## 2 Related Work  
Emotion classification using EEG signals involves several stages: emotional induction, signal acquisition, signal preprocessing, feature extraction, dimensionality reduction, emotion recognition, and classification. Emotional induction methods often rely on visual and auditory stimuli like emotional videos [21], music videos [1], or film clips [3; 22]. Feature extraction plays a crucial role in identifying emotions from EEG signals [23]. Prior studies have demonstrated a strong correlation between EEG emotions and EEG frequencies [24; 25]. Typically, EEG frequency features are extracted by mapping the EEG signals to Theta (4-7 Hz), Alpha (8-13 Hz), Beta (14-29 Hz), Gamma (30-47 Hz), and other frequency bands [26]. Dimensionality reduction and classification are also essential stages in the emotion classification pipeline [27]. Commonly used dimensionality reduction techniques include linear discriminant analysis (LDA) [28] and principal component analysis (PCA) [29].The key to accurate emotion recognition from EEG signals lies in extracting emotion-related features and optimizing the performance of the classifier. Deep learning and machine learning state-of-art techniques have been widely used for this purpose. [PERSON] et al. [30] conducted emotion classification on the DEAP dataset using Support Vector Machine (SVM) classifiers with both higher-order crossing (HOC) and power spectral density (PSD) features. The leave-one-subject-out classification approach achieved average accuracies of 53.42% for valence and 52.03% for arousal. [PERSON] et al. [31] used a support vector machine with the Gaussian kernel evaluated on the DEAP dataset. The accuracy scores obtained after using PCA achieved 60.59% for valence and 62.45% for arousal. [PERSON] et al. [28] applied PCA for feature extraction and implementedcovariance shift adaptation of the principal components, using a deep learning network (DLN) as the classifier, which resulted in accuracies of 49.52% and 46.03% for valence and arousal, respectively. Similarly, [PERSON] et al. [32] also adopted SVM for MAHNOB dataset emotion classification, obtaining 59.75% and 57.44% for arousal and valence, respectively. [PERSON] et al. [33] developed a multimodal emotion recognition method combining EEG and facial video clips. Using DCCA, they fused features extracted by ResNet50 (for facial video) and 1D-CNN (for EEG). Classification of happy, neutral, and sad emotions was performed using the SoftMax classifier. The method was validated on MAHNOB HCI and DEAP datasets. [PERSON] et al. [34] developed a model that integrates efficient channel attention into a combination of CNN and gated circulation units for emotion recognition on the DEAP dataset. [PERSON] et al. [35] proposed a CNN model for cross-subject emotion recognition and selecting the channels with the highest accuracy as the feature vector, the model is validated on the SEED dataset. Riemannian geometry has been exploited for emotion recognition [36]. Proposed the utilization of the Riemannian manifold and PCA for speech emotion recognition. [PERSON] et al. [37] implemented a visual-based emotion recognition model using the Riemannian manifold. [PERSON] et al. [7] developed a subject-dependent binary emotion classification of valence and arousal using transfer learning MDRM framework, and validated the model on the DEAP dataset. [PERSON] et al. proposed a Riemannian-based deep learning network that generates discriminative features for EEG classification on the DEAP dataset, achieving an accuracy of 55.2% [38]. [PERSON] et al. [39] proposed deep neural architecture, utilizing a deep long short-term memory (LSTM) network with a soft attention mechanism to learn temporal information from EEG signals and validated it on the SEED dataset. In this work, we introduce and validate an EEG-based emotion classification framework that utilizes Riemannian geometry, which integrates data normalization, dimensionality reduction, and hyperparameter tuning techniques.