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This paper aims to investigate the effects of steady-state visual evoked potential (SSVEP) in the aspects of viewing distance variation for a smart TV control system. We designed an experimental environments with different viewing distance. Four healthy people (age 27.5±1, male) participated in the experiment. Four visual stimuli with a round shape were designed and presented on LED monitor flickering...
This paper presents a prototype for an electro-encephalogram (EEG) based brain-actuated wheelchair system using motor imagery. To overcome some of the limitations of other previous works, such as gaze dependence and unnecessary stops, five commands (left, left-diagonal, right, right-diagonal, and forward) were decoded based on the motor imagery correlates in EEG signals. Also, the system was modularized...
In this paper, we propose a novel Bayesian frame-work for discriminative feature extraction for motor imagery classification in an EEG-based BCI, in which the class-discriminative frequency bands and the corresponding spatial filters are optimized by means of the probabilistic and information-theoretic approaches. In our framework, the problem of simultaneous spatio-spectral filter optimization is...
Neuronal power attenuation or enhancement in the specific frequency bands over the sensorimotor cortex, called Event-Related Desynchronization (ERD) or Event-Related Synchronization (ERS), is one of the major phenomena in brain signals evoked by imagination of body parts movement. So many research groups have devoted their efforts to extract discriminative features by utilizing these phenomena and...
Classifiers in a high dimensional space based on the signals of multiple electrodes in EEG-based BCIs suffer from the curse of dimensionality due to the limited training dataset. In order to tackle this problem, we design a framework of two-layer hidden Markov models (HMMs) for probabilistic classification of EEG signals. We first independently model the characteristics of EEG signals embedded in...
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