长短时记忆人工神经网络、合成数据和微调如何改进原始脑电图数据的分类

A. Nasybullin, V. Maksimenko, S. Kurkin
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引用次数: 0

摘要

本文讨论了一种用于脑电数据分类的机器学习管道。我们提出了一种综合数据生成、长短期记忆人工神经网络(LSTM)和微调相结合的方法来解决内隐视觉刺激实验的分类问题,如不同模糊程度的Necker立方体。该方法提高了原始脑电数据分类模型的质量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
How Long short-term memory artificial neural network, synthetic data, and fine-tuning improve the classification of raw EEG data
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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