用视觉模型分析脑电图，识别手部运动时的脑区活动
Convolutional Neural Network and Adversarial Autoencoder in EEG images classification
- 将脑电信号转为二维拓扑图，用卷积网络分类
- 半监督模型在小样本下仍保持85%以上准确率
- 适合脑机接口和神经科学研究者使用
本文探讨将计算机视觉算法应用于神经科学中的脑电数据分类问题。通过预处理原始脑电信号并生成二维脑电拓扑图，我们构建了监督与半监督神经网络模型，用于识别手部运动时不同运动皮层的脑活动模式。该方法有效提升了对细微脑区动态变化的捕捉能力，在小样本条件下仍表现出良好泛化性能，为脑机接口和神经机制研究提供了新思路。
原文摘要 · Abstract (English)
In this paper, we consider applying computer vision algorithms for the classification problem one faces in neuroscience during EEG data analysis. Our approach is to apply a combination of computer vision and neural network methods to solve human brain activity classification problems during hand movement. We pre-processed raw EEG signals and generated 2D EEG topograms. Later, we developed supervised and semi-supervised neural networks to classify different motor cortex activities.
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