It Takes Two (Seconds): Decreasing Encoding Time for Two-2 Choice FNIRS BCI Communication

DOI

Significance. Brain-computer interfaces (BCIs) can provide severely motor-impaired patients with a motor-independent communication channel. Functional near-infrared spectroscopy (fNIRS) constitutes a promising BCI-input modality given its high mobility, safety, user comfort, cost-efficiency, and relatively low motion sensitivity. Aim. The present study aimed at developing an efficient and convenient two-choice fNIRS communication BCI by implementing a relatively short encoding time (2s), considerably increasing communication speed, and decreasing the cognitive load of BCI users. Approach. To encode binary answers to ten biographical questions, ten healthy adults repeatedly performed a combined motor-speech imagery task within two different time windows guided by auditory instructions. Each answer-encoding run consisted of ten trials. Answers were decoded during the ongoing experiment from the time course of the individually identified most-informative fNIRS channel-by-chromophore combination. Results. The answers of participants were decoded online with an accuracy of 85.8% (run-based group mean). Post-hoc analysis yielded an average single-trial accuracy of 68.1%. Analysis of the effect of number of trial repetitions showed that the best information-transfer rate could be obtained by combining four encoding trials. Conclusions. The study demonstrates that an encoding time as short as 2s can enable immediate, efficient, and convenient fNIRS-BCI communication.

Identifier
DOI https://doi.org/10.34894/XJSOFU
Metadata Access https://dataverse.nl/oai?verb=GetRecord&metadataPrefix=oai_datacite&identifier=doi:10.34894/XJSOFU
Provenance
Creator Vorreuther, Anna ORCID logo; Bastian, Lisa ORCID logo; Benitez Andonegui, Amaia ORCID logo; Evenblij, Danielle ORCID logo; Riecke, Lars ORCID logo; Lührs, Michael ORCID logo; Sorger, Bettina ORCID logo
Publisher DataverseNL
Contributor faculty data manager FPN; Sorger, Bettina
Publication Year 2023
Rights info:eu-repo/semantics/restrictedAccess
OpenAccess false
Contact faculty data manager FPN (Maastricht University); Sorger, Bettina (Maastricht University)
Representation
Resource Type Dataset
Format application/zip
Size 855629978
Version 1.0
Discipline Agriculture, Forestry, Horticulture, Aquaculture; Agriculture, Forestry, Horticulture, Aquaculture and Veterinary Medicine; Life Sciences; Social Sciences; Social and Behavioural Sciences; Soil Sciences