ArticleScientific reports2019
Optimising non-invasive brain-computer interface systems for free communication between naïve human participants.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Deep learning-enhanced anti-noise triboelectric acoustic sensor for human-machine collaboration in noisy environments.Nature communications · 2025Article
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- A spatial-temporal linear feature learning algorithm for P300-based brain-computer interfaces.Heliyon · 2023Article
- Decoding of the neural representation of the visual RGB color model.PeerJ. Computer science · 2023Article
- A comprehensive review on motion trajectory reconstruction for EEG-based brain-computer interface.Frontiers in neuroscience · 2023Review
- Article
- Optimising the classification of feature-based attention in frequency-tagged electroencephalography data.Scientific data · 2022Article
- cVEP Training Data Validation-Towards Optimal Training Set Composition from Multi-Day Data.Brain sciences · 2022Article
- Implicit Neurofeedback Training of Feature-Based Attention Promotes Biased Sensory Processing during Integrative Decision-Making.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2021Article
- Joint control of visually guided actions involves concordant increases in behavioural and neural coupling.Communications biology · 2021Article
- Asynchronous c-VEP communication tools-efficiency comparison of low-target, multi-target and dictionary-assisted BCI spellers.Scientific reports · 2020Article
- Brain Computer Interfaces for Improving the Quality of Life of Older Adults and Elderly Patients.Frontiers in neuroscience · 2020Review
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Authors and funding
3 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Free communication is one of the cornerstones of modern civilisation. While manual keyboards currently allow us to interface with computers and manifest our thoughts, a next frontier is communication without manual input. Brain-computer interface (BCI) spellers often achieve this by decoding patterns of neural activity as users attend to flickering keyboard displays. To date, the highest performing spellers report typing rates of ~10.00 words/minute. While impressive, these rates are typically calculated for experienced users repetitively typing single phrases. It is therefore not clear whether naïve users are able to achieve such high rates with the added cognitive load of genuine free communication, which involves continuously generating and spelling novel words and phrases. In two experiments, we developed an open-source, high-performance, non-invasive BCI speller and examined its feasibility for free communication. The BCI speller required users to focus their visual attention on a flickering keyboard display, thereby producing unique cortical activity patterns for each key, which were decoded using filter-bank canonical correlation analysis. In Experiment 1, we tested whether seventeen naïve users could maintain rapid typing during prompted free word association. We found that information transfer rates were indeed slower during this free communication task than during typing of a cued character sequence. In Experiment 2, we further evaluated the speller's efficacy for free communication by developing a messaging interface, allowing users to engage in free conversation. The results showed that free communication was possible, but that information transfer was reduced by voluntary textual corrections and turn-taking during conversation. We evaluated a number of factors affecting the suitability of BCI spellers for free communication, and make specific recommendations for improving classification accuracy and usability. Overall, we found that developing a BCI speller for free communication requires a focus on usability over reduced character selection time, and as such, future performance appraisals should be based on genuine free communication scenarios.
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