基于多分支注意的时间谱CNN的气味诱发脑电MCI检测。

IF 5.2 2区 医学 Q2 ENGINEERING, BIOMEDICAL
Farhan Riaz, Muhammad Muzammal, Christos Frantzidis, Imran Khan Niazi
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引用次数: 0

摘要

痴呆症是一种进行性神经退行性疾病,通常伴有轻度认知障碍(MCI),其特征是早期记忆困难和认知灵活性降低。早期发现轻度认知障碍对于及时干预和改善长期认知健康和生活质量至关重要。在本文中,我们的目的是基于脑电图信号的气味诱发脑电位来区分正常受试者和MCI患者。为了解决这个问题,我们使用了公开的多通道脑电图数据,并使用小波、频谱分组和典型相关计算了一组时间谱成分。这些特征被单独输入到基于注意力的卷积神经网络(CNN)模型中,该模型在每个特征集上进行单独训练,从而产生单独的特征分支。随后,这些分支被馈送到一个完全连接的网络中,以执行分类任务。实验表明,该方法优于本文所考虑的其他方法。消融研究还揭示了本研究中采用的每组特征的单独强度,以及在使用整个特征集进行分类时它们的综合强度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
MCI Detection from Odor-Evoked EEG Using a Multibranch Attention-Based Temporal-Spectral CNN.

Dementia is a progressive neurodegenerative condition often preceded by Mild Cognitive Impairment (MCI), which is marked by early-stage memory difficulties and reduced cognitive flexibility. Detecting MCI at an early stage is crucial for timely intervention and for improving long-term cognitive health and quality of life. In this paper, we aim to differentiate between normal subjects and those suffering from MCI based on odor-evoked brain potentials from EEG signals. To address this challenge, we used publicly available multichannel EEG data and calculated a set of temporal-spectral components using wavelets, spectral grouping, and canonical correlation. These features are fed separately into attention-based convolutional neural network (CNN) models, which are individually trained on each feature-set, leading to individual feature branches. Later, these branches are fed into a fully connected network for performing the classification task. Our experiments demonstrate that the proposed method outperforms other methods considered in this paper. Ablation studies also reveal the individual strength of each set of features adopted in this study, along with their combined strength when the entire feature set is used for classification.

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来源期刊
CiteScore
8.60
自引率
8.20%
发文量
479
审稿时长
6-12 weeks
期刊介绍: Rehabilitative and neural aspects of biomedical engineering, including functional electrical stimulation, acoustic dynamics, human performance measurement and analysis, nerve stimulation, electromyography, motor control and stimulation; and hardware and software applications for rehabilitation engineering and assistive devices.
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