In recent decades, automatic human emotion detection plays a crucial role in human and machine interaction. Electroencephalograph (EEG) based human emotion detection is a challenging process due to the diversity, and complexity of human emotions. For recognizing diverse emotions, a novel model is presented in this paper. Initially, an average mean reference technique is used to eliminate the environmental artifacts, instrumentation artifacts, and biological artifacts from the EEG signals, which are collected from DEAP dataset. Next, feature extraction is carried out using Fast Fourier transform (FFT) with Power Spectral Density (PSD) to extract feature vectors from the denoised EEG signals. Further, feature dimensionality reduction is performed utilizing Principal Component Analysis (PCA) to diminish the dimensions of the extracted features. A total of 230 EEG feature vectors are given as the input to Artificial Neural Network (ANN) for classifying valence and arousal emotion states. The proposed PCA-ANN model performance is validated in terms of average classification accuracy and f-score. The experimental outcome demonstrates that the proposed PCA-ANN model achieved an improved accuracy in emotion classification, which is effective compared to the existing models such as ensemble learning algorithm, a convolutional neural network with the statistical method, and sparse autoencoder with logistic regression. The proposed PCA-ANN model achieved 87.14% and 86.31% of accuracy in valence and arousal states, and obtained 90.45% and 92.03% of f-score value in valence and arousal emotion states.

近几十年来，自动人类情感检测在人机交互中发挥着至关重要的作用。由于人类情绪的多样性和复杂性，基于脑电图 (EEG) 的人类情绪检测是一个具有挑战性的过程。为了识别不同的情绪，本文提出了一种新颖的模型。最初，平均参考技术用于消除 EEG 信号中的环境伪影、仪器伪影和生物伪影，这些信号是从 DEAP 数据集中收集的。接下来，使用具有功率谱密度（PSD）的快速傅里叶变换（FFT）进行特征提取，以从去噪的脑电信号中提取特征向量。此外，使用主成分分析 (PCA) 执行特征降维以减少提取特征的维度。总共有 230 个 EEG 特征向量作为人工神经网络 (ANN) 的输入，用于对效价和唤醒情绪状态进行分类。所提出的 PCA-ANN 模型性能在平均分类精度和 f 分数方面得到验证。实验结果表明，所提出的 PCA-ANN 模型在情感分类方面取得了更高的准确性，与集成学习算法、具有统计方法的卷积神经网络和具有逻辑回归的稀疏自动编码器等现有模型相比是有效的。所提出的 PCA-ANN 模型在效价和唤醒状态下的准确率分别为 87.14% 和 86.31%，在效价和唤醒情绪状态下的 f-score 值分别为 90.45% 和 92.03%。