Using the DEAP dataset, this conference paper aims to investigate the effectiveness of K-Nearest neighbours (KNN) and Multilayer Perceptron (MLP) classifiers in the context of emotion recognition. The primary focus is on decoding valence-arousal emotions, with special attention to how EEG bands are represented in relation to time and channels. The study comprises pre-processing and feature extraction from the multimodal DEAP dataset, which contains EEG signals associated with emotional responses. The Valence-Arousal model is chosen because it may be used to capture the essential elements of affective experiences. KNN and MLP, two distinct classifiers, are used to assess how well they distinguish emotions from EEG signals. The efficacy of these classifiers is evaluated using a range of metrics, such as accuracy, precision, re-call, and F1-score, which offer a thorough grasp of both their benefits and drawbacks. The study also examines the representation of EEG bands across time and across channels in order to find trends and connections in emotional responses. This means taking a close look at the ways in which different frequency bands help distinguish between Valence-Arousal emotions and offer insights into the temporal and spatial dynamics of emotional processing. The EEG data processing process employed the Multi-layer Perceptron (MLP) and k-Nearest neighbours (KNN) algorithms to assess the precision of the Arousal and Valence classifications in different regions of the brain. In the left region, KNN outperformed MLP in terms of arousal (69.11% vs. 60.16%) and valence (69.35% vs. 62.89%). Similarly, KNN scored better than MLP in the parietal region in terms of accuracy for valence (66.67%) and arousal (68.29% vs. 60.16%). In the middle region, KNN outperformed MLP in terms of valence accuracy (72.34% vs. 65.77%) whereas MLP had a lower arousal accuracy (57.72% vs. 68.29%). These results emphasise how crucial it is to take into account technique choice as well as brain regions when evaluating EEG data related to affective states. The findings may have an impact on the creation of more accurate and efficient emotion recognition systems, which may have an impact on applications in affective computing, human-computer interface, and medicine.