All signals point to personality: A dual-pipeline LSTM-attention and symbolic dynamics framework for predicting personality traits from Bio-Electrical signals

The prediction of personality traits offers valuable insights into human behaviour, more specifically in psychology, healthcare, and social science. In this paper, we present a novel methodology for personality trait prediction using a dual-pipeline architecture. The model architecture leverages Long Short-Term Memory (LSTM) networks with batch normalization for capturing sequential dependencies in data and incorporates temporal attention heads for feature extraction. By combining these parallel pipelines, our network effectively utilizes both LSTM and attention mechanisms to create a comprehensive representation of input data. The network's goal is to predict the OCEAN (openness, conscientiousness, extraversion, agreeableness and neuroticism) traits using physiological signals including: EEG, ECG and GSR. Including attention mechanisms enables the model to focus on critical moments in these signals, resulting in significantly improved prediction accuracy. Experimental evaluations demonstrate the superior performance of our method compared to traditional machine learning methods on two publicly available datasets: ASCERTAIN and AMIGOS. Our source code is accessible at https://github.com/deepakkumar-iitr/AT3NET.