基于卷积神经网络的人类情绪识别智能脑电图系统设计

Kai-Yen Wang, Yun-Lung Ho, Yu-De Huang, W. Fang
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引用次数: 20

摘要

情绪在情感计算和人机界面领域中扮演着重要的角色。本文提出了一种基于脑电特征的多通道融合处理的智能人类情绪检测系统。我们还提出了一种先进的卷积神经网络实现在VLSI硬件设计。该硬件设计可以加快训练和分类过程,满足系统对快速情感检测的实时性要求。采用DEAP[1]数据库对32名受试者的数据集进行了性能验证,平均分类准确率为83.88%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Design of Intelligent EEG System for Human Emotion Recognition with Convolutional Neural Network
Emotions play a significant role in the field of affective computing and Human-Computer Interfaces(HCI). In this paper, we propose an intelligent human emotion detection system based on EEG features with a multi-channel fused processing. We also proposed an advanced convolutional neural network that was implemented in VLSI hardware design. This hardware design can accelerate both the training and classification processes and meet real-time system requirements for fast emotion detection. The performance of this design was validated using DEAP [1] database with datasets from 32 subjects, the mean classification accuracy achieved is 83.88%.
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