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Related Experiment Video

Updated: Jun 13, 2025

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

Understanding Learning from EEG Data: Combining Machine Learning and Feature Engineering Based on Hidden Markov

Gabriel R Palma^1,2, Conor Thornberry³, Seán Commins⁴

¹Hamilton Institute, Maynooth University, Maynooth, Ireland. gabriel.palma.2022@mumail.ie.

Neuroinformatics

|September 10, 2024

Summary

This summary is machine-generated.

Frontal theta oscillations (4-8 Hz) are crucial for spatial navigation. Machine learning models, particularly deep neural networks, can effectively classify learners from non-learners using standardized electroencephalography data.

Keywords:

Deep learning EEG data Hidden Markov models Machine learning Time series

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Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy

Published on: June 27, 2013

Area of Science:

Neuroscience
Cognitive Science
Machine Learning

Background:

Theta oscillations (4-8 Hz) are vital for spatial learning and memory during navigation.
Frontal theta oscillations are particularly implicated in spatial navigation and memory processes.
Electroencephalography (EEG) data complexity poses challenges for interpreting neural signals related to behavior.

Purpose of the Study:

To investigate the efficacy of machine learning techniques in classifying participants as learners or non-learners based on EEG data during a spatial navigation task.
To evaluate the impact of feature engineering using hidden Markov and linear mixed effects models on classification performance.
To assess the influence of different EEG data standardization methods on the classification accuracy.

Main Methods:

Engineered features from frontal theta EEG data using hidden Markov and linear mixed effects models.
Applied six machine learning algorithms to classify learner and non-learner participants based on engineered features from early (first) and late (last) trials.
Compared classification performance using EEG-derived features versus solely coordinate-based features (e.g., idle time, average speed).

Main Results:

Coordinate-based features generally yielded better classification performance across most machine learning methods.
Deep neural networks achieved an area under the ROC curve exceeding 80% when using theta EEG data alone.
Standardization of theta EEG data significantly improved classification performance when combined with deep neural networks.

Conclusions:

Standardizing electroencephalography (EEG) data and employing deep neural networks can enhance the classification of learner and non-learner subjects in spatial learning tasks.
While coordinate-based features are effective, EEG-derived features, particularly with advanced models like DNNs, offer valuable insights into learning processes.