Mental Workload Estimation with Electroencephalogram Signals by Combining Multi-Space Deep Models

• URL: http://arxiv.org/abs/2308.02409v2
• Date: Tue, 12 Mar 2024 12:25:24 GMT
• Title: Mental Workload Estimation with Electroencephalogram Signals by Combining Multi-Space Deep Models
• Authors: Hong-Hai Nguyen, Ngumimi Karen Iyortsuun, Seungwon Kim, Hyung-Jeong Yang, and Soo-Hyung Kim
• Abstract summary: Mental activity is a daily process, and if the brain becomes excessively active, known as overload, it can adversely affect human health. In this paper, we categorize mental workload into three states (low, middle, and high) and estimate a continuum of mental workload levels. Our method leverages information from multiple spatial dimensions to achieve optimal results in mental estimation.
• Score: 13.054897887317342
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The human brain remains continuously active, whether an individual is working or at rest. Mental activity is a daily process, and if the brain becomes excessively active, known as overload, it can adversely affect human health. Recently, advancements in early prediction of mental health conditions have emerged, aiming to prevent serious consequences and enhance the overall quality of life. Consequently, the estimation of mental status has garnered significant attention from diverse researchers due to its potential benefits. While various signals are employed to assess mental state, the electroencephalogram, containing extensive information about the brain, is widely utilized by researchers. In this paper, we categorize mental workload into three states (low, middle, and high) and estimate a continuum of mental workload levels. Our method leverages information from multiple spatial dimensions to achieve optimal results in mental estimation. For the time domain approach, we employ Temporal Convolutional Networks. In the frequency domain, we introduce a novel architecture based on combining residual blocks, termed the Multi-Dimensional Residual Block. The integration of these two domains yields significant results compared to individual estimates in each domain. Our approach achieved a 74.98% accuracy in the three-class classification, surpassing the provided data results at 69.00%. Specially, our method demonstrates efficacy in estimating continuous levels, evidenced by a corresponding Concordance Correlation Coefficient (CCC) result of 0.629. The combination of time and frequency domain analysis in our approach highlights the exciting potential to improve healthcare applications in the future.

Related papers

• Cross-subject Brain Functional Connectivity Analysis for Multi-task Cognitive State Evaluation [16.198003101055264]
  This study adopts brain functional connectivity with electroencephalography signals to capture associations in brain regions across multiple subjects for evaluating real-time cognitive states. Thirty subjects are acquired for analysis and evaluation. The results are interpreted through different perspectives, including inner-subject and cross-subject for task-wise and gender-wise underlying brain functional connectivity.
  arXiv  Detail & Related papers  (2024-08-27T12:51:59Z)
• BrainODE: Dynamic Brain Signal Analysis via Graph-Aided Neural Ordinary Differential Equations [67.79256149583108]
  We propose a novel model called BrainODE to achieve continuous modeling of dynamic brain signals. By learning latent initial values and neural ODE functions from irregular time series, BrainODE effectively reconstructs brain signals at any time point.
  arXiv  Detail & Related papers  (2024-04-30T10:53:30Z)
• A Self-supervised Framework for Improved Data-Driven Monitoring of Stress via Multi-modal Passive Sensing [7.084068935028644]
  We propose a multi-modal semi-supervised framework for tracking physiological precursors of the stress response. Our methodology enables utilizing multi-modal data of differing domains and resolutions from wearable devices. We perform training experiments using a corpus of real-world data on perceived stress.
  arXiv  Detail & Related papers  (2023-03-24T20:34:46Z)
• Modeling cognitive load as a self-supervised brain rate with electroencephalography and deep learning [2.741266294612776]
  This research presents a novel self-supervised method for mental workload modelling from EEG data. The method is a convolutional recurrent neural network trainable with spatially preserving spectral topographic head-maps from EEG data to fit the brain rate variable. Findings point to the existence of quasi-stable blocks of learnt high-level representations of cognitive activation because they can be induced through convolution and seem not to be dependent on each other over time, intuitively matching the non-stationary nature of brain responses.
  arXiv  Detail & Related papers  (2022-09-21T07:44:21Z)
• The world seems different in a social context: a neural network analysis of human experimental data [57.729312306803955]
  We show that it is possible to replicate human behavioral data in both individual and social task settings by modifying the precision of prior and sensory signals. An analysis of the neural activation traces of the trained networks provides evidence that information is coded in fundamentally different ways in the network in the individual and in the social conditions.
  arXiv  Detail & Related papers  (2022-03-03T17:19:12Z)
• Overcoming the Domain Gap in Neural Action Representations [60.47807856873544]
  3D pose data can now be reliably extracted from multi-view video sequences without manual intervention. We propose to use it to guide the encoding of neural action representations together with a set of neural and behavioral augmentations. To reduce the domain gap, during training, we swap neural and behavioral data across animals that seem to be performing similar actions.
  arXiv  Detail & Related papers  (2021-12-02T12:45:46Z)
• Improving Phenotype Prediction using Long-Range Spatio-Temporal Dynamics of Functional Connectivity [9.015698823470899]
  We present an approach to model functional brain connectivity across space and time. We use the Human Connectome Project dataset on sex classification and fluid intelligence prediction. Results show a prediction accuracy of 94.4% for sex, and an improvement of correlation with fluid intelligence of 0.325 vs 0.144, relative to a baseline model that encodes space and time separately.
  arXiv  Detail & Related papers  (2021-09-07T14:23:34Z)
• Voxel-level Importance Maps for Interpretable Brain Age Estimation [70.5330922395729]
  We focus on the task of brain age regression from 3D brain Magnetic Resonance (MR) images using a Convolutional Neural Network, termed prediction model. We implement a noise model which aims to add as much noise as possible to the input without harming the performance of the prediction model. We test our method on 13,750 3D brain MR images from the UK Biobank, and our findings are consistent with the existing neuropathology literature.
  arXiv  Detail & Related papers  (2021-08-11T18:08:09Z)
• MET: Multimodal Perception of Engagement for Telehealth [52.54282887530756]
  We present MET, a learning-based algorithm for perceiving a human's level of engagement from videos. We release a new dataset, MEDICA, for mental health patient engagement detection.
  arXiv  Detail & Related papers  (2020-11-17T15:18:38Z)
• Detecting Parkinsonian Tremor from IMU Data Collected In-The-Wild using Deep Multiple-Instance Learning [59.74684475991192]
  Parkinson's Disease (PD) is a slowly evolving neuro-logical disease that affects about 1% of the population above 60 years old. PD symptoms include tremor, rigidity and braykinesia. We present a method for automatically identifying tremorous episodes related to PD, based on IMU signals captured via a smartphone device.
  arXiv  Detail & Related papers  (2020-05-06T09:02:30Z)
• Towards a predictive spatio-temporal representation of brain data [0.2580765958706854]
  We show that fMRI datasets are constituted by complex and highly heterogeneous timeseries. We compare various modelling techniques from deep learning and geometric deep learning to pave the way for future research. We hope that our methodological advances can ultimately be clinically and computationally relevant by leading to a more nuanced understanding of the brain dynamics in health and disease.
  arXiv  Detail & Related papers  (2020-02-29T18:49:45Z)

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.