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Bimodal electroence...
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Simistira Liwicki, FoteiniLuleå tekniska universitet,EISLAB,Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Intelligent Systems LAB, Luleå, Sweden
(author)
Bimodal electroencephalography-functional magnetic resonance imaging dataset for inner-speech recognition
- Article/chapterEnglish2023
Publisher, publication year, extent ...
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Springer Nature,2023
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electronicrdacarrier
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LIBRIS-ID:oai:DiVA.org:ltu-98322
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https://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-98322URI
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https://doi.org/10.1038/s41597-023-02286-wDOI
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https://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-211406URI
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Language:English
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Summary in:English
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Subject category:ref swepub-contenttype
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Subject category:art swepub-publicationtype
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Validerad;2023;Nivå 2;2023-06-13 (hanlid);Funder: Grants for Excellent Research Projects Proposals of SRT.ai 2022
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The recognition of inner speech, which could give a ‘voice’ to patients that have no ability to speak or move, is a challenge for brain-computer interfaces (BCIs). A shortcoming of the available datasets is that they do not combine modalities to increase the performance of inner speech recognition. Multimodal datasets of brain data enable the fusion of neuroimaging modalities with complimentary properties, such as the high spatial resolution of functional magnetic resonance imaging (fMRI) and the temporal resolution of electroencephalography (EEG), and therefore are promising for decoding inner speech. This paper presents the first publicly available bimodal dataset containing EEG and fMRI data acquired nonsimultaneously during inner-speech production. Data were obtained from four healthy, right-handed participants during an inner-speech task with words in either a social or numerical category. Each of the 8-word stimuli were assessed with 40 trials, resulting in 320 trials in each modality for each participant. The aim of this work is to provide a publicly available bimodal dataset on inner speech, contributing towards speech prostheses.
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Gupta, VibhaLuleå tekniska universitet,EISLAB,Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Intelligent Systems LAB, Luleå, Sweden(Swepub:ltu)vibgup
(author)
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Saini, Rajkumar,Dr.1988-Luleå tekniska universitet,EISLAB,Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Intelligent Systems LAB, Luleå, Sweden(Swepub:ltu)rajsai
(author)
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De, KanjarLuleå tekniska universitet,EISLAB,Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Intelligent Systems LAB, Luleå, Sweden(Swepub:ltu)kandeq
(author)
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Abid, Nosheen,1993-Luleå tekniska universitet,EISLAB,Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Intelligent Systems LAB, Luleå, Sweden(Swepub:ltu)nosabi
(author)
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Rakesh, SumitLuleå tekniska universitet,EISLAB,Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Intelligent Systems LAB, Luleå, Sweden(Swepub:ltu)sumrak
(author)
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Wellington, ScottUniversity of Bath, Department of Computer Science, Bath, United Kingdom
(author)
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Wilson, HollyUniversity of Bath, Department of Computer Science, Bath, United Kingdom
(author)
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Liwicki, MarcusLuleå tekniska universitet,EISLAB,Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Intelligent Systems LAB, Luleå, Sweden(Swepub:ltu)marliw
(author)
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Eriksson, JohanUmeå universitet,Institutionen för integrativ medicinsk biologi (IMB),Umeå centrum för funktionell hjärnavbildning (UFBI),Umeå University, Department of Integrative Medical Biology (IMB) and Umeå Center for Functional Brain Imaging (UFBI), Umeå, Sweden(Swepub:umu)joer0003
(author)
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Luleå tekniska universitetEISLAB
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In:Scientific Data: Springer Nature102052-4463
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