Research on Artifact Removal in Brain-Computer Interfaces Based on Deep Learning Fusion Algorithm

As an emerging human-machine interaction technology, brain-computer interface (BCI) has broad application prospects. However, various artifacts existing in electroencephalogram (EEG) signals seriously affect the performance of BCI systems. This paper proposes a method based on a deep learning fusion algorithm to remove artifacts from EEG signals. This method combines convolutional neural networks (CNNs) and recurrent neural networks (RNNs), adopts a fusion strategy with a channel attention mechanism, and constructs a deep learning fusion model. Compared with traditional methods, the fusion algorithm proposed in this paper can effectively remove artifacts from EEG signals, providing a new solution for improving the accuracy and reliability of BCI systems.