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

arXiv: https://arxiv.org/abs/2604.04316 · HTML: https://mlsanity.com/paper/2604.04316/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 17 references that are in the map.
- First posted on arXiv 5 Apr 2026.

## Builds on (most cited first, 2 of 2)

- [An LSTM Based Architecture to Relate Speech Stimulus to EEG](https://mlsanity.com/paper/2002.10988/) (2020)
- [A Simple Way to Deal with Cherry-picking](https://mlsanity.com/paper/1810.04996/) (2018)

## Abstract (arXiv)

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.