## 5 Discussion and Future Work  
Understanding and achieving desirable results in the field of emotion recognition remains a challenging task, presenting opportunities for further innovation and investigation. Although previous studies [7; 28; 30; 32; 34; 38] have demonstrated satisfactory results, there are still critical issues yet to be addressed, such as robustness, generalizability, and handling of intra-subject variability and observation variations. To address these gaps, this work aims to develop a robust and flexible pipeline for emotion recognition that can be applied to real-world data, while also mitigating the impact of these challenges. The proposed approach leverages Riemannian geometry to classify emotions by preprocessing raw EEG data and reducing its dimensionality using PCA [29]. The preprocessed data are represented as covariance matrices, on which Riemannian metrics and algorithms are applied for classification. The results showed that the utilization of Riemannian geometry alone improved the classification accuracy for the valence class compared to a CNN model on the DEAP dataset by 0.76%, and for the MAHNOB dataset, the accuracy improved by 6% compared to KNN. However, the accuracy for M1, the baseline model, remained slightly lower than that of state-of-the-art algorithms. To overcome this limitation, we introduced M2, which incorporates traditional PCA in addition to the MDRM approach. By integrating PCA, we aimed to capture relevant information and reduce noise in the EEG signal representations. The results across the DEAP, DREAMER, and MAHNOB datasets demonstrated improved accuracy compared to M1 as shown in Figure 9.  
Building upon the improvements of M2, M3 introduced dynamic PCA, enabling adaptive feature extraction and a more effective representation of EEG signal variations. The results demonstrated that M3 consistently achieved higher accuracy compared to both M1 and M2. Notably, in the DEAP dataset, the accuracy increased by 6% and 2% for the valence and arousal classes, respectively. The DREAMER dataset exhibited a similar pattern, with accuracy improvements of 6.48% and 1.1% for the arousal and valence classes, respectively. In the MAHNOB dataset, M3 yielded accuracy improvements of 3.6% for arousal and 5% for valence. The SEED dataset showcased substantial enhancement, with a classification accuracy improvement of 12.4%. These consistent improvements across the datasets, combined with the notable performance compared to state-of-the-art machine learning  
Figure 9: Performance (classification accuracy) comparison of the three methods proposed in this work, compared with the results from using KNN and CNN applied to the same datasets. methods, particularly in the classification of the SEED dataset, underscore the effectiveness of our proposed approach. While M1 may yield lower accuracy compared to the state-of-the-art methods, it serves the purpose of establishing a baseline and showcasing the potential of Riemannian geometry in EEG signal analysis. The progression from M1 to M3 highlights the significance of integrating these methodologies to enhance classification performance. To assess the generalizability of the model, we evaluated it across different datasets with diverse characteristics, including DEAP, DREAMER, SEED, and MAHNOB, demonstrating its robustness and adaptability for emotion classification. Furthermore, the results confirmed the effectiveness of using the Riemannian mean and AIRM in Riemannian space, as they are designed to operate on covariance matrices, making computations more efficient and accurate compared to other feature spaces [47]. These findings highlight the effectiveness of Riemannian geometry for emotion classification and pave the way for further brain-machine interface (BMI) research in this area. While this study provides valuable insights, there are limitations to consider. The data used were pre-selected based on specific criteria to avoid imbalanced data, resulting in a potentially sparse dataset.