PAFWF-EEGC Net: parallel adaptive feature weight fusion based on EEG-dynamic characteristics using channels neural network for driver drowsiness detection

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dc.authoridhttps://orcid.org/0000-0003-3144-5232
dc.contributor.authorAbdulwahhab, Ali Hussein
dc.contributor.authorMyderrizi, Indrit
dc.contributor.authorYurdakul, Muhammet Mustafa
dc.date.accessioned2025-06-17T14:21:03Z
dc.date.available2025-06-17T14:21:03Z
dc.date.issued2025
dc.departmentMühendislik ve Mimarlık Fakültesi
dc.description.abstractDrowsy driving is considered one of the most dangerous causes of road accidents and deaths worldwide. Drivers’ concentration is directly affected by fatigue, which affects their reaction time, reducing their attention and decision-making ability on the road. This can often lead to dangerous situations. With the development of Human Computer Interface systems and the rise of intelligent transportation systems, examining the effects of driver fatigue has become more critical, and research aimed at reducing the risk of fatigue-related accidents has gained importance. For this purpose, this study proposes a Parallel Adaptive Feature Weight Fusion based on EEG-Dynamic Characteristics using Channels Neural Network (PAFWF-EEGC Net) to detect the driver drowsiness condition. Two signal processing techniques are used to extract EEG dynamic features: first, Continuous Wavelet Transform (CWT) to capture the spectral-temporal features by accurately estimating both time and frequency localizations, and second, Fast Fourier Transform (FFT)—Power Spectrum Density (PSD) to convert the signals from the time domain to the frequency domain and show the distribution of signal power over frequency. These extracted dynamic features are passed to Attention channels and Parallel Adaptive Feature Fusion to integrate the most relevant feature channels to detect mental state. Furthermore, three processing dataset scenarios and cross-validation techniques are used to validate the Net. The Net showed excellent performance through ninefold/3rd scenario by achieving 98% detection accuracy, and 84%, 88.75%, 93.8% average detection accuracy through 1st, 2nd, 3rd scenarios respectively
dc.identifier.doi10.1007/s11760-025-04102-x
dc.identifier.issn1863-1703
dc.identifier.issn1863-1711
dc.identifier.issue7
dc.identifier.urihttps://hdl.handle.net/11363/9935
dc.identifier.volume19
dc.identifier.wos001483008300003
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.institutionauthoridhttps://orcid.org/0000-0003-3144-5232
dc.language.isoen
dc.publisherSPRINGER LONDON LTD, 236 GRAYS INN RD, 6TH FLOOR, LONDON WC1X 8HL, ENGLAND
dc.relation.ispartofSIGNAL IMAGE AND VIDEO PROCESSING
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectElectroencephalogram (EEG)
dc.subjectAttention channel
dc.subjectAdaptive feature weight fusion
dc.subjectConvolution neural network (CNN)
dc.subjectDrowsiness driver
dc.titlePAFWF-EEGC Net: parallel adaptive feature weight fusion based on EEG-dynamic characteristics using channels neural network for driver drowsiness detection
dc.typeArticle

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