On the public citation graph
Convolutional neural network, personalised, closed-loop Brain-Computer Interfaces for multi-way control mode switching in real-time
0 signed · 19 inferred · view as graph
Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1002/hbm.23730openalex-snapshot
Deep learning with convolutional neural networks for EEG decoding and visualization
inferredcites →https://doi.org/10.1016/j.apmr.2014.05.028openalex-snapshot
What Would Brain-Computer Interface Users Want: Opinions and Priorities of Potential Users With Spinal Cord Injury
inferredcites →https://doi.org/10.1016/j.clinph.2012.11.010openalex-snapshot
Long-term evaluation of a 4-class imagery-based brain–computer interface
inferredcites →https://doi.org/10.1016/j.neuroimage.2013.10.027openalex-snapshot
MNE software for processing MEG and EEG data
inferredcites →https://doi.org/10.1038/nature17435openalex-snapshot
Restoring cortical control of functional movement in a human with quadriplegia
inferredcites →https://doi.org/10.1038/s41551-016-0014openalex-snapshot
Neurocognitive barriers to the embodiment of technology
inferredcites →https://doi.org/10.1088/1741-2560/14/1/016003openalex-snapshot
A novel deep learning approach for classification of EEG motor imagery signals
inferredcites →https://doi.org/10.1109/biorob.2016.7523807openalex-snapshot
Gaze-based teleprosthetic enables intuitive continuous control of complex robot arm use: Writing & drawing
inferredcites →https://doi.org/10.1109/embc.2015.7318929openalex-snapshot
On the use of convolutional neural networks and augmented CSP features for multi-class motor imagery of EEG signals classification
inferredcites →https://doi.org/10.1109/icorr.2017.8009387openalex-snapshot
“Wink to grasp” — comparing eye, voice & EMG gesture control of grasp with soft-robotic gloves
inferredcites →https://doi.org/10.1109/icorr.2017.8009388openalex-snapshot
Towards free 3D end-point control for robotic-assisted human reaching using binocular eye tracking
inferredcites →https://doi.org/10.1109/iembs.2010.5626045openalex-snapshot
A telepresence robotic system operated with a P300-based brain-computer interface: Initial tests with ALS patients
inferredcites →https://doi.org/10.1109/tnsre.2016.2601240openalex-snapshot
A Deep Learning Scheme for Motor Imagery Classification based on Restricted Boltzmann Machines
inferredcites →https://doi.org/10.1162/neco_a_00089openalex-snapshot
Machine-Learning-Based Coadaptive Calibration for Brain-Computer Interfaces
inferredcites →https://doi.org/10.3389/fnhum.2015.00308openalex-snapshot
Effects of user mental state on EEG-BCI performance
inferredcites →https://doi.org/10.3389/fnins.2013.00267openalex-snapshot
MEG and EEG data analysis with MNE-Python
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1101/282848openalex-snapshot
Deep Learning Based BCI Control of a Robotic Service Assistant Using Intelligent Goal Formulation
inferred← citeshttps://doi.org/10.3389/fnins.2018.00843openalex-snapshot
A New Frontier: The Convergence of Nanotechnology, Brain Machine Interfaces, and Artificial Intelligence
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