Abstract
We propose a method for reconstruction of the high-dimensional phase space of the electroencephalography (EEG) signal. The method is based on the selection of positive trajectories from the phase space, distance-adaptive sampling of the negative trajectories from the phase space, classification of trajectories in the phase space, and reconstruction of a fuzzy state of the signal for classification of EEG signals. DOI: http://dx.doi.org/10.5755/j01.eee.20.8.8441
Highlights
Brain-computer interface (BCI) is a communication system that translates brain activity into commands for a computer or other digital device [1]
Nonlinearity of the EEG and other types of biosignals has been studied by applying nonlinear analysis methods such as Detrended Fluctuation Analysis (DFA) [5], Poincaré Plots [6], reconstruction of Local Phase Space, segmentation of the EEG signal into stationary fragments [7], application of non-linear operators to the EEG time series [8]
This paper proposes a method for reconstruction of the high-dimensional phase space of the EEG signal
Summary
Brain-computer interface (BCI) is a communication system that translates brain activity into commands for a computer or other digital device [1]. Most BCI systems work by reading and interpreting cortically-evoked electropotentials via an electroencephalogram (EEG) data. The frequencies of these brain waves range from 0.5 Hz to 100 Hz, and their characteristics change dynamically depending on the activity of the human brain [2]. BCI systems require correct classification of the EEG signals for useful operation. Since the EEG signal may be considered as chaotic [3], [4], the nonlinear dynamics and chaos theory methods can be applied for analysis and classification of the EEG data. Nonlinearity of the EEG and other types of biosignals has been studied by applying nonlinear analysis methods such as Detrended Fluctuation Analysis (DFA) [5], Poincaré Plots [6], reconstruction of Local Phase Space, segmentation of the EEG signal into stationary fragments [7], application of non-linear operators to the EEG time series [8]
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