ArticleScientific data2023
EEG-based BCI Dataset of Semantic Concepts for Imagination and Perception Tasks.
Article in Scientific data, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- A Deep Learning Framework for EEG-Based Decoding of Visually Imagined Arrows with Different Colors and Directions.Biosensors · 2026Article
- An EEG Dataset for Visual Imagery-Based Brain-Computer Interface.Scientific data · 2026Article
- Exploring individual biases in BCI research and users: Does gender matter?Frontiers in human neuroscience · 2025Article
- Chisco: An EEG-based BCI dataset for decoding of imagined speech.Scientific data · 2024Article
- A Spatio-Temporal Capsule Neural Network with Self-Correlation Routing for EEG Decoding of Semantic Concepts of Imagination and Perception Tasks.Sensors (Basel, Switzerland) · 2024Article
- An EEG Dataset of Neural Signatures in a Competitive Two-Player Game Encouraging Deceptive Behavior.Scientific data · 2024Article
- Brain-computer interfaces and human factors: the role of language and cultural differences-Still a missing gap?Frontiers in human neuroscience · 2024Article
- Covert cortical processing: a diagnosis in search of a definition.Neuroscience of consciousness · 2024Article
- EEG-based BCI Dataset of Semantic Concepts for Imagination and Perception Tasks.Scientific data · 2023Article
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Authors and funding
5 authors.
Funding
Abstract
Electroencephalography (EEG) is a widely-used neuroimaging technique in Brain Computer Interfaces (BCIs) due to its non-invasive nature, accessibility and high temporal resolution. A range of input representations has been explored for BCIs. The same semantic meaning can be conveyed in different representations, such as visual (orthographic and pictorial) and auditory (spoken words). These stimuli representations can be either imagined or perceived by the BCI user. In particular, there is a scarcity of existing open source EEG datasets for imagined visual content, and to our knowledge there are no open source EEG datasets for semantics captured through multiple sensory modalities for both perceived and imagined content. Here we present an open source multisensory imagination and perception dataset, with twelve participants, acquired with a 124 EEG channel system. The aim is for the dataset to be open for purposes such as BCI related decoding and for better understanding the neural mechanisms behind perception, imagination and across the sensory modalities when the semantic category is held constant.
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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.