Hyperdimensional Computing for ADHD Classification using EEG Signals

TL;DR

This study introduces a hyperdimensional computing approach for ADHD classification using EEG signals, achieving high accuracy with minimal training data, thus offering a promising efficient diagnostic tool.

Contribution

First application of hyperdimensional computing to classify ADHD from EEG data, demonstrating high accuracy and data efficiency compared to traditional methods.

Findings

Achieved 88.9% accuracy in ADHD classification.

Model trained on only 7 patients performs comparably to full dataset models.

Demonstrated efficiency in training data requirements for medical diagnosis.

Abstract

Following the recent interest in applying the Hyperdimensional Computing paradigm in medical context to power up the performance of general machine learning applied to biomedical data, this study represents the first attempt at employing such techniques to solve the problem of classification of Attention Deficit Hyperactivity Disorder using electroencephalogram signals. Making use of a spatio-temporal encoder, and leveraging the properties of HDC, the proposed model achieves an accuracy of 88.9%, outperforming traditional Deep Neural Networks benchmark models. The core of this research is not only to enhance the classification accuracy of the model but also to explore its efficiency in terms of the required training data: a critical finding of the study is the identification of the minimum number of patients needed in the training set to achieve a sufficient level of accuracy. To this end, the accuracy of our model trained with only $7$ of the $79$ patients is comparable to the one from benchmarks trained on the full dataset. This finding underscores the model's efficiency and its potential for quick and precise ADHD diagnosis in medical settings where large datasets are typically unattainable.