## 5 EEG Signal Processing and Classification Techniques in Rehabilitation Research
### Performance Indicators and Metrics in Evaluating the Effectiveness of Methods  
In assessing the efficacy of neural rehabilitation methods utilizing EEG, a rich array of performance indicators comes to the forefront, each offering a unique lens through which the impact of these interventions can be comprehensively evaluated [11; 13; 14; 19; 38; 31; 13]. 1. Classification Accuracy: The efficacy of EEG-based models is scrutinized through the lens of classification accuracy, providing insights into their ability to distinguish  
Figure 9: Diagram of a Multi-Layer Perceptron. between different classes or states in activities like differentiating motor imagery or identifying cognitive states [11; 13; 14; 19; 31; 38; 38]. 2. ROC Curve and AUC: The evaluation of the balance between specificity and sensitivity in classification assignments is facilitated through the utilization of ROC curves and AUC values, adding a layer of sophistication to the assessment process [17; 38]. 3. MSE or RMSE: In the realm of regression functions, the metrics of Mean Squared Error (MSE) or Root Mean Squared Error (RMSE) emerge as crucial, providing a nuanced measure of the accuracy of predictions by quantifying the difference between predicted and actual values [13; 31]. 4. R-squared (R2): The extent to which the regression model fits the data is gauged through the lens of R-squared (R2), offering valuable insights into the predictive power of the model [49; 50]. 5. Real-time Performance Measurements: In situations necessitating instantaneous responsiveness, parameters such as response latency, response time, and overall system latency offer a comprehensive evaluation of the system's real-world applicability [26; 55]. 6 Present Constraints in the Ongoing Research on Rehabilitation Utilizing EEG with ML and DL Methods  
While the landscape of EEG-based neural rehabilitation research with machine learning and deep learning showcases remarkable progress, it is essential to acknowledge and navigate through existing limitations and gaps, each presenting a unique challenge to the advancement of this field. 1. Noise and Artifacts: Concerns surrounding data quality, preprocessing methodologies, and the standardization of data gathering protocols cast a spotlight on the imperative need to address these aspects to ensure the reliability and consistency of results. 2. Small Sample Sizes: The challenges when acquiring high-quality EEG data from groups of patients contribute to the prevalence of small sample sizes, potentially leading to model overfitting and hindering the generalizability of findings. 3. Longitudinal EEG Datasets: The scarcity of longitudinal EEG datasets poses a significant hurdle in monitoring progress during neural rehabilitation. A dedicated focus on long-term research is indispensable for comprehensively understanding the effectiveness of diverse approaches and customizing interventions and treatments accordingly. 4. Interpretability of Deep Learning: The opaque nature of deep learning models poses challenges in interpreting results, necessitating research that seamlessly combines deep learning methodologies with insights from neuroscience. This integration is crucial for gaining a deeper understanding of the fundamental neurophysiological mechanisms associated with brain rehabilitation. 5. Ethical Concerns in Real-Time Applications: While offline analysis dominates several EEG-based brain rehabilitation techniques, the shift towards real-time applications introduces ethical considerations related to patient consent, data ownership, and privacy. Meticulous attention is required to ensure that these issues are addressed with the utmost care, respecting patients' rights. 6. Bridging the Gap Between Research and Clinical Implementation: Despite the strides made in research in neurological rehabilitation utilizing EEG signals, there remains a discernible discrepancy between academic research and the practical implementation of clinical solutions.