Fuzzy logic-based automatic alertness state classification using multi-channel EEG data

Ahmed Al-Ani, Mostefa Mesbah, Bram Van Dun, Harvey Dillon

نتاج البحث

5 اقتباسات (Scopus)

ملخص

This paper represents an attempt to automatically classify alertness state using information extracted from multi-channel EEG. To reduce the amount of data and improve the performance, a channel selection method based on support vector machine (SVM) classifier has been performed. The features used for the EEG channel selection process and subsequently for alertness classification represent the energy values of the five EEG rhythms; namely δ, θ, α, β and γ. In order to identify the feature/channel combination that leads to the best alertness state classification performance, we used a fuzzy rule-based classification system (FRBCS) that utilizes differential evolution in constructing the rules. The results obtained using the FRBCS were found to be comparable to those of SVM but with the added advantage of revealing the rhythm/channel combination associated with each alertness state.

اللغة الأصليةEnglish
عنوان منشور المضيفNeural Information Processing - 20th International Conference, ICONIP 2013, Proceedings
الصفحات176-183
عدد الصفحات8
طبعةPART 1
المعرِّفات الرقمية للأشياء
حالة النشرPublished - 2013
منشور خارجيًانعم
الحدث20th International Conference on Neural Information Processing, ICONIP 2013 - Daegu
المدة: نوفمبر ٣ ٢٠١٣نوفمبر ٧ ٢٠١٣

سلسلة المنشورات

الاسمLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
الرقمPART 1
مستوى الصوت8226 LNCS
رقم المعيار الدولي للدوريات (المطبوع)0302-9743
رقم المعيار الدولي للدوريات (الإلكتروني)1611-3349

Other

Other20th International Conference on Neural Information Processing, ICONIP 2013
الدولة/الإقليمKorea, Republic of
المدينةDaegu
المدة١١/٣/١٣١١/٧/١٣

ASJC Scopus subject areas

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