Revealing the Self: Brainwave-Based Human Trait Identification
Md Mirajul Islam, Md Nahiyan Uddin, Maoyejatun Hasana, Debojit Pandit, Nafis Mahmud Rahman, Sriram Chellappan, Sami Azam, A. B. M. Alim Al Islam
TL;DR
The paper addresses the problem of identifying diverse human traits from brain activity in real time using EEG signals. It collects data from $80$ participants across four emotional states with a low-cost headset, extracting a $16$-dimensional feature vector comprising mean and standard deviation across eight bands, and trains $56$ Auto-WEKA models across traits and emotions while benchmarking LSTM and BiLSTM baselines. The authors present a unified, real-time trait-identification pipeline implemented in a Java application and validated with $20$ new participants, showing high accuracy for several traits and favorable user feedback. The work demonstrates the potential of low-cost, portable brainwave analytics for applications in psychology, security, and healthcare, while outlining avenues to improve robustness and broaden trait coverage.
Abstract
People exhibit unique emotional responses. In the same scenario, the emotional reactions of two individuals can be either similar or vastly different. For instance, consider one person's reaction to an invitation to smoke versus another person's response to a query about their sleep quality. The identification of these individual traits through the observation of common physical parameters opens the door to a wide range of applications, including psychological analysis, criminology, disease prediction, addiction control, and more. While there has been previous research in the fields of psychometrics, inertial sensors, computer vision, and audio analysis, this paper introduces a novel technique for identifying human traits in real time using brainwave data. To achieve this, we begin with an extensive study of brainwave data collected from 80 participants using a portable EEG headset. We also conduct a statistical analysis of the collected data utilizing box plots. Our analysis uncovers several new insights, leading us to a groundbreaking unified approach for identifying diverse human traits by leveraging machine learning techniques on EEG data. Our analysis demonstrates that this proposed solution achieves high accuracy. Moreover, we explore two deep-learning models to compare the performance of our solution. Consequently, we have developed an integrated, real-time trait identification solution using EEG data, based on the insights from our analysis. To validate our approach, we conducted a rigorous user evaluation with an additional 20 participants. The outcomes of this evaluation illustrate both high accuracy and favorable user ratings, emphasizing the robust potential of our proposed method to serve as a versatile solution for human trait identification.