Fuzzy Rule-based Alertness State Classification based on the Optimization of EEG Rhythm/Channel Combinations

Ahmed Al-Ani and Mostefa Mesbah


Alertness classification, drowsiness, EEG, Fuzzy rule-based classification system


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.

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