Revealing the Self: Brainwave-Based Human Trait Identification

• URL: http://arxiv.org/abs/2412.19041v1
• Date: Thu, 26 Dec 2024 03:27:34 GMT
• Title: Revealing the Self: Brainwave-Based Human Trait Identification
• Authors: Md Mirajul Islam, Md Nahiyan Uddin, Maoyejatun Hasana, Debojit Pandit, Nafis Mahmud Rahman, Sriram Chellappan, Sami Azam, A. B. M. Alim Al Islam,
• Abstract summary: This paper introduces a novel technique for identifying human traits in real time using brainwave data. Our analysis uncovers several new insights, leading us to a groundbreaking unified approach for identifying diverse human traits. We have developed an integrated, real-time trait identification solution using EEG data, based on the insights from our analysis.
• Score: 2.660113491122853
• License:
• 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.

Related papers

• Are LLMs effective psychological assessors? Leveraging adaptive RAG for interpretable mental health screening through psychometric practice [2.9775344067885974]
  We propose a novel adaptive Retrieval-Augmented Generation (RAG) approach that completes psychological questionnaires by analyzing social media posts. Our method retrieves the most relevant user posts for each question in a psychological survey and uses Large Language Models (LLMs) to predict questionnaire scores in a zero-shot setting.
  arXiv  Detail & Related papers  (2025-01-02T00:01:54Z)
• Modeling User Preferences via Brain-Computer Interfacing [54.3727087164445]
  We use Brain-Computer Interfacing technology to infer users' preferences, their attentional correlates towards visual content, and their associations with affective experience. We link these to relevant applications, such as information retrieval, personalized steering of generative models, and crowdsourcing population estimates of affective experiences.
  arXiv  Detail & Related papers  (2024-05-15T20:41:46Z)
• Synthesizing Affective Neurophysiological Signals Using Generative Models: A Review Paper [28.806992102323324]
  The integration of emotional intelligence in machines is an important step in advancing human-computer interaction. The scarcity of public affective datasets presents a challenge. We emphasize the use of generative models to address this issue in neurophysiological signals.
  arXiv  Detail & Related papers  (2023-06-05T08:38:30Z)
• Co-Located Human-Human Interaction Analysis using Nonverbal Cues: A Survey [71.43956423427397]
  We aim to identify the nonverbal cues and computational methodologies resulting in effective performance. This survey differs from its counterparts by involving the widest spectrum of social phenomena and interaction settings. Some major observations are: the most often used nonverbal cue, computational method, interaction environment, and sensing approach are speaking activity, support vector machines, and meetings composed of 3-4 persons equipped with microphones and cameras, respectively.
  arXiv  Detail & Related papers  (2022-07-20T13:37:57Z)
• Emotion Estimation from EEG -- A Dual Deep Learning Approach Combined with Saliency [2.555313870523154]
  We propose a dual method considering the physiological knowledge defined by specialists combined with novel deep learning (DL) models initially dedicated to computer vision. To present a global approach, the model has been evaluated on four publicly available datasets and achieves similar results to the state-of-theart approaches.
  arXiv  Detail & Related papers  (2022-01-11T11:38:36Z)
• Human-in-the-Loop Disinformation Detection: Stance, Sentiment, or Something Else? [93.91375268580806]
  Both politics and pandemics have recently provided ample motivation for the development of machine learning-enabled disinformation (a.k.a. fake news) detection algorithms. Existing literature has focused primarily on the fully-automated case, but the resulting techniques cannot reliably detect disinformation on the varied topics, sources, and time scales required for military applications. By leveraging an already-available analyst as a human-in-the-loop, canonical machine learning techniques of sentiment analysis, aspect-based sentiment analysis, and stance detection become plausible methods to use for a partially-automated disinformation detection system.
  arXiv  Detail & Related papers  (2021-11-09T13:30:34Z)
• Deep Convolution Network Based Emotion Analysis for Automatic Detection of Mild Cognitive Impairment in the Elderly [15.217754542927961]
  Early detection of cognitive impairment is of great importance to both patients and caregivers. It has been found that patients with cognitive impairment show abnormal emotion patterns. We present a novel deep convolution network-based system to detect the cognitive impairment.
  arXiv  Detail & Related papers  (2021-11-09T11:51:33Z)
• A Novel Transferability Attention Neural Network Model for EEG Emotion Recognition [51.203579838210885]
  We propose a transferable attention neural network (TANN) for EEG emotion recognition. TANN learns the emotional discriminative information by highlighting the transferable EEG brain regions data and samples adaptively. This can be implemented by measuring the outputs of multiple brain-region-level discriminators and one single sample-level discriminator.
  arXiv  Detail & Related papers  (2020-09-21T02:42:30Z)
• DRG: Dual Relation Graph for Human-Object Interaction Detection [65.50707710054141]
  We tackle the challenging problem of human-object interaction (HOI) detection. Existing methods either recognize the interaction of each human-object pair in isolation or perform joint inference based on complex appearance-based features. In this paper, we leverage an abstract spatial-semantic representation to describe each human-object pair and aggregate the contextual information of the scene via a dual relation graph.
  arXiv  Detail & Related papers  (2020-08-26T17:59:40Z)
• Prediction of Human Empathy based on EEG Cortical Asymmetry [0.0]
  lateralization of brain oscillations at specific frequency bands is an important predictor of self-reported empathy scores. Results could be employed in the development of brain-computer interfaces that assist people with difficulties in expressing or recognizing emotions.
  arXiv  Detail & Related papers  (2020-05-06T13:49:56Z)
• Continuous Emotion Recognition via Deep Convolutional Autoencoder and Support Vector Regressor [70.2226417364135]
  It is crucial that the machine should be able to recognize the emotional state of the user with high accuracy. Deep neural networks have been used with great success in recognizing emotions. We present a new model for continuous emotion recognition based on facial expression recognition.
  arXiv  Detail & Related papers  (2020-01-31T17:47:16Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.