A functional BCI model by the IEEE P2731 working group: data storage and sharing

Bianchi, Luigi; Antonietti, Alberto; Bajwa, Garima; Ferrante, Raffaele; Mahmud, Mufti; Balachandran, Pradeep

doi:10.1080/2326263x.2021.1968632

Cited by 6 publications

(5 citation statements)

References 18 publications

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“…The IEEE Standard Associations P2731 levels 0 and 1 specifications for BCI data storage were adopted (Bianchi et al, 2021 ). They deal with the information that should be stored in a file and not the technology to be used or any software implementation.…”

Section: Methodsmentioning

confidence: 99%

“…The logical alphabet for a given P300 system may be defined according to the IEEE P2731 BCI functional model as the outputs of the transducer (Bianchi et al, 2021 ), which oversees the acquisition and processing of brain signals (Sahonero-Alvarez et al, 2021 ). In most cases, the logical alphabet is equivalent to the set of possible classifier labels (e.g., the 6 rows and the 6 columns) and is used to solve ML problems.…”

Section: Methodsmentioning

confidence: 99%

“…The IEEE P2731 proposal (Bianchi et al, 2021 ) follows the FAIR principles (Wilkinson et al, 2016 ; Stall et al, 2019 ) but does not deal with adopting technologies for data storage. Extensible Markup Language (Bray et al, 2008 ) and JavaScript Object Notation (JSON, 1999 ) are possible technologies for real implementation, widely utilized in several ICT areas because they are human-readable, extensible, and easy to understand and use.…”

Section: Methodsmentioning

confidence: 99%

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Merging Brain-Computer Interface P300 speller datasets: Perspectives and pitfalls

Bianchi¹,

Ferrante²,

Hu³

et al. 2022

Front. Neuroergonomics

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BackgroundIn the last decades, the P300 Speller paradigm was replicated in many experiments, and collected data were released to the public domain to allow research groups, particularly those in the field of machine learning, to test and improve their algorithms for higher performances of brain-computer interface (BCI) systems. Training data is needed to learn the identification of brain activity. The more training data are available, the better the algorithms will perform. The availability of larger datasets is highly desirable, eventually obtained by merging datasets from different repositories. The main obstacle to such merging is that all public datasets are released in various file formats because no standard way is established to share these data. Additionally, all datasets necessitate reading documents or scientific papers to retrieve relevant information, which prevents automating the processing. In this study, we thus adopted a unique file format to demonstrate the importance of having a standard and to propose which information should be stored and why.MethodsWe described our process to convert a dozen of P300 Speller datasets and reported the main encountered problems while converting them into the same file format. All the datasets are characterized by the same 6 × 6 matrix of alphanumeric symbols (characters and numbers or symbols) and by the same subset of acquired signals (8 EEG sensors at the same recording sites).Results and discussionNearly a million stimuli were converted, relative to about 7000 spelled characters and belonging to 127 subjects. The converted stimuli represent the most extensively available platform for training and testing new algorithms on the specific paradigm – the P300 Speller. The platform could potentially allow exploring transfer learning procedures to reduce or eliminate the time needed for training a classifier to improve the performance and accuracy of such BCI systems.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Merging Brain-Computer Interface P300 speller datasets: Perspectives and pitfalls

Bianchi¹,

Ferrante²,

Hu³

et al. 2022

Front. Neuroergonomics

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“…The source files and EEG data files in this dataset were organized according to EEG-BIDS 28 , which was an extension of the brain imaging data structure for EEG. There were many ways to access data, such as IEEE P2731 29 , FAIR 30 , and EEG-BIDS. Among them, IEEE P2731 defines a complete storage system, which included decoding algorithms, preprocessing, feature extraction, and classification.…”

Section: Experimental Paradigmmentioning

confidence: 99%

A large EEG dataset for studying cross-session variability in motor imagery brain-computer interface

Yang

Qiu

et al. 2022

Sci Data

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In building a practical and robust brain-computer interface (BCI), the classification of motor imagery (MI) from electroencephalography (EEG) across multiple days is a long-standing challenge due to the large variability of the EEG signals. We collected a large dataset of MI from 5 different days with 25 subjects, the first open-access dataset to address BCI issues across 5 different days with a large number of subjects. The dataset includes 5 session data from 5 different days (2–3 days apart) for each subject. Each session contains 100 trials of left-hand and right-hand MI. In this report, we provide the benchmarking classification accuracy for three conditions, namely, within-session classification (WS), cross-session classification (CS), and cross-session adaptation (CSA), with subject-specific models. WS achieves an average classification accuracy of up to 68.8%, while CS degrades the accuracy to 53.7% due to the cross-session variability. However, by adaptation, CSA improves the accuracy to 78.9%. We anticipate this new dataset will significantly push further progress in MI BCI research in addressing the cross-session and cross-subject challenge.

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“…Another standardization project, IEEE P2731: Standard for a Unified Terminology for Brain-Computer Interfaces (BCI) (https://sagroups.ieee.org/2731/) aims at developing a comprehensive BCI lexicography and a functional model of BCI systems [312][313][314]. It is also working on identifying the required information to be stored in BCI files to enable efficient sharing of data and tools among stakeholders [315]. These activities can contribute to the development of standard experimental and usage protocols, benchmarking procedures, and increased interoperability of neurotechnology systems.…”

Section: On the Need Of Good Practices And Standards For Benchmarking...mentioning

confidence: 99%

Workshops of the eighth international brain–computer interface meeting: BCIs: the next frontier

Huggins

Krusienski

Vansteensel

et al. 2022

Brain-Computer Interfaces

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The Eighth International Brain-Computer Interface (BCI) Meeting was held June 7-9, 2021 in a virtual format. The conference continued the BCI Meeting series' interactive nature with 21 workshops covering the breadth of topics in BCI (also called brain-machine interface) research. Some workshops provided detailed examinations of methods, hardware, or processes. Others focused on BCI applications or user groups. Several workshops continued consensus building efforts designed to create BCI standards and improve comparisons between studies and the potential for meta-analysis and large multi-site clinical trials. Ethical and translational considerations were the primary topic for some workshops or an important secondary consideration for others. The range of BCI applications continues to expand, with more workshops focusing on approaches that can extend beyond the needs of those with physical impairments. This paper summarizes each workshop, provides background information and references for further study, summarizes discussions, and describes the resulting conclusion, challenges, or initiatives.

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A functional BCI model by the IEEE P2731 working group: data storage and sharing

Cited by 6 publications

References 18 publications

Merging Brain-Computer Interface P300 speller datasets: Perspectives and pitfalls

Merging Brain-Computer Interface P300 speller datasets: Perspectives and pitfalls

A large EEG dataset for studying cross-session variability in motor imagery brain-computer interface

Workshops of the eighth international brain–computer interface meeting: BCIs: the next frontier

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