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Simões, Marco [Author]; Borra, Davide [Author]; Santamaría-Vázquez, Eduardo [Author]; UPM, GBT [Author]; Bittencourt-Villalpando, Mayra [Author]; Krzemi´nski, Dominik [Author]; Miladinovic, Aleksandar [Author]; Group, Neural_Engineering [Author]; Schmid, Thomas [Author]; Zhao, Haifeng [Author]; Amaral, Carlos [Author]; Direito, Bruno [Author]; Henriques, Jorge [Author]; Carvalho, Paulo [Author]; Castelo-Branco, Miguel [Author]

BCIAUT-P300: A Multi-Session and Multi-Subject Benchmark Dataset on Autism for P300-Based Brain-Computer-Interfaces

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  • Media type: E-Article
  • Title: BCIAUT-P300: A Multi-Session and Multi-Subject Benchmark Dataset on Autism for P300-Based Brain-Computer-Interfaces
  • Contributor: Simões, Marco [Author]; Borra, Davide [Author]; Santamaría-Vázquez, Eduardo [Author]; UPM, GBT [Author]; Bittencourt-Villalpando, Mayra [Author]; Krzemi´nski, Dominik [Author]; Miladinovic, Aleksandar [Author]; Group, Neural_Engineering [Author]; Schmid, Thomas [Author]; Zhao, Haifeng [Author]; Amaral, Carlos [Author]; Direito, Bruno [Author]; Henriques, Jorge [Author]; Carvalho, Paulo [Author]; Castelo-Branco, Miguel [Author]
  • Published: Lausanne: Frontiers Research Foundation, [2023]
  • Published in: Frontiers in neuroscience ; 14, (2020)
  • Language: English
  • Keywords: autism spectrum disorder ; benchmark dataset ; brain-computer interface ; multi-subject ; P300 ; EEG ; multi-session
  • Origination:
  • Footnote:
  • Description: 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 fewBCI sessions. In this sense, the lack of large, comprehensive datasets with variousindividuals and multiple sessions has limited advances in the development of moreeffective data processing and analysis methods for BCI systems. This is particularlyevident to explore the feasibility of deep learning methods that require large datasets.Here we present the BCIAUT-P300 dataset, containing 15 autism spectrum disorderindividuals undergoing 7 sessions of P300-based BCI joint-attention training, for atotal of 105 sessions. The dataset was used for the 2019 IFMBE Scientific Challengeorganized during MEDICON 2019 where, in two phases, teams from all over the worldtried 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 bythe 9 finalist teams during the competition. The winner obtained an average accuracyof 92.3% with a convolutional neural network based on EEGNet. The dataset is nowpublicly released and stands as a benchmark for future P300-based BCI algorithmsbased on multiple session data.
  • Access State: Open Access
  • Rights information: Attribution (CC BY)