ArticleScientific data2022
Thinking out loud, an open-access EEG-based BCI dataset for inner speech recognition.
Article in Scientific data, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 2 of them syntheses that pooled it.
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Who cites it
32 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Artificial intelligence for brain-to-speech decoding in paralysis: a systematic review.BMC medical informatics and decision making · 2026Pooled it
- Systematic Review of EEG-Based Imagined Speech Classification Methods.Sensors (Basel, Switzerland) · 2024Pooled it
- EEG-based brain-computer interface (BCI) dataset for directional word recognition.Scientific data · 2026Article
- Imagined Speech Brain-Computer Interface: A Task-Oriented Review of Neural Decoding.Sensors (Basel, Switzerland) · 2026Review
- The current status of foundation models in decoding inner speech from non-invasive brain signals: a mini review.Frontiers in human neuroscience · 2026Review
- Decoding Chinese speech across multiple neural conditions via EEG: dataset construction and interpretability driven spatial optimization.Frontiers in psychology · 2026Article
- Brain-CLIPLM: semantic compression for EEG-to-text decoding.Frontiers in neuroscience · 2026Article
- PQ-Neurolink: latency-aware post-quantum-ready secure communication for distributed brain-computer interfaces.Frontiers in systems neuroscience · 2026Article
- Hybrid EEG-fNIRS phoneme classification based on imagined and perceived speech.Frontiers in neuroergonomics · 2026Article
- Simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) data during an inner speech task.Data in brief · 2025Article
- Evaluating EEG-to-text models through noise-based performance analysis.Scientific reports · 2025Article
- STCCA: Spatial-Temporal Coupled Cross-Attention Through Hierarchical Network for EEG-Based Speech Recognition.Sensors (Basel, Switzerland) · 2025Article
- ArEEG: an Open-Access Arabic Inner Speech EEG Dataset.Scientific data · 2025Article
- Towards Predictive Communication: The Fusion of Large Language Models and Brain-Computer Interface.Sensors (Basel, Switzerland) · 2025Review
- An open-access EEG dataset for speech decoding: Exploring the role of articulation and coarticulation.Scientific data · 2025Article
- Gamma and Theta/Alpha-Band Oscillations in the Electroencephalogram Distinguish the Content of Inner Speech.eNeuro · 2025Article
- From pronounced to imagined: improving speech decoding with multi-condition EEG data.Frontiers in neuroinformatics · 2025Article
- Stochasticity as a solution for overfitting-A new model and comparative study on non-invasive EEG prospects.Frontiers in human neuroscience · 2025Article
- Synthesizing intelligible utterances from EEG of imagined speech.Frontiers in neuroscience · 2025Article
- Neural signals, machine learning, and the future of inner speech recognition.Frontiers in human neuroscience · 2025Review
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5 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Surface electroencephalography is a standard and noninvasive way to measure electrical brain activity. Recent advances in artificial intelligence led to significant improvements in the automatic detection of brain patterns, allowing increasingly faster, more reliable and accessible Brain-Computer Interfaces. Different paradigms have been used to enable the human-machine interaction and the last few years have broad a mark increase in the interest for interpreting and characterizing the "inner voice" phenomenon. This paradigm, called inner speech, raises the possibility of executing an order just by thinking about it, allowing a "natural" way of controlling external devices. Unfortunately, the lack of publicly available electroencephalography datasets, restricts the development of new techniques for inner speech recognition. A ten-participant dataset acquired under this and two others related paradigms, recorded with an acquisition system of 136 channels, is presented. The main purpose of this work is to provide the scientific community with an open-access multiclass electroencephalography database of inner speech commands that could be used for better understanding of the related brain mechanisms.
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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.