TSANet: multibranch attention deep neural network for classifying tactile selective attention in brain-computer interfaces.

ABSTRACT: Brain-computer interfaces (BCIs) enable communication between the brain and a computer and electroencephalography (EEG) has been widely used to implement BCIs because of its high temporal resolution and noninvasiveness. Recently, a tactile-based EEG task was introduced to overcome the current limitations of visual-based tasks, such as visual fatigue from sustained attention. However, the classification performance of tactile-based BCIs as control signals is unsatisfactory. Therefore, a novel classification approach is required for this purpose. Here, we propose TSANet, a deep neural network, that uses multibranch convolutional neural networks and a feature-attention mechanism to classify tactile selective attention (TSA) in a tactile-based BCI system. We tested TSANet under three evaluatio

SUBMITTER: Jang H 

PROVIDER: S-EPMC10770016 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature