Ahmed Al-Ani,Mostefa Mesbah
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Alertness classification; drowsiness; EEG; Fuzzy Rule-Based Classification System; Variable selection; Differen-tial Evolution.
This paper presents a method for automatically selecting the optimal EEG rhythm/channel combination capable of classifying the different human alertness states. We con- sidered four alertness states, namely ’engaged’, ’calm’, ’drowsy’, and ’asleep’. Energies associated with the con- ventional 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 pro- cess. In the first stage, the optimal brain regions, repre- sented by a set of EEG channels, are identified. In the sec- ond 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 al- gorithm. Unlike most of the existing classification meth- ods, the proposed classification approach reveals easy to interpret rules that describe each of the alertness states.
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