Detection and classification of movement-related cortical potentials associated with task force and speed
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OBJECTIVE: In this study, the objective was to detect movement intentions and extract different levels of force and speed of the intended movement from scalp electroencephalography (EEG). We then estimated the performance of the closed loop system. APPROACH: Cued movements were detected from continuous EEG recordings using a template of the initial phase of the movement-related cortical potential in 12 healthy subjects. The temporal features, extracted from the movement intention, were classified with an optimized support vector machine. The system performance was evaluated when combining detection with classification. MAIN RESULTS: The system detected 81% of the movements and correctly classified 75 ± 9% and 80 ± 10% of these at the point of detection when varying the force and speed, respectively. When the detector was combined with the classifier, the system detected and correctly classified 64 ± 13% and 67 ± 13% of these movements. The system detected and incorrectly classified 21 ± 7% and 16 ± 9% of the movements. The movements were detected 317 ± 73 ms before the movement onset. SIGNIFICANCE: The results indicate that it is possible to detect movement intentions with limited latencies, and extract and classify different levels of force and speed, which may be combined with assistive technologies for patient-driven neurorehabilitation.
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Common Spatial Pattern (CSP) performs spatial filtering maximizing inter-class variance differences, enhances EEG signal separability, and is used for EEG motor imagery classification.
“| Common Spatial | Spatial filtering maximizing inter-class | Enhance EEG signal separability | EEG motor imagery classification 139,260 |”
From Recent advances in intelligent wearable systems: from multiscale biomechanical features towards human motion intent prediction (2026) · cites this paper as [139]
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- Authors
- Jochumsen, M., Niazi, I. K., Mrachacz-Kersting, N., Farina, D., & Dremstrup, K.
- Affiliations
- Aalborg University (DK)
- Bernstein Center for Computational Neuroscience Göttingen (DE)
- Corresponding author
- Jochumsen, Mads
- Niazi, Imran Khan
- Farina, Dario
- Dremstrup, Kim
- Venue
- Journal of Neural Engineering
- Publisher
- IOP Publishing
- Published
- 2013-08-28
- Volume
- 10
- Issue
- 5
- Pages
- 056015
- ISSN
- 1741-2552 (ISSN-L)
- 1741-2560
- OpenAlex
- W2087711998
- Cited by
- 128
- References
- 39
- Citations by year
- 2026: 6 · 2025: 5 · 2024: 14 · 2023: 7 · 2022: 8 · 2021: 14 · 2020: 11 · 2019: 11 · 2018: 6 · 2017: 11 · 2016: 8 · 2015: 15 · 2014: 11 · 2013: 1
- FWCI
- 6.40
- Type
- journal article
- Language
- en
- Open access
- green
- vbn.aau.dk
- Licence
- Locations
- Journal of Neural Engineering (publishedVersion)
- PubMed (publishedVersion)
- FreiDok plus (Universitätsbibliothek Freiburg) (submittedVersion)
- GRO (submittedVersion)
- VBN Forskningsportal (Aalborg Universitet) (submittedVersion, cc-by-nc-nd)
- Funders
- Teknologi og Produktion, Det Frie Forskningsråd
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Physical Sciences › Engineering › Biomedical Engineering › Muscle activation and electromyography studies
- Life Sciences › Neuroscience › Cognitive Neuroscience › Free Will and Agency
- Keywords
- movement-related cortical potentials, motion intention recognition, EEG-based brain-computer interface, support vector machine, closed-loop systems, assistive technology, neurorehabilitation
- MeSH terms
- Adult, Analysis of Variance, Cerebral Cortex, Cues, Electroencephalography, Electrooculography, Female, Humans, Male, Movement, Muscle Contraction, Perception, ROC Curve, Signal Processing, Computer-Assisted, Reproducibility of Results, Young Adult, Support Vector Machine
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