## 5 Discussion  
Analyzing the factors affecting the performance of EEG signal processing and classification systems involves considering multiple aspects of system design, data quality, and method implementation. One crucial factor is dataset quality and size. A larger and cleaner dataset often leads to better model performance. Small or noisy datasets can result in overfitting or poor generalization. To improve, we could increase the dataset size through data augmentation techniques or collect additional data. Enhancing data quality by applying robust preprocessing methods to reduce noise and artifacts also contributes significantly to better performance. Feature extraction techniques play a pivotal role in determining system performance. Different methods, such as wavelet transforms, ICA, or PCA, affect the relevance and quality of the features used for classification. Experimenting with various feature extraction techniques, including hybrid approaches, can help identify the most effective ones for our specific dataset. Additionally, exploring deep learning-based feature extraction methods may offer significant improvements if computational resources permit. The choice of classification algorithms is another critical factor. Algorithms such as SVMs, neural networks, or decision trees have varying effectiveness depending on the data and features. Testing a range of classification algorithms and fine-tuning their parameters can lead to better results for our proposed work, which we will implement for our future work. Utilizing ensemble methods to combine predictions from multiple classifiers can further enhance accuracy and robustness. Preprocessing and denoising techniques are essential for ensuring high-quality  
Figure 34: Performance comparisons of the three models in performing intersubject classification, using data from participants 1 to 11.  
input data. Advanced preprocessing methods, such as adaptive filtering and artifact removal, can significantly improve the signal quality. Implementing these techniques helps in enhancing the effectiveness of the classification system. Model complexity can also impact performance. Overfitting is a common issue with complex models, especially if the dataset is small or not diverse enough. To address overfitting, we could explore using cross-validation, regularization, and dropout techniques for our future work. Simplifying models may also help in achieving better generalization. The classification accuracy in this proposed work reveals several factors that influence performance compared to recent research using similar datasets and methods. Differences in accuracy can be attributed to various elements, including dataset characteristics, feature extraction methods, model architectures, and training strategies. For instance, dataset size, quality, and diversity play a crucial role in performance. Smaller or less diverse datasets may lead to overfitting or inadequate model generalization. The choice of feature extraction techniques, such as wavelet transforms or ICA, can also impact accuracy, with some methods better capturing relevant patterns in the data than others. Model architecture and algorithm choices significantly affect performance. Variations between models such as SVMs, CNNs, and RNNs can lead to different accuracy outcomes, as each model type has its strengths and limitations depending on the data and task. Additionally, training procedures, including hyperparameter tuning and cross-validation strategies, can contribute to performance discrepancies. Effective training practices and robust validation approaches are essential for optimizing model accuracy. Several factors affect performance, including data quality and preprocessing. Issues such as noise, artifacts, and incorrect labeling can degrade accuracy, highlighting the importance of careful data handling and preprocessing. Model complexity also plays a role; while more complex models may capture intricate patterns, they can also be prone to overfitting if not properly managed.