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Recent studies have shown that the various brain networks over different cognitive states. In contrast to measure a physiological change over a single region, the information flows between brain regions described by effective connectivity provides an informative dynamic over the whole brain. In this study, we proposed a source information flow network based on the combination of Granger causality...
For the BCI research to classify the different imagined movements of both left and right hands, a method using wavelet packet decomposition for feature extraction and using SVM for pattern classification was adopted. Firstly discusses the wavelet packet transform in depth and brings out an idea of taking wavelet packet coefficients' variance as feature into account, then extracts the feature serials...
In recent years, developments in functional neuroimaging technologies have helped facilitate a clearer understanding of the activation of sites in the brain. This technology is applied to brain-computer interfaces (BCIs). Previous BCIs have primarily used information on the brain activity related to the motor system. In this study, we examined the possibility of controlling the decision-making abilities...
This paper presents the satellite television remote control system based on brain-computer interface. The Brain Controlled Satellite Television Remote System (BCSTRS) is a real time system that can help the patients suffering from Amyotrophic Lateral Sclerosis (ALS) to select TV channels or adjust volume using their brain waves. In this paper we propose an algorithm including data acquisition and...
Brain-Computer Interface is an alternative communication system between human and outside world which enables paralyzed and locked-in patients (like Amyotrophic lateral sclerosis - ALS) to communicate with their environment or control some electronic devices like computer using only their brain activity. Over the last two decades, numerous studies have been performed on this title and researchers...
This paper evaluates supervised and unsupervised adaptive schemes applied to online support vector machine (SVM) that classifies BCI data. Online SVM processes fresh samples as they come and update existing support vectors without referring to pervious samples. It is shown that the performance of online SVM is similar to that of the standard SVM, and both supervised and unsupervised schemes improve...
In this paper we study the effectiveness of using multiple classifier combination for EEG signal classification aiming to obtain more accurate results than it possible from each of the constituent classifiers. The developed system employs two linear classifiers (SVM,LDA) fused at the abstract and measurement levels for integrating information to reach a collective decision. For making decision, the...
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