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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)
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)
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)
STUDY DESIGN A porcine model of thoracic pedicle screw insertion was used to determine the effect of screw position on triggered electromyographic response. OBJECTIVE To develop a model of intraoperative detection of misplaced thoracic pedicle screws. SUMMARY OF BACKGROUND DATA Triggered electromyographic stimulation has been a valuable aid in(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)
In this paper, we address the important problem of feature selection for a P300-based brain computer interface (BCI) speller system in several aspects. Firstly, time segment selection and electroencephalogram channel selection are jointly performed for better discriminability of P300 and background signals. Secondly, in view of the situation that training(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)
| 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)
Parameter setting plays an important role for improving the performance of a brain computer interface (BCI). Currently, parameters (e.g. channels and frequency band) are often manually selected. It is time-consuming and not easy to obtain an optimal combination of parameters for a BCI. In this paper, motor imagery-based BCIs are considered, in which(More)