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首页> 外文会议>Proceedings of the 17th IASTED international conference on robotics applications ; Proceedings of the 11th IASTED international conference on biomedical engineering >FUZZY RULE-BASED ALERTNESS STATE CLASSIFICATION BASED ON THE OPTIMIZATION OF EEG RHYTHM/CHANNEL COMBINATIONS
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FUZZY RULE-BASED ALERTNESS STATE CLASSIFICATION BASED ON THE OPTIMIZATION OF EEG RHYTHM/CHANNEL COMBINATIONS

机译：基于脑电节奏/通道组合优化的基于规则的疲劳状态分类

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摘要

This paper presents a method for automatically selecting the optimal EEG rhythm/channel combination capable of classifying the different human alertness states. We considered four alertness states, namely 'engaged', 'calm', 'drowsy', and 'asleep'. Energies associated with the conventional EEG rhythms, δ, θ, α, β and γ, extracted from overlapping segments of the different EEG channels were used as features. The proposed method is a two-stage process. In the first stage, the optimal brain regions, represented by a set of EEG channels, are identified. In the second stage, a fuzzy rule-based alertness classification system (FRBACS) is developed to select the optimal EEG rhythms extracted from the previously selected EEG channels. The IF-THEN rules used in FRBACS are constructed using a novel bi-level differential evolution (DE) based search algorithm. Unlike most of the existing classification methods, the proposed classification approach reveals easy to interpret rules that describe each of the alertness states.
机译：本文提出了一种能够自动选择最佳EEG节奏/通道组合的方法，该方法能够对不同的人类警觉状态进行分类。我们考虑了四个警觉状态，即“订婚”，“镇静”，“困倦”和“睡着”。从不同的EEG通道的重叠部分提取的与常规EEG节奏相关的能量δ，θ，α，β和γ被用作特征。所提出的方法是一个两阶段的过程。在第一阶段，确定由一组EEG通道代表的最佳大脑区域。在第二阶段，开发了基于模糊规则的警觉分类系统（FRBACS），以选择从先前选择的EEG通道中提取的最佳EEG节奏。 FRBACS中使用的IF-THEN规则是使用一种新颖的基于两级差分进化（DE）的搜索算法构建的。与大多数现有分类方法不同，建议的分类方法揭示了易于解释的描述每个警报状态的规则。

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