Detail publikace

The application of deep learning techniques in the electroencephalogram (EEG) analysis

Originální název

The application of deep learning techniques in the electroencephalogram (EEG) analysis

Anglický název

The application of deep learning techniques in the electroencephalogram (EEG) analysis

Jazyk

en

Originální abstrakt

In this paper, we show in a nutshell a characteristic of EEG signal, approaches in the analysis of this biosignal and the possible usage in the diagnosis. It will be depicted also the recent application of deep learning methods likewise the advantages of new type of neural networks: neural ordinary differential equations (ODE). ODE networks seems to be great promise to EEG analysis and its accuracy.

Anglický abstrakt

In this paper, we show in a nutshell a characteristic of EEG signal, approaches in the analysis of this biosignal and the possible usage in the diagnosis. It will be depicted also the recent application of deep learning methods likewise the advantages of new type of neural networks: neural ordinary differential equations (ODE). ODE networks seems to be great promise to EEG analysis and its accuracy.

BibTex


@inproceedings{BUT159890,
  author="Justyna {Skibińska} and Radim {Burget}",
  title="The application of deep learning techniques in the electroencephalogram (EEG) analysis",
  annote="In this paper, we show in a nutshell a characteristic of EEG signal, approaches in the analysis of this biosignal and the possible usage in the diagnosis. It will be depicted also the recent application of deep learning methods likewise the advantages of new type of neural networks: neural ordinary differential equations (ODE). ODE networks seems to be great promise to EEG analysis and its accuracy.",
  address="Tampere University, Hervanta Campus",
  chapter="159890",
  doi="10.5281/zenodo.3532972",
  howpublished="online",
  institution="Tampere University, Hervanta Campus",
  year="2019",
  month="october",
  pages="1--4",
  publisher="Tampere University, Hervanta Campus",
  type="conference paper"
}