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The nonstationary nature of the brain signals provides a rather unstable input resulting in uncertainty and complexity in the control. Intelligent processing algorithms adapted to the task are a prerequisite for reliable BCI applications. This work presents a novel intelligent processing strategy for the realization of an effective BCI which has the capability to improved classification accuracy and...
The high order pattern discovery algorithm is applied to classify schizophrenia and health's EEG signals. Samples of 780 schizophrenia and health EEG pieces are classified. The result shows that the classification accuracy can achieve 90% in 6-order. The 6-orders are associated with frontal polar, temporal and occipital regions.
In this study, we analyze brain connectivity based on Granger causality computed from magnetoencephalographic (MEG) activity obtained at the resting state in eight autistic and eight normal subjects along with measures of network connectivity derived from graph theory in an attempt to understand how communication in a human brain network is affected by autism. A connectivity matrix was computed for...
Prosthetic hands of increasing capability and sophistication are being built, but how does the user tell the hand what to do? One method is to use the low-level electrical signals associated with forearm muscle movement, or electrogmyograms (EMGs). This paper describes an experiment in which supervised learning, or classification, was used to build a model that decides which of a set of hand gestures...
Visual evaluation of long-term EEG recordings is very difficult, time consuming and subjective process. This paper aims to present the research and development of a comprehensive scheme for computer-assisted recognition of behavioral states of sleep in newborns. In clinical practice, the ratio of behavioral states (wakefulness, quiet and active sleep) is used as an important indicator of the brain...
Always, one of the issues in the brain-computer interface (BCI) is to extract components from raw EEG data that have more information in order to separate task-related potentials from other neural and artifactual EEG sources. In this paper, a new method is proposed for extracting components from raw EEG data such that these components have maximal information for separating task-related potentials...
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