- Akronim projektu:
- Program finansujący:
- Narodowe Centrum Nauki (NCN) (National Science Centre)
- UMO-2014/15/B/ST7/04724 z dnia 2015-07-16
- Okres realizacji:
- 2015-07-16 - 2019-03-15
- Kierownik projektu:
- prof. dr hab. inż. Andrzej Czyżewski
- Realizowany w:
- Katedra Systemów Multimedialnych
- Wartość projektu:
- 996 644.00 PLN
- Typ zgłoszenia:
- Krajowy Program Badawczy
- Projekt krajowy
- Politechnika Gdańska
Publikacje powiązane z tym projektem
Comparison of the effectiveness of automatic EEG signal class separation algorithmsPublikacja
In this paper, an algorithm for automatic brain activity class identification of EEG (electroencephalographic) signals is presented. EEG signals are gathered from seventeen subjects performing one of the three tasks: resting, watching a music video and playing a simple logic game. The methodology applied consists of several steps, namely: signal acquisition, signal processing utilizing z-score normalization, parametrization and...
Method for Clustering of Brain Activity Data Derived from EEG SignalsPublikacja
A method for assessing separability of EEG signals associated with three classes of brain activity is proposed. The EEG signals are acquired from 23 subjects, gathered from a headset consisting of 14 electrodes. Data are processed by applying Discrete Wavelet Transform (DWT) for the signal analysis and an autoencoder neural network for the brain activity separation. Processing involves 74 wavelets from 3 DWT families: Coiflets,...
Automatic Clustering of EEG-Based Data Associated with Brain ActivityPublikacja
The aim of this paper is to present a system for automatic assigning electroencephalographic (EEG) signals to appropriate classes associated with brain activity. The EEG signals are acquired from a headset consisting of 14 electrodes placed on skull. Data gathered are first processed by the Independent Component Analysis algorithm to obtain estimates of signals generated by primary sources reflecting the activity of the brain....
Comparison of Methods for Real and Imaginary Motion Classification from EEG SignalsPublikacja
A method for feature extraction and results of classification of EEG signals obtained from performed and imagined motion are presented. A set of 615 features was obtained to serve for the recognition of type and laterality of motion using 8 different classifications approaches. A comparison of achieved classifiers accuracy is presented in the paper, and then conclusions and discussion are provided. Among applied algorithms the...
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