Emotion has a significant influence on how one thinks and interacts with others. It serves as a link between how a person feels and the actions one takes, or it could be said that it influences one's life decisions on occasion. Since the patterns of emotions and their reflections vary from person to person, their inquiry must be based on approaches that are effective over a wide range of population regions. To extract features and enhance accuracy, emotion recognition using brain waves or EEG signals requires the implementation of efficient signal processing techniques. Various approaches to human-machine interaction technologies have been ongoing for a long time, and in recent years, researchers have had great success in automatically understanding emotion using brain signals. In our research, several emotional states were classified and tested on EEG signals collected from a well-known publicly available dataset, the DEAP Dataset, using SVM (Support Vector Machine), KNN (K-Nearest Neighbor), and an advanced neural network model, RNN (Recurrent Neural Network), trained with LSTM (Long Short Term Memory). The main purpose of this study is to improve ways to improve emotion recognition performance using brain signals. Emotions, on the other hand, can change with time. As a result, the changes in emotion over time are also examined in our research.

翻译：情感对人类思维方式和人际互动具有显著影响。它既是个人感受与行为反应之间的纽带，亦可视为影响人生重大决策的关键因素。由于不同人群的情感模式及其表现存在差异性，因此必须采用适用于广泛群体的有效研究方法。基于脑电信号（EEG）的情感识别需要通过实施高效的信号处理技术来提取特征并提升准确率。人机交互技术的多种研究路径已持续多年，近年来研究者利用脑电信号实现情感自动识别取得了重大突破。本研究采用支持向量机（SVM）、K近邻（KNN）算法以及基于长短时记忆（LSTM）训练的循环神经网络（RNN）高级模型，对公开数据集DEAP的脑电信号进行多类情感状态分类与测试。本研究的核心目标是优化基于脑电信号的情感识别方法。鉴于情感具有时变特性，本研究同时探索了情感状态随时间演变的规律。