How Long short-term memory artificial neural network, synthetic data, and fine-tuning improve the classification of raw EEG data

TL;DR

This paper presents a machine learning pipeline combining synthetic data, LSTM neural networks, and fine-tuning to enhance EEG data classification accuracy.

Contribution

It introduces a novel approach integrating synthetic data and LSTM with fine-tuning specifically for EEG classification tasks.

Findings

Improved classification accuracy of raw EEG data.

Effective use of synthetic data to augment training.

LSTM-based model outperforms traditional methods.

Abstract

In this paper, we discuss a Machine Learning pipeline for the classification of EEG data. We propose a combination of synthetic data generation, long short-term memory artificial neural network (LSTM), and fine-tuning to solve classification problems for experiments with implicit visual stimuli, such as the Necker cube with different levels of ambiguity. The developed approach increased the quality of the classification model of raw EEG data.