EEG Signal Dimensionality Reduction and Classification using Tensor Decomposition and Deep Convolutional Neural Networks

TL;DR

This paper introduces a novel EEG analysis framework that combines tensor decomposition for dimensionality reduction with CNNs for classification, improving efficiency and accuracy in EEG signal processing.

Contribution

The study presents a new tensor decomposition-based method for reducing EEG data dimensionality before CNN classification, outperforming previous approaches.

Findings

Outperforms previous EEG classification methods

Reduces CNN input dimensionality effectively

Handles EEG artifacts and redundancies

Abstract

A new deep learning-based electroencephalography (EEG) signal analysis framework is proposed. While deep neural networks, specifically convolutional neural networks (CNNs), have gained remarkable attention recently, they still suffer from high dimensionality of the training data. Two-dimensional input images of CNNs are more vulnerable to be redundant versus one-dimensional input time-series of conventional neural networks. In this study, we propose a new dimensionality reduction framework for reducing the dimension of CNN inputs based on the tensor decomposition of the time-frequency representation of EEG signals. The proposed tensor decomposition-based dimensionality reduction algorithm transforms a large set of slices of the input tensor to a concise set of slices which are called super-slices. Employing super-slices not only handles the artifacts and redundancies of the EEG data but also reduces the dimension of the CNNs training inputs. We also consider different time-frequency representation methods for EEG image generation and provide a comprehensive comparison among them. We test our proposed framework on HCB-MIT data and as results show our approach outperforms other previous studies.