Electroencephalogram (EEG) is a very promising and widely implemented procedure to study brain signals and activities by amplifying and measuring the post-synaptical potential arising from electrical impulses produced by neurons and detected by specialized electrodes attached to specific points in the scalp. It can be studied for detecting brain abnormalities, headaches, and other conditions. However, there are limited studies performed to establish a smart decision-making model to identify EEG's relation with the mood of the subject. In this experiment, EEG signals of 28 healthy human subjects have been observed with consent and attempts have been made to study and recognise moods. Savitzky-Golay band-pass filtering and Independent Component Analysis have been used for data filtration.Different neural network algorithms have been implemented to analyze and classify the EEG data based on the mood of the subject. The model is further optimised by the usage of Blackman window-based Fourier Transformation and extracting the most significant frequencies for each electrode. Using these techniques, up to 96.01% detection accuracy has been obtained.

翻译：脑电图（EEG）是一种非常有前景且广泛应用的程序，通过放大和测量由神经元产生的电脉冲所引起的突触后电位，这些电位由附着在头皮特定点上的专用电极检测，从而研究大脑信号和活动。它可以用于检测脑部异常、头痛和其他状况。然而，目前建立智能决策模型以识别脑电图与受试者情绪关系的相关研究仍然有限。在本实验中，经过同意，观察了28名健康人类受试者的脑电图信号，并尝试研究和识别情绪。使用Savitzky-Golay带通滤波和独立成分分析进行数据过滤。采用不同的神经网络算法，根据受试者的情绪对脑电图数据进行分析和分类。该模型进一步通过使用Blackman窗函数傅里叶变换提取每个电极的最显著频率进行优化。利用这些技术，获得了高达96.01%的检测准确率。