Tensor-driven extraction of developmental features from varying paediatric EEG datasets

TL;DR

This study introduces a tensor-based analysis method to extract developmental features from paediatric EEG data, improving classification accuracy and visualization of developmental stages, with potential applications in brain-computer interfaces.

Contribution

The paper presents a novel two-step constrained PARAFAC tensor decomposition approach tailored for paediatric EEG data analysis, enhancing feature extraction related to development.

Findings

Development-sensitive features were successfully identified.

SVM classification accuracy improved significantly.

Tensor factorization was crucial for effective feature extraction.

Abstract

Objective. Consistently changing physiological properties in developing children's brains challenges new data heavy technologies, like brain-computer interfaces (BCI). Advancing signal processing methods in such technologies to be more sensitive to developmental changes could help improve their function and usability in paediatric populations. Taking advantage of the multi-dimensional structure of EEG data through tensor analysis offers a framework to extract relevant developmental features present in paediatric resting-state EEG datasets. Methods. Three paediatric datasets from varying developmental states and populations were analyzed using a developed two-step constrained Parallel Factor (PARAFAC) tensor decomposition. The datasets included the Muir Maxwell Epilepsy Centre, Children's Hospital Boston-MIT and the Child Mind Institute, outlining two impaired and one healthy population, respectively. Within dataset cross-validation used support vector machines (SVM) for classification of out-of-fold data predicting subject age as a proxy measure of development. t-distributed Stochastic Neighbour Embedding (t-SNE) maps complemented classification analysis through visualization of the high-dimensional feature structures. Main Results. Development-sensitive features were successfully identified for the developmental conditions of each dataset. SVM classification accuracy and misclassification costs were improved significantly for both healthy and impaired paediatric populations. t-SNE maps revealed suitable tensor factorization was key in extracting developmental features. Significance. The described methods are a promising tool for incorporating the unique developmental features present throughout childhood EEG into new technologies like BCI and its applications.