ArticleJournal of neural engineering2014
Intention estimation in brain-machine interfaces.
Article in Journal of neural engineering, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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Who cites it
30 citing papers in PubMed.
- Speech motor cortex enables BCI cursor control and click.Journal of neural engineering · 2025Trial
- Feedback control policies employed by people using intracortical brain-computer interfaces.Journal of neural engineering · 2017Trial
- Article
- Enhanced control of a brain-computer interface by tetraplegic participants via neural-network-mediated feature extraction.Nature biomedical engineering · 2025Article
- Speech motor cortex enables BCI cursor control and click.bioRxiv : the preprint server for biology · 2024Article
- Article
- Aligning latent representations of neural activity.Nature biomedical engineering · 2023Article
- The science and engineering behind sensitized brain-controlled bionic hands.Physiological reviews · 2022Review
- An artificial intelligence that increases simulated brain-computer interface performance.Journal of neural engineering · 2021Article
- Shifts in Estimated Preferred Directions During Simulated BMI Experiments With No Adaptation.Frontiers in systems neuroscience · 2021Article
- Principled BCI Decoder Design and Parameter Selection Using a Feedback Control Model.Scientific reports · 2019Article
- Latent Factors and Dynamics in Motor Cortex and Their Application to Brain-Machine Interfaces.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2018Review
- Inferring single-trial neural population dynamics using sequential auto-encoders.Nature methods · 2018Article
- A Comparison of Intention Estimation Methods for Decoder Calibration in Intracortical Brain-Computer Interfaces.IEEE transactions on bio-medical engineering · 2018Article
- Cortical Decoding of Individual Finger Group Motions Using ReFIT Kalman Filter.Frontiers in neuroscience · 2018Article
- Augmenting intracortical brain-machine interface with neurally driven error detectors.Journal of neural engineering · 2017Article
- Leveraging neural dynamics to extend functional lifetime of brain-machine interfaces.Scientific reports · 2017Article
- Signal-independent noise in intracortical brain-computer interfaces causes movement time properties inconsistent with Fitts' law.Journal of neural engineering · 2017Article
- A Non-Human Primate Brain-Computer Typing Interface.Proceedings of the IEEE. Institute of Electrical and Electronics Engineers · 2017Article
- Making brain-machine interfaces robust to future neural variability.Nature communications · 2016Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
objectiveThe objective of this work was to quantitatively investigate the mechanisms underlying the performance gains of the recently reported 'recalibrated feedback intention-trained Kalman Filter' (ReFIT-KF). APPROACH: This was accomplished by designing variants of the ReFIT-KF algorithm and evaluating training and online data to understand the neural basis of this improvement. We focused on assessing the contribution of two training set innovations of the ReFIT-KF algorithm: intention estimation and the two-stage training paradigm. MAIN
resultsWithin the two-stage training paradigm, we found that intention estimation independently increased target acquisition rates by 37% and 59%, respectively, across two monkeys implanted with multiunit intracortical arrays. Intention estimation improved performance by enhancing the tuning properties and the mutual information between the kinematic and neural training data. Furthermore, intention estimation led to fewer shifts in channel tuning between the training set and online control, suggesting that less adaptation was required during online control. Retraining the decoder with online BMI training data also reduced shifts in tuning, suggesting a benefit of training a decoder in the same behavioral context; however, retraining also led to slower online decode velocities. Finally, we demonstrated that one- and two-stage training paradigms performed comparably when intention estimation is applied. SIGNIFICANCE: These findings highlight the utility of intention estimation in reducing the need for adaptive strategies and improving the online performance of BMIs, helping to guide future BMI design decisions.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.