ArticleSensors (Basel, Switzerland)2020
Reinforcement Learning Based Fast Self-Recalibrating Decoder for Intracortical Brain-Machine Interface.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
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Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The multiple uses of artificial intelligence in exercise programs: a narrative review.Frontiers in public health · 2025Pooled it
- A new approach for neural decoding by inspiring of hyperdimensional computing for implantable intra-cortical BMIs.Scientific reports · 2024Article
- Neural Decoders Using Reinforcement Learning in Brain Machine Interfaces: A Technical Review.Frontiers in systems neuroscience · 2022Review
- Selection of Essential Neural Activity Timesteps for Intracortical Brain-Computer Interface Based on Recurrent Neural Network.Sensors (Basel, Switzerland) · 2021Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
backgroundFor the nonstationarity of neural recordings in intracortical brain-machine interfaces, daily retraining in a supervised manner is always required to maintain the performance of the decoder. This problem can be improved by using a reinforcement learning (RL) based self-recalibrating decoder. However, quickly exploring new knowledge while maintaining a good performance remains a challenge in RL-based decoders.
methodsTo solve this problem, we proposed an attention-gated RL-based algorithm combining transfer learning, mini-batch, and weight updating schemes to accelerate the weight updating and avoid over-fitting. The proposed algorithm was tested on intracortical neural data recorded from two monkeys to decode their reaching positions and grasping gestures.
resultsThe decoding results showed that our proposed algorithm achieved an approximate 20% increase in classification accuracy compared to that obtained by the non-retrained classifier and even achieved better classification accuracy than the daily retraining classifier. Moreover, compared with a conventional RL method, our algorithm improved the accuracy by approximately 10% and the online weight updating speed by approximately 70 times.
conclusionsThis paper proposed a self-recalibrating decoder which achieved a good and robust decoding performance with fast weight updating and might facilitate its application in wearable device and clinical practice.
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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.