Advances in Computational Intelligence - 17th International Work-Conference on Artificial Neural Networks, IWANN 2023, Ponta Delgada, Portugal, June 19-21, 2023, Proceedings, Part I
Advances in Computational Intelligence - 17th International Work-Conference on Artificial Neural Networks, IWANN 2023, Ponta Delgada, Portugal, June 19-21, 2023, Proceedings, Part I
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计算智能的进展 - 第 17 届国际人工神经网络工作会议，IWANN 2023，葡萄牙蓬塔德尔加达，2023 年 6 月 19-21 日，会议记录，第一部分
DOI：
10.1007/978-3-031-43085-5_48
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发表时间：
2023
期刊：
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通讯作者：
Rushbrooke A
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作者：
Rushbrooke A
脑电(EEG)是一种使用放置在头皮上的电极来记录大脑电活动的非侵入性技术。脑电数据通常用于分类问题。然而，目前的许多分类技术都是特定于数据集的，不能作为一个整体应用于脑电数据问题。我们建议使用多变量时间序列分类(MTSC)算法作为替代。我们的实验显示了与UCR时间序列分类档案上的脑电数据集的标准方法的结果相当的准确性，而不需要执行任何特定于数据集的特征选择。我们还展示了MTSC在一个新问题上的表现，将患有纤维肌痛综合征(FMS)的患者与没有出现这种情况的患者进行分类。我们使用短时快速傅立叶变换方法来提取每个单独的脑电频段，发现theta和α频段可能包含有FMS患者和没有FMS患者之间的区别性数据。
Electroencephalography (EEG) is a non-invasive technique used to record the electrical activity of the brain using electrodes placed on the scalp. EEG data is commonly used for classification problems. However, many of the current classification techniques are dataset specific and cannot be applied to EEG data problems as a whole. We propose the use of multivariate time series classification (MTSC) algorithms as an alternative. Our experiments show comparable accuracy to results from standard approaches on EEG datasets on the UCR time series classification archive without needing to perform any dataset-specific feature selection. We also demonstrate MTSC on a new problem, classifying those with the medical condition Fibromyalgia Syndrome (FMS) against those without. We utilise a short-time Fast-Fourier transform method to extract each individual EEG frequency band, finding that the theta and alpha bands may contain discriminatory data between those with FMS compared to those without.