Classification of EEG signals at brain-computer interfaces using neural networks
DOI:
https://doi.org/10.34767/SIMIS.2024.03.03Keywords:
Brain-computer interface, EEG, Neural networks, Signal classification, Signal processingAbstract
The aim of this paper is to present methods for classifying EEG signals in brain-computer interfaces (BCIs) using neural networks. Thanks to their ability to model complex relationships in the data, it is possible to recognise patterns of brain activity more effectively, which contributes to improving the accuracy and speed of BCI systems. This paper discusses neural network architectures used to analyse EEG signals, such as convolutional networks (CNNs) or recurrent networks (RNNs). The research shows that these methods have immense potential in applications such as assistive device control, communication, and entertainment.
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