A Critical Analysis on EEG Signal Processing for BCI Applications

An overview of the brain-computer interface is given in this publication and the variety of deep learning architectures for the acquisition of brain signals we have discussed the EEG signals that are used in BCI applications. Time-frequency localization is frequently poor in EEG signals. As a result, BCI systems frequently have low task detection accuracy and high error rates. It also exhibits extremely quasi qualities and has a lot of trans variances. Additionally, we have discussed noise removal techniques given by different researchers from EEG signals and BCI applications, and the comparison is given further than we have discussed regarding the EEG signal's image retrieval and different deep learning techniques for EEG learning commons. From the analysis of the results of different approaches, it's been noticed that non-stationary EEG signals are more contributing to BCI applications. Whereas, in pre-processing steps, SWT-ICA, DWT, and CSP algorithms are most efficient for noise removal. For feature extraction sliding window spatial and temporal methods and deep learning methods contributed the most. Finally, for feature learning and classification, transfer learning and fine-tuned model performance were analyzed. It was observed from the analytical review that the fine-tuned transfer learning model outperforms better.