## 1 Introduction
The Riemannian framework can prevent this by accounting for the unique geometry of the space of covariance matrices while processing these matrices in their native space, allowing a direct implementation of the Frechet mean algorithm [10]. Moreover, the Riemannian structure induced by the affine-invariant Riemannian metric (AIRM), which ensures that the distance between these different representations of covariance matrices is equivalent [20], overcomes the constraints of Euclidean geometry in exchange for increased computation cost as the size of the manifold increases [17]. Despite the recent advancements in emotion recognition using facial expressions, speech, and EEG signals, our understanding of human emotions remains insufficient. This highlights the need for new innovations in human-computer interfaces (HCIs) to gain deeper insights into human emotional responses to specific stimuli. Emotion recognition using Riemannian metrics currently lacks comprehensive comparative studies across multiple datasets as a benchmark. Moreover, the existing work in emotion classification predominantly focuses on a single dataset, which limits its translational applicability. Additionally, the challenge of handling intra-subject variability, where the features of interest vary between subjects and instances, has not been adequately addressed. To address these limitations, this study aims to investigate and compare the performance of classification methods within the Riemannian framework, traditional feature selection, and a novel dynamic feature selection approach across four diverse datasets. The primary objective is to validate the robustness and generalizability of the proposed method, enabling its potential application in real-world scenarios. Our work aims to develop a dynamic feature selection framework within the minimum distance to Riemannian mean (DFS-MDRM) approach for classifying human emotionsand investigating the impact of feature selection on model performance. The framework involves two main steps. In the first step, we preprocess EEG signals by applying filtering and normalization techniques, followed by the utilization of shrinkage covariance matrix estimation to transform the extracted features onto the Riemannian manifold. In the second step, we employ the SPD matrices on the manifold to perform supervised classification using the MDRM method. This entails estimating the mean of each class using the Frechet mean and computing distances on the manifold using AIRM. To evaluate the effectiveness of our approach, we conducted four experiments across five frequency bands. Validation was performed using leave-one-out cross-validation (LOOCV), where we employed dynamic principal component analysis (PCA) to identify the most appropriate features for classifying each test trial, ensuring adaptability across different datasets and mitigating the impact of variability in observations. The approach presented in this work leverages the advantages of the Riemannian framework to enhance classification accuracy. It better accounts for the specific geometry of the space of covariance matrices reduces the impact of irrelevant features, and optimizes the model parameters. In this work, we make two main contributions:  
1. [label=(0)]
2. Integration of traditional feature extraction techniques, specifically principal component analysis (PCA), into a dynamic feature extraction process. By representing the extracted features as covariance matrices and leveraging their distinctive characteristics in the Riemannian manifold space, our proposed method effectively addresses variabilities observed across different instances. 3. Demonstration of the generalizability and robustness of the proposed method through a successful application to four well-known datasets with varying characteristics. The achieved results outperformed state-of-the-art methods, highlighting the considerable potential of this approach for practical applications.