论文标题
关于音乐引起的情感识别的脑电图信号的多尺度分形分析
Multiscale Fractal Analysis on EEG Signals for Music-Induced Emotion Recognition
论文作者
论文摘要
长期以来，已经研究了脑电图信号的情绪识别，因为它可以协助众多医学和康复应用。但是，事实证明，它们复杂而嘈杂的结构是传统建模方法的严重障碍。在本文中，我们采用多重分析分析来检查脑电图的行为，以波动的存在和沿其主要频段的碎片化程度，以实现情绪识别的任务。为了提取与情绪相关的特征，我们基于多尺度分形维度和多重型脱螺旋波动分析，利用两种新型算法进行脑电图分析。所提出的特征提取方法有效地表现，超过了竞争性DEAP数据集中一些广泛使用的基线特征，表明多重分析可以作为开发可靠模型以进行情感状态识别的基础。
Emotion Recognition from EEG signals has long been researched as it can assist numerous medical and rehabilitative applications. However, their complex and noisy structure has proven to be a serious barrier for traditional modeling methods. In this paper, we employ multifractal analysis to examine the behavior of EEG signals in terms of presence of fluctuations and the degree of fragmentation along their major frequency bands, for the task of emotion recognition. In order to extract emotion-related features we utilize two novel algorithms for EEG analysis, based on Multiscale Fractal Dimension and Multifractal Detrended Fluctuation Analysis. The proposed feature extraction methods perform efficiently, surpassing some widely used baseline features on the competitive DEAP dataset, indicating that multifractal analysis could serve as basis for the development of robust models for affective state recognition.
