## 6 Results and Discussion
### Example of EEG Signal Cleaning for Various Muscle Artifacts  
To evaluate how well the proposed method handles more complex EEG signals containing multiple, diverse artifacts, additional experiments were conducted. The EEG was recorded while participants were free to perform any muscle artifacts--or combinations  
Figure 13: Comparison of average SNR\({}_{\text{SSVEP}}\) between raw EEG and the CNN-LSTM method for one-second EEG signal windows containing jaw-clenching artifacts and artifact-free segments.  
thereof--over a one-minute period. Users combined artifacts such as neck muscle tension, jaw clenching, tongue movements, intense jaw clenching, cheek tension, eye movements, forehead wrinkling, and many others. This generated a mix of artifacts from which it was impossible to isolate a single source of interference. Participants were also asked to perform the artifacts with maximum intensity, posing a significant challenge for the artifact removal algorithm. Figure 14 shows four EEG channels recorded while one participant was performing these artifacts, and Figure 15 presents the corresponding EEG signals after cleaning with CNN-LSTM method. Both figures are presented on the same amplitude scale. It can be observed that the CNN-LSTM performed remarkably well in eliminating artifacts, even though the algorithm was trained solely on one type of artifact--jaw clenching. The amplitude of the artifacts was significantly reduced. This challenging scenario is particularly relevant to EEG signal analysis in epileptic seizures. These results indicate that the proposed algorithm and method have substantial potential for practical applications.  
In the experiment involving \"various muscle artifacts\", no visual stimulation (SSVEP) was used, which means the EEG signal lacked evoked potentials that could serve as a reference for evaluating the effectiveness of artifact removal. Simultaneously performing a wide range of artifacts (e.g., eye movements, jaw clenching, neck tension, and facial grimacing) while maintaining steady visual focus on a stimulator is extremely difficult and practically unfeasible. For this reason, only visual inspection was applied, with the same amplitude scale maintained before and after artifact removal, allowing for a qualitative comparison of signal distortion levels. The observed reduction in amplitude and the restoration of characteristic EEG waveforms suggest that the model effectively removes even complex artifacts, despite being trained solely on jaw clenching data. It is worth emphasizing that the data used to train the model underwent extensive augmentation, characterized by high variability. During the generation of synthetic data, many parameters--such as amplitude, temporal structure, and frequency spectrum--were randomly modified. As a result, the training set was significantly more diverse than real-world examples of muscle artifacts originating solely from jaw clenching. Moreover, since muscle artifacts  
Figure 14: Four EEG channels recorded while performing artifacts by subject 24.  
typically have a broad frequency spectrum and irregular characteristics, the model may have learned general features of noise--independent of its source or amplitude. As a result, its behavior may resemble regression, where the model learns the relationship between contaminated and clean signals. This enables the effective removal of previously unseen artifact types--without the need for domain adaptation or transfer learning.