Brain Connectivity Features-based Age Group Classification using Temporal Asynchrony Audio-Visual Integration Task

TL;DR

This study explores EEG-based brain connectivity features during audiovisual tasks to classify middle-aged groups, revealing significant age-related differences and achieving high classification accuracy with machine learning.

Contribution

It introduces a novel EEG connectivity analysis during audiovisual tasks for middle-aged groups and demonstrates effective age classification using graph features and Random Forest.

Findings

Connectivity features distinguish age groups with high accuracy.

Classification accuracy of 89.4% for audio stimuli.

Connectivity analysis reveals age-related neural changes.

Abstract

The process of integration of inputs from several sensory modalities in the human brain is referred to as multisensory integration. Age-related cognitive decline leads to a loss in the ability of the brain to conceive multisensory inputs. There has been considerable work done in the study of such cognitive changes for the old age groups. However, in the case of middle age groups, such analysis is limited. Motivated by this, in the current work, EEG-based functional connectivity during audiovisual temporal asynchrony integration task for middle-aged groups is explored. Investigation has been carried out during different tasks such as: unimodal audio, unimodal visual, and variations of audio-visual stimulus. A correlation-based functional connectivity analysis is done, and the changes among different age groups including: young (18-25 years), transition from young to middle age (25-33 years), and medium (33-41 years), are observed. Furthermore, features extracted from the connectivity graphs have been used to classify among the different age groups. Classification accuracies of $89.4\%$ and $88.4\%$ are obtained for the Audio and Audio-50-Visual stimuli cases with a Random Forest based classifier, thereby validating the efficacy of the proposed method.