Detecting the Speed Change Intention from EEG Signals: From the Offline and Pseudo-Online Analysis to an Online Closed-Loop Validation
Open accessAbstract
Control of assistive devices by voluntary user intention is an underdeveloped topic in the Brain–Machine Interfaces (BMI) literature. In this work, a preliminary real-time BMI for the speed control of an exoskeleton is presented. First, an offline analysis for the selection of the intention patterns based on the optimum features and electrodes is proposed. This is carried out comparing three different classification models: monotonous walk vs. increasing and decreasing change speed intentions, monotonous walk vs. only increasing intention, and monotonous walk vs. only decreasing intention. The results indicate that, among the features tested, the most suitable parameter to represent these models are the Hjorth statistics in alpha and beta frequency bands. The average offline classification accuracy for the offline cross-validation of the three models obtained is 68 ± 11%. This selection is also tested following a pseudo-online analysis, simulating a real-time detection of the subject’s intentions to change speed. The average results indices of the three models during this pseudoanalysis are of a 42% true positive ratio and a false positive rate per minute of 9. Finally, in order to check the viability of the approach with an exoskeleton, a case of study is presented. During the experimental session, the pros and cons of the implementation of a closed-loop control of speed change for the H3 exoskeleton through EEG analysis are commented.
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Hjorth parameter quantifies temporal complexity and stability, is computationally simple and suitable for real-time systems, and is used for IMU-based gait stability assessment.
“| Hjorth parameter | Quantify temporal complexity and stability | Computationally simple, suitable for | IMU-based gait stability assessment 137,261 |”
From Recent advances in intelligent wearable systems: from multiscale biomechanical features towards human motion intent prediction (2026) · cites this paper as [261]
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Cites
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
Harnessing neuroplasticity for clinical applications
Cramer, S. C., Sur, M., Dobkin, B. H., O'Brien, C., Sanger, T. D., Trojanowski, J. Q., Rumsey, J. M., Hicks, R., Cameron, J., Chen, D., et al. (2011) · Brain · doi:10.1093/brain/awr039 · Cited by 1,259 · see citing works
Better than random: A closer look on BCI results
Mueller-Putz, G., Scherer, R., Brunner, C., Leeb, R., & Pfurtscheller, G. (2008) · Infoscience (Ecole Polytechnique Fédérale de Lausanne) · Cited by 419 · see citing works
Temporal Characterization of the Neural Correlates of Perceptual Decision Making in the Human Brain
Philiastides, M. G., & Sajda, P. (2005) · Cerebral Cortex · doi:10.1093/cercor/bhi130 · Cited by 360 · 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
High accuracy decoding of user intentions using EEG to control a lower-body exoskeleton
Kilicarslan, A., Prasad, S., Grossman, R. G., & Contreras-Vidal, J. L. (2013) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2013.6610821 · Cited by 196 · see citing works
Singh, A. S. G., Hussain, A. A., Lal, S. P., & Guesgen, H. W. (2021) · Sensors · doi:10.3390/s21062173 · Cited by 195 · see citing works
A robust adaptive denoising framework for real-time artifact removal in scalp EEG measurements
Kilicarslan, A., Grossman, R. G., & Contreras-Vidal, J. L. (2016) · Journal of Neural Engineering · doi:10.1088/1741-2560/13/2/026013 · Cited by 185 · see citing works
A brain-controlled exoskeleton with cascaded event-related desynchronization classifiers
Lee, K., Liu, D., Perroud, L., Chavarriaga, R., & Millán, J. D. R. (2016) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2016.10.005 · Cited by 136 · see citing works
López-Larraz, E., Trincado-Alonso, F., Rajasekaran, V., Pérez-Nombela, S., del-Ama, A., Aranda, J., Minguez, J., Gil-Agudo, Á., & Montesano, L. (2016) · Frontiers in Neuroscience · doi:10.3389/fnins.2016.00359 · Cited by 123 · 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
EEG Single-Trial Detection of Gait Speed Changes during Treadmill Walk
Lisi, G., & Morimoto, J. (2015) · PLoS ONE · doi:10.1371/journal.pone.0125479 · Cited by 64 · see citing works
Prediction of gait intention from pre-movement EEG signals: a feasibility study
Shafiul Hasan, S. M., Siddiquee, M. R., Atri, R., Ramon, R., Marquez, J. S., & Bai, O. (2020) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/s12984-020-00675-5 · Cited by 63 · see citing works
Zhang, Y., Prasad, S., Kilicarslan, A., & Contreras-Vidal, J. L. (2017) · Frontiers in Neuroscience · doi:10.3389/fnins.2017.00170 · Cited by 58 · 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
Self-citationPersonalized Offline and Pseudo-Online BCI Models to Detect Pedaling Intent
Rodríguez-Ugarte, M., Iáñez, E., Ortíz, M., & Azorín, J. M. (2017) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2017.00045 · Cited by 44 · see citing works
Self-citationImproving Real-Time Lower Limb Motor Imagery Detection Using tDCS and an Exoskeleton
Rodríguez-Ugarte, M., Iáñez, E., Ortíz, M., & Azorín, J. M. (2018) · Frontiers in Neuroscience · doi:10.3389/fnins.2018.00757 · Cited by 28 · see citing works
Self-citationOrtíz, M., Iáñez, E., Contreras-Vidal, J., & Azorín, J. (2020) · Frontiers in Neurorobotics · doi:10.3389/fnbot.2020.00048 · Cited by 28 · see citing works
Self-citationApplication 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
Self-citationAsynchronous 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
Real-time Decoding of EEG Gait Intention for Controlling a Lower-limb Exoskeleton System
Choi, J., & Kim, H. (2019) · International Winter Conference on Brain-Computer Interface (BCI) · doi:10.1109/iww-bci.2019.8737311 · Cited by 18 · 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
Spatio-temporal Comparison between ERD/ERS and MRCP-based Movement Prediction
Seeland, A., Manca, L., Kirchner, F., & Kirchner, E. (2015) · International Conference on Bio-inspired Systems and Signal Processing · doi:10.5220/0005214002190226 · Cited by 15 · see citing works
Kong, W., Lin, J., Waaning, L., Sessa, S., Cosentino, S., Magistro, D., Zecca, M., Kawashima, R., & Takanishi, A. (2016) · IEEE International Conference on Robotics and Biomimetics (ROBIO) · doi:10.1109/robio.2016.7866633 · Cited by 14 · see citing works
Luft, A. R. (2013) · Monographs in clinical neuroscience/Frontiers of neurology and neuroscience/Monographs in neural sciences · doi:10.1159/000348879 · Cited by 11 · see citing works
Quantitative measure of complexity of the dynamic event-related EEG data
Pei, X.-M., Zheng, C., Wei-xing, H., & Xu, J. (2006) · Neurocomputing · doi:10.1016/j.neucom.2006.02.011 · Cited by 10 · see citing works
Assessing Footwear Comfort by Electroencephalography Analysis
Ortíz, M., Vicente, P., Ianez, E., Montiel, E., & Azorín, J. M. (2021) · IEEE Access · doi:10.1109/access.2021.3115179 · Cited by 7 · see citing works
Self-citationSelection of Spatial, Temporal and Frequency Features to Detect Direction Changes During Gait
Soriano-Segura, P., Iáñez, E., Quiles, V., Ferrero, L., Ortíz, M., & Azorín, J. M. (2020) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc44109.2020.9176164 · Cited by 3 · see citing works
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Research works printed but not linked: 1
- Cohen, M.X. A tutorial on generalized eigendecomposition for source separation in multichannel electrophysiology. arXiv 2021, arXiv:2104.12356. · arXiv
Cited by
Sorted by citations
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Sriram, S., RAJAGOPAL, K., KREJCAR, O., Frischer, R., & Namazi, H. (2023) · Fractals · doi:10.1142/s0218348x23500329 · Cited by 17 · see citing works
Chen, 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
Decoding of Turning Intention during Walking Based on EEG Biomarkers
Quiles, V., Ferrero, L., Iáñez, E., Ortíz, M., & Azorín, J. M. (2022) · Biosensors · doi:10.3390/bios12080555 · Cited by 5 · see citing works
Self-citationGonzález-Cely, A. X., Blanco-Díaz, C. F., Delisle-Rodríguez, D., & Bastos-Filho, T. (2024) · IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob) · doi:10.1109/biorob60516.2024.10719823 · Cited by 2 · see citing works
Lower-Limb Motor Imagery-Based Brain-Computer Interface to Control Treadmill Velocities
González-Cely, A. X., Soekadar, S. R., Delisle-Rodríguez, D., & Bastos-Filho, T. (2025) · International Conference on Rehabilitation Robotics · doi:10.1109/icorr66766.2025.11063181 · Cited by 2 · see citing works
Application of the "Stripe" Algorithm for Online Decoding of the EEG Patterns
Lipkovich, M., & Sagatdinov, A. (2023) · Mekhatronika Avtomatizatsiya Upravlenie · doi:10.17587/mau.24.300-306 · Cited by 1 · see citing works
Riemannian classification analysis for model EEG intention speed patterns
Quiles, V., Ferrero, L., Iáñez, E., Ortíz, M., & Azorín, J. M. (2022) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc48229.2022.9871561
Self-citationBlanco-Díaz, C. F., Guerrero-Méndez, C. D., González-Cely, A. X., Ruiz-Olaya, A. F., Delisle-Rodríguez, D., Bastos-Filho, T., & Jaramillo-Isaza, S. (2024) · Communications in computer and information science · doi:10.1007/978-3-031-63848-0_1
Blanco-Díaz, C. F., González-Cely, A. X., Delisle-Rodríguez, D., & Bastos-Filho, T. (2025) · Signals · doi:10.3390/signals6040052
The classification of walking and phases of gait using EEG: a scoping review
Ryan, C., White, C., & Lennon, O. C. (2026) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/s12984-026-02076-6
- Authors
- Quiles, V., Ferrero, L., Iáñez, E., Ortiz, M., Cano, J. M., & Azorín, J. M.
- Affiliations
- Universitat de Miguel Hernández d'Elx (ES)
- Brain-Machine Interface System Lab, Miguel Hernández University of Elche, 03202 Elche, Spain
- Universidad Politécnica de Cartagena (ES)
- Corresponding author
- Quiles, Vicente
- Venue
- Applied Sciences
- Publisher
- MDPI AG
- Published
- 2022-01-01
- Volume
- 12
- Issue
- 1
- Pages
- 415
- ISSN
- 2076-3417 (ISSN-L)
- OpenAlex
- W4206195853
- Cited by
- 10
- References
- 28
- Citations by year
- 2026: 2 · 2025: 2 · 2024: 2 · 2023: 2 · 2022: 2
- FWCI
- 1.03
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Applied Sciences (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- MCIN/AEI/ 10.13039/501100011033 (RTI2018-096677-B-I00)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Physical Sciences › Engineering › Biomedical Engineering › Muscle activation and electromyography studies
- Physical Sciences › Engineering › Biomedical Engineering › Prosthetics and Rehabilitation Robotics
- Keywords
- brain-computer interface, exoskeleton control, feedback control, assistive devices
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