Prediction of multitasking performance post-longitudinal tDCS via EEG-based functional connectivity and machine learning methods

• URL: http://arxiv.org/abs/2401.17711v1
• Date: Wed, 31 Jan 2024 10:03:27 GMT
• Title: Prediction of multitasking performance post-longitudinal tDCS via EEG-based functional connectivity and machine learning methods
• Abstract summary: We employ different EEG-based functional connectivity analyses and machine learning algorithms to predict changes in cognitive performance. We discuss the implications of our results in developing real-time cognitive state assessors.
• Score: 1.9351774578832828
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Predicting and understanding the changes in cognitive performance, especially after a longitudinal intervention, is a fundamental goal in neuroscience. Longitudinal brain stimulation-based interventions like transcranial direct current stimulation (tDCS) induce short-term changes in the resting membrane potential and influence cognitive processes. However, very little research has been conducted on predicting these changes in cognitive performance post-intervention. In this research, we intend to address this gap in the literature by employing different EEG-based functional connectivity analyses and machine learning algorithms to predict changes in cognitive performance in a complex multitasking task. Forty subjects were divided into experimental and active-control conditions. On Day 1, all subjects executed a multitasking task with simultaneous 32-channel EEG being acquired. From Day 2 to Day 7, subjects in the experimental condition undertook 15 minutes of 2mA anodal tDCS stimulation during task training. Subjects in the active-control condition undertook 15 minutes of sham stimulation during task training. On Day 10, all subjects again executed the multitasking task with EEG acquisition. Source-level functional connectivity metrics, namely phase lag index and directed transfer function, were extracted from the EEG data on Day 1 and Day 10. Various machine learning models were employed to predict changes in cognitive performance. Results revealed that the multi-layer perceptron and directed transfer function recorded a cross-validation training RMSE of 5.11% and a test RMSE of 4.97%. We discuss the implications of our results in developing real-time cognitive state assessors for accurately predicting cognitive performance in dynamic and complex tasks post-tDCS intervention

Related papers

• ASD Classification on Dynamic Brain Connectome using Temporal Random Walk with Transformer-based Dynamic Network Embedding [1.6044444452278062]
  We propose BrainTWT, a novel dynamic network embedding approach that captures temporal evolution of the brain connectivity over time. The experimental evaluation, utilizing the Autism Brain Imaging Data Exchange (ABIDE) dataset, demonstrates that BrainTWT outperforms baseline methods in ASD classification.
  arXiv  Detail & Related papers  (2025-03-16T05:44:11Z)
• Auto Detecting Cognitive Events Using Machine Learning on Pupillary Data [0.0]
  Pupil size is a valuable indicator of cognitive workload, reflecting changes in attention and arousal governed by the autonomic nervous system. This study explores the potential of using machine learning to automatically detect cognitive events experienced using individuals.
  arXiv  Detail & Related papers  (2024-10-18T04:54:46Z)
• Classification of executive functioning performance post-longitudinal tDCS using functional connectivity and machine learning methods [0.0]
  Executive functioning is a cognitive process that enables humans to plan, organize, and regulate their behavior in a goal-directed manner. This study employs functional connectivity and machine learning algorithms to classify executive functioning performance post-tDCS.
  arXiv  Detail & Related papers  (2024-01-31T09:45:03Z)
• Analyzing Brain Activity During Learning Tasks with EEG and Machine Learning [0.1433758865948252]
  This study aims to analyze brain activity during various STEM activities, exploring the feasibility of classifying between different tasks. EEG brain data from twenty subjects engaged in five cognitive tasks were collected and segmented into 4-second clips. This study contributes to a deeper understanding of implementing machine learning in analyzing brain activity and sheds light on the brain's mechanisms.
  arXiv  Detail & Related papers  (2024-01-15T18:57:25Z)
• A Knowledge-Driven Cross-view Contrastive Learning for EEG Representation [48.85731427874065]
  This paper proposes a knowledge-driven cross-view contrastive learning framework (KDC2) to extract effective representations from EEG with limited labels. The KDC2 method creates scalp and neural views of EEG signals, simulating the internal and external representation of brain activity. By modeling prior neural knowledge based on neural information consistency theory, the proposed method extracts invariant and complementary neural knowledge to generate combined representations.
  arXiv  Detail & Related papers  (2023-09-21T08:53:51Z)
• EEG-based Cognitive Load Classification using Feature Masked Autoencoding and Emotion Transfer Learning [13.404503606887715]
  We present a new solution for the classification of cognitive load using electroencephalogram (EEG) We pre-train our model using self-supervised masked autoencoding on emotion-related EEG datasets. The results of our experiments show that our proposed approach achieves strong results and outperforms conventional single-stage fully supervised learning.
  arXiv  Detail & Related papers  (2023-08-01T02:59:19Z)
• Evaluating the structure of cognitive tasks with transfer learning [67.22168759751541]
  This study investigates the transferability of deep learning representations between different EEG decoding tasks. We conduct extensive experiments using state-of-the-art decoding models on two recently released EEG datasets.
  arXiv  Detail & Related papers  (2023-07-28T14:51:09Z)
• RObotic MAnipulation Network (ROMAN) $\unicode{x2013}$ Hybrid Hierarchical Learning for Solving Complex Sequential Tasks [70.69063219750952]
  We present a Hybrid Hierarchical Learning framework, the Robotic Manipulation Network (ROMAN) ROMAN achieves task versatility and robust failure recovery by integrating behavioural cloning, imitation learning, and reinforcement learning. Experimental results show that by orchestrating and activating these specialised manipulation experts, ROMAN generates correct sequential activations for accomplishing long sequences of sophisticated manipulation tasks.
  arXiv  Detail & Related papers  (2023-06-30T20:35:22Z)
• Incremental procedural and sensorimotor learning in cognitive humanoid robots [52.77024349608834]
  This work presents a cognitive agent that can learn procedures incrementally. We show the cognitive functions required in each substage and how adding new functions helps address tasks previously unsolved by the agent. Results show that this approach is capable of solving complex tasks incrementally.
  arXiv  Detail & Related papers  (2023-04-30T22:51:31Z)
• A Neural Active Inference Model of Perceptual-Motor Learning [62.39667564455059]
  The active inference framework (AIF) is a promising new computational framework grounded in contemporary neuroscience. In this study, we test the ability for the AIF to capture the role of anticipation in the visual guidance of action in humans. We present a novel formulation of the prior function that maps a multi-dimensional world-state to a uni-dimensional distribution of free-energy.
  arXiv  Detail & Related papers  (2022-11-16T20:00:38Z)
• fMRI from EEG is only Deep Learning away: the use of interpretable DL to unravel EEG-fMRI relationships [68.8204255655161]
  We present an interpretable domain grounded solution to recover the activity of several subcortical regions from multichannel EEG data. We recover individual spatial and time-frequency patterns of scalp EEG predictive of the hemodynamic signal in the subcortical nuclei.
  arXiv  Detail & Related papers  (2022-10-23T15:11:37Z)
• 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)

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.