How Long short-term memory artificial neural network, synthetic data, and fine-tuning improve the classification of raw EEG data
This addresses EEG data classification for neuroscience applications, but it appears incremental as it combines existing techniques.
The paper tackled EEG classification for implicit visual stimuli like the Necker cube by proposing a pipeline combining synthetic data generation, LSTM, and fine-tuning, which increased classification quality.
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.