Classifying electrical activity of the brain during imaginary movements of untrained subjects using artificial neural networks

Introduction: Developing new classification methods for human brain electrical activity patterns corresponding to actual movements or motor imagery is an essential interdisciplinary problem in brain-computer interface research. One of the most promising approaches is the development of methods based on artificial neural networks. Purpose: The development of ANN-based methods for classifying electroencephalographic patterns associated with motor imagery in untrained subjects. Methods: Classifiers based on linear neural networks, multi-layer perceptrons, radial basis function networks and support vector machines. Results: The authors selected the optimal type, topology, learning algorithms and parameters of an artificial neural network in order to provide the most accurate and fast classification of lower limb motor imagery EEG signals. It has been studied how the number of the analyzed channels of a multichannel EEG and their choice affect the quality of motor imagery patterns classification. Optimal configurations were obtained for the electrode arrangements. The influence of EEG pre-processing on the accuracy of motor imagery recognition was analyzed. A computational experiment showed the accuracy of 90-95% in untrained subjects. Radial basis function network demonstrated the best performance. Besides, the dataset dimensionality has been significantly reduced down to 6–12 channels without any classification accuracy loss. Practical relevance: The obtained results can be useful for the developers of motor imagery EEG classification algorithms used in brain-computer interfaces.

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Application of Radial Basis Function Network with a Gaussian Function of Artificial Neural Networks in Osmo-dehydration of Plant Materials

Journal of Artificial Intelligence ◽

10.3923/jai.2011.233.244 ◽

2011 ◽

Vol 4 (4) ◽

pp. 233-244 ◽

Cited By ~ 5

Author(s):

C. Tortoe ◽

J. Orchard ◽

A. Beezer ◽

J. Tetteh

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Radial Basis Function ◽

Basis Function ◽

Radial Basis Function Network ◽

Gaussian Function ◽

Plant Materials ◽

Radial Basis ◽

Artificial Neural ◽

Function Network

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Improving the modeling and forecasting of fuel selling price using the radial basis function technique: A case study

Journal of Algorithms & Computational Technology ◽

10.1177/1748302619881120 ◽

2019 ◽

Vol 13 ◽

pp. 174830261988112

Author(s):

Zineb Aman ◽

Latifa Ezzine ◽

Younes Fakhradine El Bahi ◽

Haj EL Moussami

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Radial Basis Function ◽

Basis Function ◽

Radial Basis Function Network ◽

Arima Model ◽

Function Technique ◽

Selling Price ◽

Radial Basis ◽

Artificial Neural

Recently, the petroleum sector in Morocco has been liberalized which has a significant effect for petroleum product distributors. Since the beginning of December 2015, fuel prices are freely determined. This event presents many constraints affecting the balance of the sector plus the competition between its economic players. The lack of accompanying measures by the State makes this vital reform for public finances that stop subsidizing the price of gasoline vulnerable. As all fuel products are imported, we will be interested in the evolution by making forecasts of the price of fuels in the Moroccan market. In this context, our paper aims mainly to study the selling price of diesel and gasoline in order to provide precise forecasts to the company and to respect the permissible error margin of 3%. To this end, we worked with a widely used approach for price forecasting: artificial neural networks technique (radial basis function). Recently, it is suggested to work with artificial neural networks in forecasting field as an alternative to the traditional linear methods. We developed a radial basis function network to come up with conclusions in terms of the superiority in forecasting performance. Consequently, the radial basis function technique proved its strength manifested in the error that was further minimized: 1.95% instead of 2.85% for autoregressive integrated moving average (ARIMA) model used in our previous work. The error is further minimized by applying radial basis function technique.

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