Mental arithmetic task classification with convolutional neural network based on spectral-temporal features from EEG

TL;DR

This study develops a shallow CNN model that efficiently classifies mental arithmetic tasks from EEG spectral-temporal features, outperforming deeper models in accuracy and robustness, aiding brain-computer interface applications for patients with motor disorders.

Contribution

Introduces a shallow CNN architecture for EEG-based mental task classification, demonstrating superior accuracy and robustness over deeper neural networks.

Findings

Shallow CNN achieved 90.68% accuracy.

Model showed only 3% standard deviation across subjects.

Outperformed deeper neural network models.

Abstract

In recent years, neuroscientists have been interested to the development of brain-computer interface (BCI) devices. Patients with motor disorders may benefit from BCIs as a means of communication and for the restoration of motor functions. Electroencephalography (EEG) is one of most used for evaluating the neuronal activity. In many computer vision applications, deep neural networks (DNN) show significant advantages. Towards to ultimate usage of DNN, we present here a shallow neural network that uses mainly two convolutional neural network (CNN) layers, with relatively few parameters and fast to learn spectral-temporal features from EEG. We compared this models to three other neural network models with different depths applied to a mental arithmetic task using eye-closed state adapted for patients suffering from motor disorders and a decline in visual functions. Experimental results showed that the shallow CNN model outperformed all the other models and achieved the highest classification accuracy of 90.68%. It's also more robust to deal with cross-subject classification issues: only 3% standard deviation of accuracy instead of 15.6% from conventional method.