通过放大脑电连接矩阵，提升情绪分类准确率
Emotional EEG Classification using Upscaled Connectivity Matrices
- 将脑电连接矩阵放大以增强局部模式
- 实验表明分类性能显著提升
- 适合做脑机接口与情绪识别研究者
近期情绪脑电（EEG）分类研究中，连接矩阵被用作卷积神经网络（CNN）的输入，可有效捕捉脑区间的交互模式。然而，我们发现该方法在CNN的卷积操作中可能丢失重要模式。为此，我们提出并验证了放大连接矩阵以强化局部模式的新思路。实验结果表明，这一简单策略能显著提升分类性能。
原文摘要 · Abstract (English)
In recent studies of emotional EEG classification, connectivity matrices have been successfully employed as input to convolutional neural networks (CNNs), which can effectively consider inter-regional interaction patterns in EEG. However, we find that such an approach has a limitation that important patterns in connectivity matrices may be lost during the convolutional operations in CNNs. To resolve this issue, we propose and validate an idea to upscale the connectivity matrices to strengthen the local patterns. Experimental results demonstrate that this simple idea can significantly enhance the classification performance.
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