ArticleFrontiers in neuroscience2020
BCIAUT-P300: A Multi-Session and Multi-Subject Benchmark Dataset on Autism for P300-Based Brain-Computer-Interfaces.
Article in Frontiers in neuroscience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 65 citations in OpenAlex.
- Brain-Computer Interfaces: The Dawn of a New Era in Disease Treatment.Exploration (Beijing, China) · 2026Review
- Inferring Arm Movement Direction from EEG Signals Using Explainable Deep Learning.Sensors (Basel, Switzerland) · 2026Article
- A deep learning-enriched framework for analyzing brain functional connectivity.Scientific reports · 2025Article
- A role for preparatory midfrontal theta in autism as revealed by a high executive load brain-computer interface reverse spelling task.Scientific reports · 2025Article
- A toolbox for decoding BCI commands based on event-related potentials.Frontiers in human neuroscience · 2024Article
- A brain functional network feature extraction method based on directed transfer function and graph theory for MI-BCI decoding tasks.Frontiers in neuroscience · 2024Article
- Deep learning applied to EEG source-data reveals both ventral and dorsal visual stream involvement in holistic processing of social stimuli.Scientific reports · 2023Article
- Machine Learning Enabled P300 Classifier for Autism Spectrum Disorder Using Adaptive Signal Decomposition.Brain sciences · 2023Article
- On the relative importance of attention and response selection processes for multi-component behavior - Evidence from EEG-based deep learning.Neuroimage. Reports · 2022Article
- A Lightweight Multi-Scale Convolutional Neural Network for P300 Decoding: Analysis of Training Strategies and Uncovering of Network Decision.Frontiers in human neuroscience · 2021Article
Corrections and comments
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Authors and funding
15 authors at 6 institutions in 7 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
There is a lack of multi-session P300 datasets for Brain-Computer Interfaces (BCI). Publicly available datasets are usually limited by small number of participants with few BCI sessions. In this sense, the lack of large, comprehensive datasets with various individuals and multiple sessions has limited advances in the development of more effective data processing and analysis methods for BCI systems. This is particularly evident to explore the feasibility of deep learning methods that require large datasets. Here we present the BCIAUT-P300 dataset, containing 15 autism spectrum disorder individuals undergoing 7 sessions of P300-based BCI joint-attention training, for a total of 105 sessions. The dataset was used for the 2019 IFMBE Scientific Challenge organized during MEDICON 2019 where, in two phases, teams from all over the world tried to achieve the best possible object-detection accuracy based on the P300 signals. This paper presents the characteristics of the dataset and the approaches followed by the 9 finalist teams during the competition. The winner obtained an average accuracy of 92.3% with a convolutional neural network based on EEGNet. The dataset is now publicly released and stands as a benchmark for future P300-based BCI algorithms based on multiple session data.
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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.