Neural networks in recognition of handwriting

Artificial neural networks consist of many simple elements capable of processing data. These are tools inspired by the construction of the human brain, used in machine learning. The aim of the research was to analyze the occuracy of the created neural network in the process of handwriting recognition. The article presents the results obtained during the learning and testing of a convolution network with a different number of hidden layers. Each time learning and testing the network was carried out using the same set of images (taken from the publicly available IAM database) depicting handwritten words in English

Comparative analysis of two-group supervised classification algorithms in the study of P300-based brain-computer interface

The main goal of the paper is to perform a comparative accuracy analysis of the two-group classification of EEG data collected during the P300-based brain-computer interface tests. The brain-computer interface is a technology that allows establishing communication between a human brain and external devices. BCIs may be applied in medicine to improve the life of disabled people and as well for entertainment. The P300 is an event-related potential (ERP) appearing about 300 ms after the occurrence of the stimulus of visual, auditory or sensory nature. It is based on the phenomenon observed in anticipation for a target event among non-target events. The 21-channel 201 Mitsar amplifier was used during the experiment to store EEG data from seven electrodes placed on the dedicated cap. The study was conducted on a group of five persons using P300 scenario available in OpenVibe software. The experiment was based on three steps the classifier learning process, comparison and averaging of the obtained result and the final test of the classifier. The comparative analysis was performed with the application of two supervised classification methods: Linear Discriminant Analysis (LDA) and Multi-layer Perceptron (MLP). The preliminary data analysis, extraction and feature selection was performed prior to the classification

Comparative analysis of two-group supervised classification algorithms in the study of P300-based brain-computer interface

The main goal of the paper is to perform a comparative accuracy analysis of the two-group classification of EEG data collected during the P300-based brain-computer interface tests. The brain-computer interface is a technology that allows establishing communication between a human brain and external devices. BCIs may be applied in medicine to improve the life of disabled people and as well for entertainment. The P300 is an event-related potential (ERP) appearing about 300 ms after the occurrence of the stimulus of visual, auditory or sensory nature. It is based on the phenomenon observed in anticipation for a target event among non-target events. The 21-channel 201 Mitsar amplifier was used during the experiment to store EEG data from seven electrodes placed on the dedicated cap. The study was conducted on a group of five persons using P300 scenario available in OpenVibe software. The experiment was based on three steps the classifier learning process, comparison and averaging of the obtained result and the final test of the classifier. The comparative analysis was performed with the application of two supervised classification methods: Linear Discriminant Analysis (LDA) and Multi-layer Perceptron (MLP). The preliminary data analysis, extraction and feature selection was performed prior to the classification