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2. Coherence of EEG Signals and Biometric Signals of Handwriting under Influence of Nicotine, Alcohol, and Light Drugs
- Creator:
- Maršálek, Tomáš, Matoušek, Václav, Mautner, Pavel, Merta, Michal, and Mouček, Roman
- Format:
- bez média and svazek
- Type:
- model:article and TEXT
- Subject:
- computerized EEG signal evaluation, biometric signals, and signal coherence
- Language:
- English
- Description:
- Subject matter of investigations being carried out at the University of West Bohemia in Pilsen and described in this chapter is the objective evaluation of possible coherence of EEG signals and signals of handwriting generated by special developed BiSP pen. The influence of nicotine, alcohol and light drugs on the vigility and activity of human operators was investigated and evaluated; the results of the experiments being realized during the last five months are summarized in the last paragraph.
- Rights:
- http://creativecommons.org/publicdomain/mark/1.0/ and policy:public
3. Modifications of unsupervised neural networks for single trial P300 detection
- Creator:
- Vařeka, Lukáš and Mautner, Pavel
- Format:
- bez média and svazek
- Type:
- model:article and TEXT
- Subject:
- brain-computer interfaces, event-related potentials, P300, adaptive resonance theory, self-organizing maps, Fuzzy ARTMAP, and Bayesian Linear discriminant analysis
- Language:
- English
- Description:
- P300 brain-computer interfaces (BCIs) have been gaining attention in recent years. To achieve good performance and accuracy, it is necessary to optimize both feature extraction and classification algorithms. This article aims at verifying whether supervised learning models based on self-organizing maps (SOM) or adaptive resonance theory (ART) can be useful for this task. For feature extraction, the state-of-the-art Windowed means paradigm was used. For classification, proposed classifiers were compared with state-of-the-art classifiers used in BCI research, such as Bayesian Linear Discriminant Analysis, or shrinkage LDA. Publicly available datasets from fifteen healthy subjects were used for the experiments. The results indicated that SOM-based models yield better results than ART-based models. The best performance was achieved by the LASSO model that was comparable to state-of-the-art BCI classifiers. Further possibilities for improvements are discussed.
- Rights:
- http://creativecommons.org/publicdomain/mark/1.0/ and policy:public
4. Processing and categorization of Czech written documents using neural networks
- Creator:
- Mautner, Pavel and Mouček, Roman
- Format:
- bez média and svazek
- Type:
- model:article and TEXT
- Subject:
- Document categorization, WEBSOM, SOM, ART-2, neural networks, and document semantics
- Language:
- English
- Description:
- The Kohonen Self-organizing Feature Map (SOM) has been developed for clustering input vectors and for projection of continuous high-dimensional signal to discrete low-dimensional space. The application area, where the map can be also used, is the processing of text documents. Within the project WEBSOM, some methods based on SOM have been developed. These methods are suitable either for text documents information retrieval or for organization of large document collections. All methods have been tested on collections of English and Finnish written documents. This article deals with the application of WEBSOM methods to Czech written documents collections. The basic principles of WEBSOM methods, transformation of text information into the real components feature vector and results of documents classification are described. The Carpenter-Grossberg ART-2 neural network, usually used for adaptive vector clustering, was also tested as a document categorization tool. The results achieved by using this network are also presented.
- Rights:
- http://creativecommons.org/publicdomain/mark/1.0/ and policy:public