2020 · 14 citations · 10 references
ComputingComputer ScienceNeuroscienceCognitive NeuroscienceFeature EngineeringMachine LearningElectroencephalogram AnalysisElectroencephalogram Signal ProcessingElectroencephalographyPattern RecognitionSignal ProcessingTraffic SafetyCognitive DistractionDiscrete Wavelet TransformDriver DistractionEeg Feature ExtractionElectroencephalogram Signal AnalysisElectroencephalography Feature ExtractionVisual DistractionWavelet Transform
Driver distraction is considered as major factors in most of the traffic accidents. Driving errors may arise due to the distraction of the drivers. The aim of this paper is to analyze the EEG signals to detect distractive driving. Data from 10 different subjects were obtained and categorised into different frequency bands. Distractive driving is related with Theta band, so the Theta frequency band were decomposed using Discrete Wavelet Transform (DWT) and 17 different features were extracted. By enabling Principle Component Analysis (PCA) the accuracy rate was found for Cognitive Distraction and Visual Distraction using different machine learning algorithms. K Nearest Neighbour (KNN) performed well when compared to other machine learning algorithms with a better accuracy rate of 71.1%.
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