Neural Network Model for Artifacts Marking in EEG Signals

One of the main methods for research of the holistic activity system of human brain is the method of electroencephalography (EEG). For example, eye movements, blink, hearth activity, muscle activity that affects EEG signal interfere with cerebral activity. The paper describes the development of an intelligent neural network model aimed at detecting the artifacts in EEG signals. The series of experiments were conducted to investigate the performance of different neural networks architectures for the task of artifact detection. As a result, the performance rates for different ML methods were obtained. The neural network model based on U-net architecture with recurrent networks elements was developed. The system detects the artifacts in EEG signals using the model with 128 channels and 70% accuracy. The system can be used as an auxiliary instrument for EEG signal analysis.

@article{Komisaruk2021,
  title     = {Neural Network Model for Artifacts Marking in EEG Signals},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {12},
  number    = {12},
  year      = {2021},
  publisher = {The Science and Information Organization},
  author    = {Olga Komisaruk and Evgeny Nikulchev},
  doi       = {10.14569/IJACSA.2021.0121204},
  url       = {https://doi.org/10.14569/IJACSA.2021.0121204}
}