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The article presents the use of genetic algorithm (GA) to select and classify ERD/ERS patterns. One hundred twenty eight channel EEG signal was used in the experiments. The signal was recorded for 40 people, during the process of imagining right and left hand movements. Feature extraction was performed using frequency analysis (FFT) with the resolution of 1Hz. So the features were spectral lines associated...
This article contains a description of a data acquisition system that enables simultaneous recording of selected human physiological signals, resulting from brain electrical activity, eye movement, facial expression and skin-galvanic reaction. The signals, recorded using various types of sensors/devices, are fully synchronized and can be used to detect and identify emotions.
A new method of automatic SSVEP detection in the EEG signal based on Independent Components Analysis (A-ICA) is described in the article. For the presented method reduction of artifacts in EEG signal is not required. Besides, the method has low computational complexity. In order to select the best ICA decomposition, we conducted a comparative study of known ICA algorithms for the use in SSVEP detection...
In the article we present a new method of feature extraction from EEG signal for SSVEP recognition, called a simplified Matching Pursuit (sMP). The method has been tested on SSVEP for stimulation frequencies close to each other − 5, 6, 7, 8Hz. Its effectiveness has been verified and compared with commonly used for this purpose FFT method. sMP enables effective detection of SSVEP with an accuracy of...
This study was carried out to select EEG signal preprocessing methods to effectively detect and classify Steady State Visually Evoked Potentials (SSVEP). Algorithms, such as: Common Average Reference, Independent Component Analysis (in the task of electrooculography artifacts removing and SSVEP enhancement) and combinations of them were implemented and tested. The best classification accuracy improvement...
In the article the authors present their own method of designing spatial filters to use in brain-computer interface, based on steady state visually evoked potentials. The spatial filter is calculated by minimizing a specially created objective function. The developed method allows us to create a dedicated filter for each user, however it demands a calibration session. By using designed spatial filters...
This paper discusses an asynchronous system of brain-computer interface, operating in real time. In the proposed system, the processing, analysis and classification of EEG signal is implemented using the Matlab programming environment and the BCI2000 package.
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