ArticleAnnals of neurology2020
Classification of Facial Expressions for Intended Display of Emotions Using Brain-Computer Interfaces.
Article in Annals of neurology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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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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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.
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Who cites it
6 citing papers in PubMed, 10 citations in OpenAlex.
- Implications of shared motor and perceptual activations on the sensorimotor cortex for neuroprosthetic decoding.Journal of neural engineering · 2025Article
- A preliminary study of steady-state visually-evoked potential-based non-invasive brain-computer interface technology as a communication aid for patients with amyotrophic lateral sclerosis.Quantitative imaging in medicine and surgery · 2025Article
- The speech neuroprosthesis.Nature reviews. Neuroscience · 2024Review
- Boosting brain-computer interfaces with functional electrical stimulation: potential applications in people with locked-in syndrome.Journal of neuroengineering and rehabilitation · 2023Review
- Article
- Functional MRI based simulations of ECoG grid configurations for optimal measurement of spatially distributed hand-gesture information.Journal of neural engineering · 2021Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Facial expressions are important for intentional display of emotions in social interaction. For people with severe paralysis, the ability to display emotions intentionally can be impaired. Current brain-computer interfaces (BCIs) allow for linguistic communication but are cumbersome for expressing emotions. Here, we investigated the feasibility of a BCI to display emotions by decoding facial expressions. We used electrocorticographic recordings from the sensorimotor cortex of people with refractory epilepsy and classified five facial expressions, based on neural activity. The mean classification accuracy was 72%. This approach could be a promising avenue for development of BCI-based solutions for fast communication of emotions. ANN NEUROL 2020;88:631-636.
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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.