Bimodal electroencephalography-functional magnetic resonance imaging dataset for inner-speech recognition.

ABSTRACT: 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,

SUBMITTER: Simistira Liwicki F 

PROVIDER: S-EPMC10264396 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature