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Considering the two-class classification problem in brain imaging data analysis, we propose a sparse representation-based multi-variate pattern analysis (MVPA) algorithm to localize brain activation patterns corresponding to different stimulus classes/brain states respectively. Feature selection can be modeled as a sparse representation (or sparse(More)
Two-dimensional cursor control is an important and challenging issue in EEG-based brain-computer interfaces (BCIs). To address this issue, here we propose a new approach by combining two brain signals including Mu/Beta rhythm during motor imagery and P300 potential. In particular, a motor imagery detection mechanism and a P300 potential detection mechanism(More)
Brain-computer interfaces (BCIs) are used to translate brain activity signals into control signals for external devices. Currently, it is difficult for BCI systems to provide the multiple independent control signals necessary for the multi-degree continuous control of a wheelchair. In this paper, we address this challenge by introducing a hybrid BCI that(More)
Wheelchair control requires multiple degrees of freedom and fast intention detection, which makes electroencephalography (EEG)-based wheelchair control a big challenge. In our previous study, we have achieved direction (turning left and right) and speed (acceleration and deceleration) control of a wheelchair using a hybrid brain–computer interface (BCI)(More)
To control a cursor on a monitor screen, a user generally needs to perform two tasks sequentially. The first task is to move the cursor to a target on the monitor screen (termed a 2-D cursor movement), and the second task is either to select a target of interest by clicking on it or to reject a target that is not of interest by not clicking on it. In a(More)
Brain-computer interface-based communication plays an important role in brain-computer interface (BCI) applications; electronic mail is one of the most common communication tools. In this study, we propose a hybrid BCI-based mail client that implements electronic mail communication by means of real-time classification of multimodal features extracted from(More)
One of the central questions in cognitive neuroscience is the precise neural representation, or brain pattern, associated with a semantic category. In this study, we explored the influence of audiovisual stimuli on the brain patterns of concepts or semantic categories through a functional magnetic resonance imaging (fMRI) experiment. We used a pattern(More)
| Despite rapid advances in the study of brain– computer interfaces (BCIs) in recent decades, two fundamental challenges, namely, improvement of target detection performance and multidimensional control, continue to be major barriers for further development and applications. In this paper, we review the recent progress in multimodal BCIs (also called hybrid(More)
Neurobiological markers of stress symptom progression for healthy survivors from a disaster (e.g., an earthquake) would greatly help with early intervention to prevent the development of stress-related disorders. However, the relationship between the neurobiological alterations and the symptom progression over time is unclear. Here, we examined 44 healthy(More)
Previous studies have shown that audiovisual integration improves identification performance and enhances neural activity in heteromodal brain areas, for example, the posterior superior temporal sulcus/middle temporal gyrus (pSTS/MTG). Furthermore, it has also been demonstrated that attention plays an important role in crossmodal integration. In this study,(More)