ArticleFrontiers in neuroscience2018
An Approach for Brain-Controlled Prostheses Based on a Facial Expression Paradigm.
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 10 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed.
- Near-Field Radio-Frequency for Real-Time Facial Expression Recognition.IEEE access : practical innovations, open solutions · 2026Article
- Methods for motion artifact reduction in online brain-computer interface experiments: a systematic review.Frontiers in human neuroscience · 2023Review
- A Comprehensive Review of Endogenous EEG-Based BCIs for Dynamic Device Control.Sensors (Basel, Switzerland) · 2022Review
- Electroencephalogram and surface electromyogram fusion-based precise detection of lower limb voluntary movement using convolution neural network-long short-term memory model.Frontiers in neuroscience · 2022Article
- An asynchronous artifact-enhanced electroencephalogram based control paradigm assisted by slight facial expression.Frontiers in neuroscience · 2022Article
- Feasibility study of personalized speed adaptation method based on mental state for teleoperated robots.Frontiers in neuroscience · 2022Article
- A novel EEG decoding method for a facial-expression-based BCI system using the combined convolutional neural network and genetic algorithm.Frontiers in neuroscience · 2022Article
- Homology Characteristics of EEG and EMG for Lower Limb Voluntary Movement Intention.Frontiers in neurorobotics · 2021Article
- Realizing the Application of EEG Modeling in BCI Classification: Based on a Conditional GAN Converter.Frontiers in neuroscience · 2021Article
- Novel approach for electromyography-controlled prostheses based on facial action.Medical & biological engineering & computing · 2020Article
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
One of the most exciting areas of rehabilitation research is brain-controlled prostheses, which translate electroencephalography (EEG) signals into control commands that operate prostheses. However, the existing brain-control methods have an obstacle between the selection of brain computer interface (BCI) and its performance. In this paper, a novel BCI system based on a facial expression paradigm is proposed to control prostheses that uses the characteristics of theta and alpha rhythms of the prefrontal and motor cortices. A portable brain-controlled prosthesis system was constructed to validate the feasibility of the facial-expression-based BCI (FE-BCI) system. Four types of facial expressions were used in this study. An effective filtering algorithm based on noise-assisted multivariate empirical mode decomposition (NA-MEMD) and sample entropy (SampEn) was used to remove electromyography (EMG) artifacts. A wavelet transform (WT) was applied to calculate the feature set, and a back propagation neural network (BPNN) was employed as a classifier. To prove the effectiveness of the FE-BCI system for prosthesis control, 18 subjects were involved in both offline and online experiments. The grand average accuracy over 18 subjects was 81.31 ± 5.82% during the online experiment. The experimental results indicated that the proposed FE-BCI system achieved good performance and can be efficiently applied for prosthesis control.
Indexed as
Identifiers
What OpenQuestion holds
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.