用合成数据+LSTM+微调提升脑电分类效果

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

• 用合成数据增强样本，结合LSTM处理时序信号
• 在模糊视觉刺激下分类准确率显著提升
• 适合小样本脑电数据分析的科研人员参考

本文提出一种用于脑电图（EEG）数据分类的机器学习流程。针对隐式视觉刺激（如不同模糊度的奈克立方体）实验中的分类难题，采用合成数据生成、长短期记忆人工神经网络（LSTM）和微调相结合的方法。该方法有效提升了原始脑电数据分类模型的性能，显著改善了对复杂认知状态的识别能力。

原文摘要 · Abstract (English)

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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