Abstract
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.
Highlights
Personality has been developed from different theories, but personality core is a function of individual behavioral differences and experiences affected by an individual’s development, such as his/her emotions, social relationships, and life experiences [1]
The electroencephalogram (EEG) signals have grown in prominence in recent years and have achieved a higher classification accuracy [17, 18]. e electrical activity produced by neurons in the brain is recorded using EEG, which have been widely utilised to study functional changes in the brain [19, 20]
In 50–50, 60–40, and 70–30 training-testing partition, 50%, 60%, and 70%, respectively, data is used for training, and 50%, 40%, and 30%, respectively, of the data is used for testing. e complete dataset is partitioned into approximately ten equal size blocks in a 10-fold cross-validation scheme; 90% of the dataset, i.e., nine blocks, becomes our training data, and 10% of the dataset, i.e. one block, becomes our testing data. is process is repeated ten times, with each time a different data block being used for testing
Summary
Personality has been developed from different theories, but personality core is a function of individual behavioral differences and experiences affected by an individual’s development, such as his/her emotions, social relationships, and life experiences [1]. E personality prediction using physiological signals has recently received a lot of interest [11]. Recognizing personality from physiological signals [12,13,14] is more accurate than digital footprints [15, 16] because this approach achieves a higher classification accuracy. E first step in the successful classification [23, 24] of personality traits is to extract important EEG signal features. For a decomposing signal with fast Fourier transform (FFT), the TGAM1 chip has an algorithm. Each second data are gathered and processed in the temporal field to identify and correct as much as possible the artifacts and background noise, without the practical usage of NeuroSky’s proprietary algorithms, of the original signal.
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