ArticlePloS one2019
World's fastest brain-computer interface: Combining EEG2Code with deep learning.
Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Towards neuromorphic neurotechnologies: integrating brain-inspired computing with brain-computer interfaces.npj biomedical innovations · 2026Review
- Towards Predictive Communication: The Fusion of Large Language Models and Brain-Computer Interface.Sensors (Basel, Switzerland) · 2025Review
- Human-centred physical neuromorphics with visual brain-computer interfaces.Nature communications · 2024Article
- Blink-To-Live eye-based communication system for users with speech impairments.Scientific reports · 2023Article
- BCI-Utility Metric for Asynchronous P300 Brain-Computer Interface Systems.IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society · 2023Article
- Improving user experience of SSVEP BCI through low amplitude depth and high frequency stimuli design.Scientific reports · 2022Article
- Dual Passive Reactive Brain-Computer Interface: A Novel Approach to Human-Machine Symbiosis.Frontiers in neuroergonomics · 2022Article
- Benefits of deep learning classification of continuous noninvasive brain-computer interface control.Journal of neural engineering · 2021Article
- Prevalence, antimicrobial resistance, and genotyping of Shiga toxin-producing Escherichia coli in foods of cattle origin, diarrheic cattle, and diarrheic humans in Egypt.Gut pathogens · 2021Article
- Artificial Intelligence Algorithms in Visual Evoked Potential-Based Brain-Computer Interfaces for Motor Rehabilitation Applications: Systematic Review and Future Directions.Frontiers in human neuroscience · 2021Review
- Progress in Brain Computer Interface: Challenges and Opportunities.Frontiers in systems neuroscience · 2021Review
- Automatic seizure detection based on imaged-EEG signals through fully convolutional networks.Scientific reports · 2020Article
- Performance Improvement of Near-Infrared Spectroscopy-Based Brain-Computer Interface Using Regularized Linear Discriminant Analysis Ensemble Classifier Based on Bootstrap Aggregating.Frontiers in neuroscience · 2020Article
- Passive Brain-Computer Interfaces for Enhanced Human-Robot Interaction.Frontiers in robotics and AI · 2020Review
- Brain Computer Interfaces for Improving the Quality of Life of Older Adults and Elderly Patients.Frontiers in neuroscience · 2020Review
- Enhanced Accuracy for Multiclass Mental Workload Detection Using Long Short-Term Memory for Brain-Computer Interface.Frontiers in neuroscience · 2020Article
- Neurophysiological screening of individual variability for robust decoding in c-VEP-based BCI.Imaging neuroscience (Cambridge, Mass.)Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We present a novel approach based on deep learning for decoding sensory information from non-invasively recorded Electroencephalograms (EEG). It can either be used in a passive Brain-Computer Interface (BCI) to predict properties of a visual stimulus the person is viewing, or it can be used to actively control a BCI application. Both scenarios were tested, whereby an average information transfer rate (ITR) of 701 bit/min was achieved for the passive BCI approach with the best subject achieving an online ITR of 1237 bit/min. Further, it allowed the discrimination of 500,000 different visual stimuli based on only 2 seconds of EEG data with an accuracy of up to 100%. When using the method for an asynchronous self-paced BCI for spelling, an average utility rate of 175 bit/min was achieved, which corresponds to an average of 35 error-free letters per minute. As the presented method extracts more than three times more information than the previously fastest approach, we suggest that EEG signals carry more information than generally assumed. Finally, we observed a ceiling effect such that information content in the EEG exceeds that required for BCI control, and therefore we discuss if BCI research has reached a point where the performance of non-invasive visual BCI control cannot be substantially improved anymore.
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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.