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Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments

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Published on: January 23, 2017

Decoding covert visual attention based on phase transfer entropy.

Amirmasoud Ahmadi¹, Saeideh Davoudi¹, Mahsa Behroozi¹

¹Neuroscience & Neuroengineering Research Lab., Biomedical Engineering Department, School of Electrical Engineering, Iran University of Science and Technology (IUST), Tehran 16846-13114, Iran.

Physiology & Behavior

|May 16, 2020

Summary

This study introduces a novel expert system to decode covert visual attention using electroencephalography (EEG) signals. The system, utilizing Phase Transfer Entropy (PTE), achieves high accuracy in brain-computer interfaces (BCI).

Keywords:

Cognitive Brain Computer Interface Covert Visual Attention Human EEG Phase Transfer Entropy

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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 and Brain-Computer Interfaces (BCI)
Signal Processing and Machine Learning

Background:

Covert attention, a new BCI control signal, requires decoding from human brain activity (EEG) to direct processing resources.
Existing methods for decoding covert attention have limitations, necessitating novel approaches.

Purpose of the Study:

To design and evaluate a novel expert system for decoding covert visual attention using EEG signals.
To investigate the efficacy of Phase Transfer Entropy (PTE) as a decoding feature for the first time in this context.
To compare binary and multi-class classification systems for attention decoding.

Main Methods:

Developed an expert system using EEG signals from 15 subjects performing a visual attention task with color changes.
Employed Phase Transfer Entropy (PTE) for feature extraction and decoding.
Evaluated performance across binary and multi-class systems, focusing on Alpha (8-13 Hz) and Beta1 (13-20 Hz) frequency bands.

Main Results:

Achieved high two-class classification accuracies of 91.87% (Alpha band) and 89.53% (Beta1 band).
Multi-class classification accuracies reached 65.11% (Alpha band) and 63.38% (Beta1 band).
PTE demonstrated superior performance compared to previous phase synchronization methods, with the Alpha band from the posterior region showing optimal results.

Conclusions:

The novel expert system effectively decodes covert visual attention using EEG and PTE.
PTE is a promising feature for attention decoding, outperforming traditional methods.
The Alpha frequency band, particularly from the posterior brain region, is crucial for accurate covert attention decoding.