2025.110069 · Cited by 2 · see citing works
Zahid, F. B., Ong, Z. C., Khoo, S. Y., & Salleh, M. F. M. (2023) · Measurement · doi:10.1016/j.measurement.2023.112454 · Cited by 2 · see citing works
Prediction of Gait Intention by Utilizing Pre-Movement EEG Signals: A Pilot Study
Naser, M. Y. M., Mcclary, C., Cheruku, I. A., Dendi, B. P. R., & Bhattacharya, S. (2023) · IEEE SoutheastCon · doi:10.1109/southeastcon51012.2023.10115079 · Cited by 2 · see citing works
A Machine Learning Model for Predicting Critical Minimum Foot Clearance (MFC) Heights
Nagano, H., Prokofieva, M., Asogwa, C. O., Sarashina, E., & Begg, R. K. (2024) · Applied Sciences · doi:10.3390/app14156705 · Cited by 2 · see citing works
Sanchez-Bautista, J. A., Antelis, J. M., & Mendoza-Montoya, O. (2025) · IEEE Access · doi:10.1109/access.2025.3553554 · Cited by 2 · see citing works
Classification of EEG Signals on Standing, Walking and Running Dataset using LSTM-RNN
Murugavalli, K., Ramalakshmi, R., Rajasekaran, M. P., & Gandhi, V. (2022) · International Conference on Advances in Computing, Communication Control and Networking (ICAC3N) · doi:10.1109/icac3n56670.2022.10074500 · Cited by 1 · see citing works
Dong, R., Zhang, X., Zhou, Z., Zha, W., & Zhu, A. (2025) · Journal of Applied Biomedicine · doi:10.1016/j.bbe.2025.06.004 · Cited by 1 · see citing works
EEG-to-gait decoding via phase-aware representation learning
Fu, X., Jiang, W., Liu, R., Muller-Putz, G. R., & Guan, C. (2026) · Neural Networks · doi:10.1016/j.neunet.2026.108608 · Cited by 1 · see citing works
Decoding Locomotion intention in Virtual Reality Using EEG
Cho, Y.-T., Yoshimura, N., & Martínez-Tejada, L. A. (2023) · Biomedical Engineering International Conference · doi:10.1109/bmeicon60347.2023.10321978 · Cited by 1 · see citing works
Enhancement of impact synchronous modal analysis with brain-computer interface
Zahid, F. B., Ong, Z. C., Khoo, S. Y., Salleh, M. F. M., & Akram, N. (2024) · Measurement Science and Technology · doi:10.1088/1361-6501/ad8df4 · Cited by 1 · see citing works
A Machine Learning Model for Predicting Critical Minimum Foot Clearance (MFC) HeightsPreprint
Asogwa, C. O., Nagano, H., Sarashina, E., Begg, R. K., & Sankai, Y. (2023) · Preprints.org · doi:10.20944/preprints202308.1076.v1 · Cited by 1 · see citing works
Sensor Approach for Brain Pathophysiology of Freezing of Gait in Parkinson's Disease Patients
Marquez, J. S. (2020) · doi:10.25148/etd.fidc009227
Self-citationEEG Connectivity Analysis in a Motor Imagery Task
Covantes-Osuna, C., Paredes, O., García, D. Y. D. L. M., Vélez-Pérez, H. A., & Romo‐Vázquez, R. (2022) · World Congress on Medical Physics and Biomedical Engineering, September 7 - 12, 2009, Munich, Germany · doi:10.1007/978-3-031-18256-3_37
Towards a Gait Planning Training Strategy Using Lokomat
de Albuquerque, T. S., da Costa, L. J., da Silva, E. R., Rocha, G. K. L., Dantas, A. F. O. D. A., Santo, C. C. D. E., & Delisle-Rodríguez, D. (2024) · Lecture notes in networks and systems · doi:10.1007/978-3-031-47272-5_30
Hand Movement Recognition Using Dynamical Graph Convolutional Neural Network in EEG Source Space
Tao, Y., Xu, W., Zhu, J., Wang, M., & Wang, G. (2024) · World Congress on Medical Physics and Biomedical Engineering, September 7 - 12, 2009, Munich, Germany · doi:10.1007/978-3-031-51455-5_35
Kubacska, R. I., Hende, C., Csipor, J., & Márton, G. (2024) · IEEE BioSensors Conference (BioSensors) · doi:10.1109/biosensors61405.2024.10712665
Realtime Gait Phase Prediction and Early Detection of Gait Abnormalities
Jha, C., Jha, S. K., Bandyopadhyay, O., & Nandi, D. (2024) · International Conference on Computer, Communication, Control and Information Technology · doi:10.1109/c3it60531.2024.10829427
Posterior parietal cortex activity during visually cued gait: a preliminary study
McDonnell, P., Grimmitt, A. B., Jaramillo, J. M., Hoogkamer, W., & Martini, D. N. (2025) · Experimental Brain Research · doi:10.1007/s00221-025-07146-0
Zhu, C., Zheng, T., Yi, J., & Dutta, A. (2025) · IEEE/ASME International Conference on Advanced Intelligent Mechatronics · doi:10.1109/aim64088.2025.11175787
EEG-Based Gait Phase Decoding From Combined Action Observation and Motor Imagery
Ravi, A., Jiang, N., & Tung, J. Y. (2026) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2026.3659011
fard, M. K., Fallah, A., & Maleki, A. (2026) · Journal of Applied Biomedicine · doi:10.1016/j.bbe.2026.02.001
Boncompte, P., Martínez-Tejada, L. A., & Yoshimura, N. (2026) · International Winter Conference on Brain-Computer Interface (BCI) · doi:10.1109/bci69045.2026.11435097
Wan, K. R., Ng, Z.-Y. V., Wee, S. K., Misbaah, F., Lui, W. L., Phua, M. W., Plunkett, T. K., Robinson, N., Wai, A. A. P., 关存太, et al. (2026) · Frontiers in Neurology · doi:10.3389/fneur.2026.1967078
Joint State Estimation Across Diverse Dynamic Conditions Using PAM Air Dynamics
Willke, R. F., Miyazaki, T., & Kawashima, K. (2026) · SICE Annual Conference (SICE) · doi:10.23919/sicefes71276.2026.11739191
- Authors
- Shafiul Hasan, S. M., Siddiquee, M. R., Atri, R., Ramon, R., Marquez, J. S., & Bai, O.
- Affiliations
- Florida International University (US)
- Venue
- Journal of NeuroEngineering and Rehabilitation
- Published
- 2020-04-16
- Volume
- 17
- Issue
- 1
- Pages
- 50-50
- ISSN
- 1743-0003 (ISSN-L)
- PMC
- PMC7164221
- OpenAlex
- W3016642122
- Cited by
- 63
- References
- 67
- Citations by year
- 2026: 7 · 2025: 8 · 2024: 12 · 2023: 10 · 2022: 14 · 2021: 7 · 2020: 5
- FWCI
- 4.84
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Journal of NeuroEngineering and Rehabilitation (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Science Foundation (1552163)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Physical Sciences › Engineering › Biomedical Engineering › Muscle activation and electromyography studies
- Health Sciences › Medicine › Rehabilitation › Stroke Rehabilitation and Recovery
- Keywords
- brain-computer interface, neurorehabilitation, amputees, discrete wavelet transform, Hjorth parameters, feature selection, support vector machine, alpha and beta bands, sensorimotor cortex, prosthetic control
- MeSH terms
- Adult, Amputees, Electroencephalography, Feasibility Studies, Female, Humans, Male, Middle Aged, Movement, Signal Processing, Computer-Assisted, Somatosensory Cortex, Intention, Young Adult, Support Vector Machine, Brain-Computer Interfaces
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
