ReviewFrontiers in neurorobotics2017
Hybrid Brain-Computer Interface Techniques for Improved Classification Accuracy and Increased Number of Commands: A Review.
Review in Frontiers in neurorobotics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 98 papers, 2 of them syntheses that pooled it.
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Who cites it
98 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Use of Artificial Intelligence in the Classification of Upper-Limb Motion Using EEG and EMG Signals: A Review.Sensors (Basel, Switzerland) · 2026Pooled it
- Combining EEG and eye-tracking for cognitive and physiological states monitoring: a systematic review.Frontiers in neuroergonomics · 2025Pooled it
- Rethinking Brain-Computer Interfaces for Soft Robotic Systems: A Unified Framework and Perspective.Sensors (Basel, Switzerland) · 2026Article
- Emotion recognition using spectral-spatial attention multi-temporal scale network: EEG study.Brain informatics · 2026Article
- Comparison of Point-and-Click Performance Between the Brainfingers BCI and the Mouse.Sensors (Basel, Switzerland) · 2026Article
- A Cheonjiin Layout Mental Speller: Developing a Simple and Cost-Effective EEG-Based Brain-Computer Interface System.Sensors (Basel, Switzerland) · 2026Article
- A Cloud-Aware Scalable Architecture for Distributed Edge-Enabled BCI Biosensor System.Biosensors · 2026Article
- Bridging cognition and control through passive eye movement integration in motor imagery brain-computer interfaces.Frontiers in human neuroscience · 2026Review
- Co-localized optode-electrode design for multimodal functional near infrared spectroscopy and electroencephalography.Neurophotonics · 2025Article
- Response coupling with an auxiliary neural signal for enhancing brain signal detection.Scientific reports · 2025Article
- A hybrid CNN model for classification of motor tasks obtained from hybrid BCI system.Scientific reports · 2025Article
- Filter bank common spatial pattern and envelope-based features in multimodal EEG-fTCD brain-computer interfaces.PloS one · 2025Article
- A novel scrolling text reading paradigm for improving the performance of multiclass and hybrid brain computer interface systems.PloS one · 2025Article
- A Comprehensive Survey of Brain-Computer Interface Technology in Health care: Research Perspectives.Journal of medical signals and sensors · 2025Review
- Simultaneous Mental Fatigue and Mental Workload Assessment With Wearable High-Density Diffuse Optical Tomography.IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society · 2025Article
- Hybrid Functional Near-Infrared Spectroscopy System and Electromyography for Prosthetic Knee Control.Biosensors · 2024Article
- A Brain Network Analysis Model for Motion Sickness in Electric Vehicles Based on EEG and fNIRS Signal Fusion.Sensors (Basel, Switzerland) · 2024Article
- Low frequency oscillations reflect neurovascular coupling and disappear after cerebral death.Scientific reports · 2024Article
- Article
- Evaluating the Feasibility of Visual Imagery for an EEG-Based Brain-Computer Interface.IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society · 2024Article
38 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In this article, non-invasive hybrid brain-computer interface (hBCI) technologies for improving classification accuracy and increasing the number of commands are reviewed. Hybridization combining more than two modalities is a new trend in brain imaging and prosthesis control. Electroencephalography (EEG), due to its easy use and fast temporal resolution, is most widely utilized in combination with other brain/non-brain signal acquisition modalities, for instance, functional near infrared spectroscopy (fNIRS), electromyography (EMG), electrooculography (EOG), and eye tracker. Three main purposes of hybridization are to increase the number of control commands, improve classification accuracy and reduce the signal detection time. Currently, such combinations of EEG + fNIRS and EEG + EOG are most commonly employed. Four principal components (i.e., hardware, paradigm, classifiers, and features) relevant to accuracy improvement are discussed. In the case of brain signals, motor imagination/movement tasks are combined with cognitive tasks to increase active brain-computer interface (BCI) accuracy. Active and reactive tasks sometimes are combined: motor imagination with steady-state evoked visual potentials (SSVEP) and motor imagination with P300. In the case of reactive tasks, SSVEP is most widely combined with P300 to increase the number of commands. Passive BCIs, however, are rare. After discussing the hardware and strategies involved in the development of hBCI, the second part examines the approaches used to increase the number of control commands and to enhance classification accuracy. The future prospects and the extension of hBCI in real-time applications for daily life scenarios are provided.
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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.