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Start Over Subject P300

1. Cognitive evoked potentials related to visual perception of motion in human subjects

Creator:
Kuba, M., Kremláček, J., and Kubová, Z.
Type:
article, model:article, and TEXT
Subject:
cognitive evoked potentials, P300, visual motion stimulation, and visual perception of motion
Language:
English
Description:
A method was tested for simultaneous recordings of evoked potentials from the secondary visual cortex (medio- temporal) and from the brain cognitive areas (fronto-central). Visual moving stimulations with cognitive tasks seem to be suitable for combined examination of visual motion perception and cognitive processes based on the magnocellular system activity. This arrangement enhances the analysis of visual information processing and evaluation of central nervous system functions.
Rights:
http://creativecommons.org/licenses/by-nc-sa/4.0/ and policy:public

2. 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