In this paper, a deep long short term memory (DeepLSTM) network to classify personality traits using the electroencephalogram (EEG) signals is implemented. For this research, the Myers–Briggs Type Indicator (MBTI) model for predicting personality is used. There are four groups in MBTI, and each group consists of two traits versus each other; i.e., out of these two traits, every individual will have one personality trait in them. We have collected EEG data using a single NeuroSky MindWave Mobile 2 dry electrode unit. For data collection, 40 Hindi and English video clips were included in a standard database. All clips provoke various emotions, and data collection is focused on these emotions, as the clips include targeted, inductive scenes of personality. Fifty participants engaged in this research and willingly agreed to provide brain signals. We compared the performance of our deep learning DeepLSTM model with other state-of-the-art-based machine learning classifiers such as artificial neural network (ANN), K-nearest neighbors (KNN), LibSVM, and hybrid genetic programming (HGP). The analysis shows that, for the 10-fold partitioning method, the DeepLSTM model surpasses the other state-of-the-art models and offers a maximum classification accuracy of 96.94%. The proposed DeepLSTM model was also applied to the publicly available ASCERTAIN EEG dataset and showed an improvement over the state-of-the-art methods.

中文翻译：

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使用快速傅立叶变换和 DeepLSTM 模型的基于 EEG 的个性预测

在本文中，实现了使用脑电图 (EEG) 信号对人格特征进行分类的深度长短期记忆 (DeepLSTM) 网络。在这项研究中，使用了 Myers-Briggs Type Indicator (MBTI) 模型来预测个性。MBTI有四组，每组由两个相互对立的特质组成；也就是说，在这两种特质中，每个人都会有一种人格特质。我们使用单个 NeuroSky MindWave Mobile 2 干电极装置收集了 EEG 数据。为了收集数据，标准数据库中包含 40 个印地语和英语视频剪辑。所有的剪辑都会引发各种情绪，数据收集集中在这些情绪上，因为剪辑包含有针对性的、归纳的个性场景。50 名参与者参与了这项研究，并心甘情愿地同意提供大脑信号。我们将我们的深度学习 DeepLSTM 模型与其他基于最先进机器学习分类器的性能进行了比较，例如人工神经网络 (ANN)、K-最近邻 (KNN)、LibSVM 和混合遗传编程 (HGP) . 分析表明，对于 10 折分区方法，DeepLSTM 模型超越了其他最先进的模型，并提供了 96.94% 的最大分类准确率。提出的 DeepLSTM 模型也应用于公开可用的 ASCERTAIN EEG 数据集，并显示出对最先进方法的改进。对于 10 折分区方法，DeepLSTM 模型超越了其他最先进的模型，并提供了 96.94% 的最大分类准确率。提出的 DeepLSTM 模型也应用于公开可用的 ASCERTAIN EEG 数据集，并显示出对最先进方法的改进。对于 10 折分区方法，DeepLSTM 模型超越了其他最先进的模型，并提供了 96.94% 的最大分类准确率。提出的 DeepLSTM 模型也应用于公开可用的 ASCERTAIN EEG 数据集，并显示出对最先进方法的改进。