[The possibility of a multiresolution wavelet analysis for detecting the P300 event related potential]. / O posibilitate de detectie a potentialului P300 cu ajutorul transformatei wavelet multirezolutie.

UNLABELLED: The main objective is to high-light the P300 potential on certain electroencephalographic signals. P300 occurs at a relatively well defined time in relation to a stimulus and it represents a signal with a specified band frequency. METHOD: The electroencephalographic (EEG) was recorded with 4 wet electrodes by means of g.MOBIlab+ module, a g.tec acquisition system. The multiresolution wavelet transform was chosen to extract the P300 potential from the EEG signal because it provides information on both time and frequency domains. RESULTS: The multiresolution wavelet transform decomposes the signal in sub-bands and it helps to highlight the P300 potential. The spectrum of the P300 potential is around 3Hz. For the multiresolution wavelet decomposition this corresponds to coefficients of approximation of order 4 according to 0 to 60 Hz band of the original EEG signal. The representation of these coefficients emphasizes a better detection of P300 potential then in the original signal. CONCLUSION: It is shown to be a more appropriate method than the direct analysis of the signal because it works with lower dimensional signals. This method of detection of the P300 potential can be used successfully in the implementation of a Brain Computer Interface (BCI).

An alternative method for the removal of the ocular artifact from the EEG signal.

UNLABELLED: The aim of this paper is to develop a technique of rejection or minimization of ocular artifacts from the electroencephalographic (EEG) recordings. MATERIAL AND METHOD: The method presented is based on mathematical morphology. The algorithm and the individual structuring element are presented along with the particularities concerning the parameters that characterize the structuring element. RESULTS: The obtained results are shown in a comprehensive form by means of an illustrating example that evidences the efficiency of the method. CONCLUSIONS: The proposed method simplyfies the task of removing the ocular artifacts by means of nonlinear filtering and introduces a new structural element for the job. The results are validated by means of Fourier analysis and this clearly shows its effectiveness.

Detection of stationary segments in interictal electroencephalographic epileptic recordings.

The electroencephalographic (EEG) recording is frequently used to acquire both ictal and interictal epileptiform abnormalities. The objective of this paper is to detect the stationary parts of the interictal EEG signals. This is achieved by means of a lattice filter based on an optimal orthogonal linear prediction algorithm. It calculates recursively a set of reflection coefficients and a likelihood ratio test built on them is applied by means of a threshold linked to its statistical properties. The method is applied on three types of interictal recordings: signals with single spikes, sequences of spikes and signals with spikes and slow waves with comparable amplitudes. The best results were pointed out for the first and the last types. When dealing with spike-sequences, the algorithm reached its lowest rate of success. The proposed method permits an adequate segmentation and therefore facilitates the automatic interpretation of EEG before epileptic seizures.