ReviewComputational intelligence and neuroscience2017
Progress in EEG-Based Brain Robot Interaction Systems.
Review in Computational intelligence and neuroscience, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 2 of them syntheses that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
12 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- A Systematic Review of Techniques for Artifact Detection and Artifact Category Identification in Electroencephalography from Wearable Devices.Sensors (Basel, Switzerland) · 2025Pooled it
- Past, Present, and Future of EEG-Based BCI Applications.Sensors (Basel, Switzerland) · 2022Pooled it
- Levels of shared autonomy in brain-robot interfaces: enabling multi-robot multi-human collaboration for activities of daily living.Frontiers in human neuroscience · 2025Article
- Deciphering Post-Stroke Sleep Disorders: Unveiling Neurological Mechanisms in the Realm of Brain Science.Brain sciences · 2024Review
- Application of Neuroengineering Based on EEG Features in the Industrial Design of Comfort.Computational intelligence and neuroscience · 2022Article
- Controlling an effector with eye movements: The effect of entangled sensory and motor responsibilities.PloS one · 2022Article
- Decoding EEG rhythms offline and online during motor imagery for standing and sitting based on a brain-computer interface.Frontiers in neuroinformatics · 2022Article
- A Novel Time-Incremental End-to-End Shared Neural Network with Attention-Based Feature Fusion for Multiclass Motor Imagery Recognition.Computational intelligence and neuroscience · 2021Article
- Optimized Complex Network Method (OCNM) for Improving Accuracy of Measuring Human Attention in Single-Electrode Neurofeedback System.Computational intelligence and neuroscience · 2019Article
- Parallel Computing Sparse Wavelet Feature Extraction for P300 Speller BCI.Computational and mathematical methods in medicine · 2018Article
- Recurrent Transformation of Prior Knowledge Based Model for Human Motion Recognition.Computational intelligence and neuroscience · 2018Article
- Object Extraction in Cluttered Environments via a P300-Based IFCE.Computational intelligence and neuroscience · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The most popular noninvasive Brain Robot Interaction (BRI) technology uses the electroencephalogram- (EEG-) based Brain Computer Interface (BCI), to serve as an additional communication channel, for robot control via brainwaves. This technology is promising for elderly or disabled patient assistance with daily life. The key issue of a BRI system is to identify human mental activities, by decoding brainwaves, acquired with an EEG device. Compared with other BCI applications, such as word speller, the development of these applications may be more challenging since control of robot systems via brainwaves must consider surrounding environment feedback in real-time, robot mechanical kinematics, and dynamics, as well as robot control architecture and behavior. This article reviews the major techniques needed for developing BRI systems. In this review article, we first briefly introduce the background and development of mind-controlled robot technologies. Second, we discuss the EEG-based brain signal models with respect to generating principles, evoking mechanisms, and experimental paradigms. Subsequently, we review in detail commonly used methods for decoding brain signals, namely, preprocessing, feature extraction, and feature classification, and summarize several typical application examples. Next, we describe a few BRI applications, including wheelchairs, manipulators, drones, and humanoid robots with respect to synchronous and asynchronous BCI-based techniques. Finally, we address some existing problems and challenges with future BRI techniques.
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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.