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

arXiv cs.LG / 4/7/2026

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Key Points

  • The paper presents a machine-learning pipeline to classify raw EEG data for experiments involving implicit visual stimuli such as the Necker cube.
  • It combines synthetic data generation with an LSTM-based artificial neural network to address challenges in EEG classification.
  • The authors also apply fine-tuning as part of the training process to improve classification performance.
  • Their results indicate that the combined approach increases the quality of EEG classification models when working directly from raw signal data.

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.

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