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Hohmann, Matthias R.; Konieczny, Lisa; Hackl, Michelle; Wirth, Brian; Zaman, Talha; Enficiaud, Raffi; Grosse-Wentrup, Moritz; Schölkopf, Bernhard

doi:10.1145/3379337.3415844

Cited by 2 publications

References 48 publications

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Hohmann

Konieczny

Hackl

et al. 2020

Proceedings of the 33rd Annual ACM Symposium on User Interface Software and Technology

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Neurophysiological laboratory studies are often constraint to immediate geographical surroundings and access to equipment may be temporally restricted. Limitations of ecological validity, scalability, and generalizability of findings pose a significant challenge for the development of brain-computer interfaces (BCIs), which ultimately need to function in any context, on consumer-grade hardware. We introduce MYND: An open-source framework that couples consumer-grade recording hardware with an easy-to-use application for the unsupervised evaluation of BCI control strategies. Subjects are guided through experiment selection, hardware fitting, recording, and data upload in order to self-administer multi-day studies that include neurophysiological recordings and questionnaires at home. As a use case, thirty subjects evaluated two BCI control strategies ("Positive memories" and "Music imagery") by using a four-channel electroencephalogram (EEG) with MYND. Neural activity in both control strategies could be decoded with an average offline accuracy of 68.5% and 64.0% across all days.

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Hohmann

Konieczny

Hackl

et al. 2020

Proceedings of the 33rd Annual ACM Symposium on User Interface Software and Technology

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GO-E-MON: A New Online Platform for Decentralized Cognitive Science

Yazawa

Sakaguchi

Hiraki

2021

BDCC

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Advances in web technology and the widespread use of smartphones and PCs have proven that it is possible to optimize various services using personal data, such as location information and search history. While considerations of personal privacy and legal aspects lead to situations where data are monopolized by individual services and companies, a replication crisis has been pointed out for the data of laboratory experiments, which is challenging to solve given the difficulty of data distribution. To ensure distribution of experimental data while guaranteeing security, an online experiment platform can be a game changer. Current online experiment platforms have not yet considered improving data distribution, and it is currently difficult to use the data obtained from one experiment for other purposes. In addition, various devices such as activity meters and consumer-grade electroencephalography meters are emerging, and if a platform that collects data from such devices and tasks online is to be realized, the platform will hold a large amount of sensitive data, making it even more important to ensure security. We propose GO-E-MON, a service that combines an online experimental environment with a distributed personal data store (PDS), and explain how GO-E-MON can realize the reuse of experimental data with the subject’s consent by connecting to a distributed PDS. We report the results of the experiment in a groupwork lecture for university students to verify whether this method works. By building an online experiment environment integrated with a distributed PDS, we present the possibility of integrating multiple experiments performed by different experimenters—with the consent of individual subjects—while solving the security issues.

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Cited by 2 publications

References 48 publications

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GO-E-MON: A New Online Platform for Decentralized Cognitive Science

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