ArticleJournal of neuroengineering and rehabilitation2011
Brain-computer interfacing using modulations of alpha activity induced by covert shifts of attention.
Article in Journal of neuroengineering and rehabilitation, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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Who cites it
32 citing papers in PubMed.
- Trial
- Decoding Visual Spatial Attention Control.eNeuro · 2025Article
- Resting-state electroencephalogram in drug-free subjects with at-risk mental states who later developed psychosis: a low-resolution electromagnetic tomography analysis.Frontiers in human neuroscience · 2024Article
- Generative adversarial networks in EEG analysis: an overview.Journal of neuroengineering and rehabilitation · 2023Review
- Article
- Target-Related Alpha Attenuation in a Brain-Computer Interface Rapid Serial Visual Presentation Calibration.Frontiers in human neuroscience · 2022Article
- Beyond the brain-computer interface: Decoding brain activity as a tool to understand neuronal mechanisms subtending cognition and behavior.Frontiers in neuroscience · 2022Review
- Bridging M/EEG Source Imaging and Independent Component Analysis Frameworks Using Biologically Inspired Sparsity Priors.Neural computation · 2021Article
- Benefits of deep learning classification of continuous noninvasive brain-computer interface control.Journal of neural engineering · 2021Article
- Spatial-temporal aspects of continuous EEG-based neurorobotic control.Journal of neural engineering · 2020Article
- Changes of Effective Connectivity in the Alpha Band Characterize Differential Processing of Audiovisual Information in Cross-Modal Selective Attention.Neuroscience bulletin · 2020Article
- Spontaneous network activity <35 Hz accounts for variability in stimulus-induced gamma responses.NeuroImage · 2020Article
- Decoding the Temporal Dynamics of Covert Spatial Attention Using Multivariate EEG Analysis: Contributions of Raw Amplitude and Alpha Power.Frontiers in human neuroscience · 2020Article
- Impact of Stimulus Features on the Performance of a Gaze-Independent Brain-Computer Interface Based on Covert Spatial Attention Shifts.Frontiers in neuroscience · 2020Article
- Commentary: Alpha Synchrony and the Neurofeedback Control of Spatial Attention.Frontiers in neuroscience · 2020Article
- More than Meets the Mind's Eye? Preliminary Observations Hint at Heterogeneous Alpha Neuromarkers for Visual Attention.Brain sciences · 2019Article
- Neurotechnologies for Human Cognitive Augmentation: Current State of the Art and Future Prospects.Frontiers in human neuroscience · 2019Review
- Three-Dimensional Brain-Computer Interface Control Through Simultaneous Overt Spatial Attentional and Motor Imagery Tasks.IEEE transactions on bio-medical engineering · 2018Article
- Geometric classification of brain network dynamics via conic derivative discriminants.Journal of neuroscience methods · 2018Article
- EEG-based neglect assessment: A feasibility study.Journal of neuroscience methods · 2018Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundVisual brain-computer interfaces (BCIs) often yield high performance only when targets are fixated with the eyes. Furthermore, many paradigms use intense visual stimulation, which can be irritating especially in long BCI sessions. However, BCIs can more directly directly tap the neural processes underlying visual attention. Covert shifts of visual attention induce changes in oscillatory alpha activity in posterior cortex, even in the absence of visual stimulation. The aim was to investigate whether different pairs of directions of attention shifts can be reliably differentiated based on the electroencephalogram. To this end, healthy participants (N = 8) had to strictly fixate a central dot and covertly shift visual attention to one out of six cued directions.
resultsCovert attention shifts induced a prolonged alpha synchronization over posterior electrode sites (PO and O electrodes). Spectral changes had specific topographies so that different pairs of directions could be differentiated. There was substantial variation across participants with respect to the direction pairs that could be reliably classified. Mean accuracy for the best-classifiable pair amounted to 74.6%. Furthermore, an alpha power index obtained during a relaxation measurement showed to be predictive of peak BCI performance (r = .66).
conclusionsResults confirm posterior alpha power modulations as a viable input modality for gaze-independent EEG-based BCIs. The pair of directions yielding optimal performance varies across participants. Consequently, participants with low control for standard directions such as left-right might resort to other pairs of directions including top and bottom. Additionally, a simple alpha index was shown to predict prospective BCI performance.
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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.