Abstract
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, ß 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.
Original language | English |
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Pages | 146-153 |
Number of pages | 8 |
DOIs | |
Publication status | Published - 2014 |
Event | IASTED International Conference on Biomedical Engineering, BioMed 2014 - Zurich, Switzerland Duration: Jun 23 2014 → Jun 25 2014 |
Other
Other | IASTED International Conference on Biomedical Engineering, BioMed 2014 |
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Country/Territory | Switzerland |
City | Zurich |
Period | 6/23/14 → 6/25/14 |
Keywords
- Alertness classification
- Differential Evolution
- Drowsiness
- EEG
- Fuzzy Rule-Based Classification System
- Variable selection
ASJC Scopus subject areas
- Modelling and Simulation