ArticleFrontiers in neuroscience2018
Rapid Decoding of Hand Gestures in Electrocorticography Using Recurrent Neural Networks.
Article in Frontiers in neuroscience, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it, 63 citations in OpenAlex.
- Decoding Intracranial EEG With Machine Learning: A Systematic Review.Frontiers in human neuroscience · 2022Pooled it
- The Latest Research Progress on Bionic Artificial Hands: A Systematic Review.Micromachines · 2024Review
- End-to-end deep learning approach to mouse behavior classification from cortex-wide calcium imaging.PLoS computational biology · 2024Article
- Boosting brain-computer interfaces with functional electrical stimulation: potential applications in people with locked-in syndrome.Journal of neuroengineering and rehabilitation · 2023Review
- Towards clinical application of implantable brain-computer interfaces for people with late-stage ALS: medical and ethical considerations.Journal of neurology · 2023Review
- Editorial: The intersection of artificial intelligence and brain for high-performance neuroprosthesis and cyborg systems.Frontiers in neuroscience · 2023Article
- Impact of dataset size and long-term ECoG-based BCI usage on deep learning decoders performance.Frontiers in human neuroscience · 2023Article
- Phase-amplitude coupling-based adaptive filters for neural signal decoding.Frontiers in neuroscience · 2023Article
- Characterization of High-Gamma Activity in Electrocorticographic Signals.Frontiers in neuroscience · 2023Article
- Odor Recognition with a Spiking Neural Network for Bioelectronic Nose.Sensors (Basel, Switzerland) · 2019Article
- Time-Variant Linear Discriminant Analysis Improves Hand Gesture and Finger Movement Decoding for Invasive Brain-Computer Interfaces.Frontiers in neuroscience · 2019Article
- Decoding Movement From Electrocorticographic Activity: A Review.Frontiers in neuroinformatics · 2019Review
Corrections and comments
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Authors and funding
8 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Brain-computer interface (BCI) is a direct communication pathway between brain and external devices, and BCI-based prosthetic devices are promising to provide new rehabilitation options for people with motor disabilities. Electrocorticography (ECoG) signals contain rich information correlated with motor activities, and have great potential in hand gesture decoding. However, most existing decoders use long time windows, thus ignore the temporal dynamics within the period. In this study, we propose to use recurrent neural networks (RNNs) to exploit the temporal information in ECoG signals for robust hand gesture decoding. With RNN's high nonlinearity modeling ability, our method can effectively capture the temporal information in ECoG time series for robust gesture recognition. In the experiments, we decode three hand gestures using ECoG signals of two participants, and achieve an accuracy of 90%. Specially, we investigate the possibility of recognizing the gestures in a time interval as short as possible after motion onsets. Our method rapidly recognizes gestures within 0.5 s after motion onsets with an accuracy of about 80%. Experimental results also indicate that the temporal dynamics is especially informative for effective and rapid decoding of hand gestures.
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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.