| Περίληψη: | The last twenty years presented increased interest for the study of cerebral processes caused by external events (stimuli). One of the most significant endogenous components of Evoked Potentials is the P600 component. The P600 component may be defined as the most positive peak in the time window between 500 and 800 msec after an eliciting stimulus. This component is thought to reflect the response selection stage of information processing. P600 component is usually less pronounced compared to other components, such as the N100 or the P300. Frequently the P600 component appears as a not-easily discernible secondary peak overlying the ascending negative-going slope of the P300 waveform. In our study we used ERP data from various groups of patients and healthy controls. Patients were recruited from the outpatient university clinic of Eginition Hospital of the University of Athens. The controls were recruited from hospital staff and local volunteer groups. The aim of the study is the implementation of classification systems for these groups, using P600 features. This is usually not achieved well using as features the ERPs amplitude and latency. So for that reason, in our study, we want to extract new features using advanced techniques for processing the original ERPs, such as the Independent Component Analysis (ICA) method. However as a precursor of ICA, is considered the Principal Component Analysis (PCA) method, which we used for comparison reasons to ICA.
In the application of ICA we achieve the decomposition of the recorded signals in ICs, supposing temporally independent components and propose ICs selection techniques in order to recompose the P600 component. The next stage was the use of a classification method based on the features extracted using the original data, data extracted through PCA processing and ICA-processed data. First we applied Kolmogorov-Smirnov test to check the normality of the distribution of the features, then we used the Logistic Regression method for classification and finally we have done two implementations of classification using Probabilistic Neural Networks. The first implementation was done with the creation of 15 features from the P600 peak amplitudes from the subjects’ data and the second implementation was done with the creation of four meta-features from the subjects’ P600 amplitude data.
The results show that the application of ICA, combined with the logistic regression classification technique, provides notable improvement, compared to the classification performance based on the original ERPs. The main merit of the application is that classification is based on single parameters, i.e. amplitude of the P600 component, or its latency or its termination latency, which are directly related to the brain mechanisms related to ERP generation and pathological processes.
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