Rhythmic temporal prediction enhances neural representations of movement intention for brain–computer interface
Abstract
Objective. Detecting movement intention is a typical use of brain–computer interfaces (BCI). However, as an endogenous electroencephalography (EEG) feature, the neural representation of movement is insufficient for improving motor-based BCI. This study aimed to develop a new movement augmentation BCI encoding paradigm by incorporating the cognitive function of rhythmic temporal prediction, and test the feasibility of this new paradigm in optimizing detections of movement intention. Methods. A visual-motion synchronization task was designed with two movement intentions (left vs. right) and three rhythmic temporal prediction conditions (1000 ms vs. 1500 ms vs. no temporal prediction). Behavioural and EEG data of 24 healthy participants were recorded. Event-related potentials (ERPs), event-related spectral perturbation induced by left- and right-finger movements, the common spatial pattern (CSP) and support vector machine, Riemann tangent space algorithm and logistic regression were used and compared across the three temporal prediction conditions, aiming to test the impact of temporal prediction on movement detection. Results. Behavioural results showed significantly smaller deviation time for 1000 ms and 1500 ms conditions. ERP analyses revealed 1000 ms and 1500 ms conditions led to rhythmic oscillations with a time lag in contralateral and ipsilateral areas of movement. Compared with no temporal prediction, 1000 ms condition exhibited greater beta event-related desynchronization (ERD) lateralization in motor area (P< 0.001) and larger beta ERD in frontal area (P< 0.001). 1000 ms condition achieved an averaged left–right decoding accuracy of 89.71% using CSP and 97.30% using Riemann tangent space, both significantly higher than no temporal prediction. Moreover, movement and temporal information can be decoded simultaneously, achieving 88.51% four-classification accuracy. Significance. The results not only confirm the effectiveness of rhythmic temporal prediction in enhancing detection ability of motor-based BCI, but also highlight the dual encodings of movement and temporal information within a single BCI paradigm, which is promising to expand the range of intentions that can be decoded by the BCI.
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Logistic Regression is a linear probabilistic model outputting class probabilities, has strong interpretability and probabilistic decision support, and is used for binary motion intent prediction.
“| Logistic Regression | Linear probabilistic model, outputting class | Strong interpretability, probabilistic | Binary motion intent prediction 265,266 |”
From Recent advances in intelligent wearable systems: from multiscale biomechanical features towards human motion intent prediction (2026) · cites this paper as [266]
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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
- Authors
- Meng, J., Zhao, Y., Wang, K., Sun, J., Yi, W., Xu, F., Xu, M., & Ming, D.
- Affiliations
- Tianjin University (CN)
- Qilu University of Technology (CN)
- Venue
- Journal of Neural Engineering
- Publisher
- IOP Publishing
- Published
- 2023-11-10
- Volume
- 20
- Issue
- 6
- Pages
- 066004
- ISSN
- 1741-2552 (ISSN-L)
- 1741-2560
- OpenAlex
- W4387899440
- Cited by
- 5
- References
- 54
- Citations by year
- 2026: 1 · 2025: 4
- FWCI
- 0.75
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Journal of Neural Engineering (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Key Research and Development Program of China (Grant No. 2022YFF1202300)
- National Natural Science Foundation of China (No. 62106173)
- Introduce Innovative Teams of 2021 (New High School 20 Items Project)
- General Projects of Postdoctoral Science Foundation of China (No. 2022M712364)
- 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 › Motor Control and Adaptation
- Keywords
- brain-computer interface, motion intention recognition, beta oscillations, common spatial pattern
- MeSH terms
- Electroencephalography, Evoked Potentials, Humans, Imagination, Movement, Intention, Brain-Computer Interfaces
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