## 11 Exploring Hybrid Approaches: Integrating Diverse Techniques
### Exploring Hybrid Approaches for Internal Artifact Removal: Case Studies  
Ref. [16] proposes a classification method based on Recurrent Neural Networks (RNN) to distinguish healthy subjects from those affected by [PERSON]'s disease using EEG data. Through the application of preprocessing techniques such as PCA, and especially robust PCA (RPCA) on corrupted data, the authors achieved an accuracy greater than 97% in the tests. RPCA, in particular, has been shown to improve accuracy by approximately 5% compared to standard PCA, even in the presence of a high rate of data corruption. These results pave the way for the use of this model for the early diagnosis of [PERSON]'s disease and suggest the possibility of extending the approach to various neurodegenerative diseases, emphasizing the effectiveness of advanced signal processing techniques in improving accuracy of classification based on brain signals. Ref. [135] describes an advanced method based on spatio-temporal Convolutional Neural Networks and Independent Component Analysis (ICA) for the automatic removal of artifacts from magnetic electroencephalography (MEG) signals. These artifacts, typically originating from eye blinks, saccades, and cardiac activity, are identified and eliminated without the use of additional electro-oculography (EOG) and electrocardiography (ECG) electrodes, thus simplifying imaging procedures and improving patient comfort. Using this approach, it was possible to achieve high accuracy in artifact classification, with results showing a detection accuracy of 98.95%, a sensitivity of 96.74%, and a specificity of 99.34% on a large sample of 217 subjects, both in resting conditions and during the execution of specific tasks. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Ref.** & **Domain** & **Method** & **Conclusions** \\ \hline
[16] & Ocular, muscle, cardiac & PCA and RNN & The accuracy on test data exceeds 97\%. Compared to baseline PCA, there is a consistent improvement of about 5\%, even when the corruption rate is significantly high, with deletions of up to 20\% in the input samples. \\ \hline
[135] & Ocular, muscle, cardiac & PCA and CNN & An exceptionally high accuracy was achieved in artifact classification, demonstrating an artifact detection accuracy of 98.95%. The system also exhibited a sensitivity of 96.74% and a specificity of 99.34% across data obtained both in the resting state and during specific tasks. \\ \hline
[136] & & The framework is engineered for high efficiency, characterized by its rapid processing capabilities and streamlined training processes. It excels in effectively removing internal artifacts from data, which enhances its reliability and performance in practical applications. Specifically, its robust design ensures quick and accurate artifact detection, crucial for environments demanding high precision. This capability ensures that the framework not only meets but exceeds standard performance metrics, making it especially suitable for high-stakes settings where speed and accuracy are paramount. \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Application of hybrid approaches for the removing of internal artifacts in EEG. This method represents a significant advance in the processing of MEG signals, providing an automatic and highly effective solution for cleaning data from non-neuronal interference. The ability to automatically adapt to acquisition time and the potential elimination of the need for EOG or ECG electrode monitoring promise dramatic improvements in the efficiency and accessibility of MEG imaging for both clinical and research applications. The study described in [136] extends the use of a robust adaptive noise cancellation scheme for the simultaneous removal of eye blinks, eye movements, amplitude drift, and recording bias. In particular, volumetric conduction was characterized, estimating the levels of signal propagation through all scalp EEG recording areas due to ocular artifact generators.