Automatic detection of drowsiness in EEG records based on time analysis

Dey, Ishita; Jagga, Shivansh; Prasad, Amit; Sharmila, A.; Borah, Siddharth K; Rao, G. Sethuram

doi:10.1109/ipact.2017.8245167

Cited by 5 publications

(1 citation statement)

References 12 publications

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“…A KNN classifier with k = 3 yielded the best accuracy of 95.24% in [30], an SVM yielded the best accuracy of 83.71% in [37], an ANN yielded a better accuracy of 86.5% compared with an SVM in [32], and linear regression was found to provide an accuracy of 90% in [39]. In [40], the KNN classifier obtained an accuracy of 91% when applied to features extracted using short-time Fourier transform (STFT) and was found to outperform an LDA and an SVM when performing classification from features extracted using time analysis [40]. However, an SVM outperformed a KNN when using the determinism (DET) feature extracted by the recurrence quantification analysis.…”

Section: Related Workmentioning

confidence: 99%

Drowsiness Detection Using Ocular Indices from EEG Signal

Tarafder

Badruddin

Yahya

et al. 2022

Sensors

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Drowsiness is one of the main causes of road accidents and endangers the lives of road users. Recently, there has been considerable interest in utilizing features extracted from electroencephalography (EEG) signals to detect driver drowsiness. However, in most of the work performed in this area, the eyeblink or ocular artifacts present in EEG signals are considered noise and are removed during the preprocessing stage. In this study, we examined the possibility of extracting features from the EEG ocular artifacts themselves to perform classification between alert and drowsy states. In this study, we used the BLINKER algorithm to extract 25 blink-related features from a public dataset comprising raw EEG signals collected from 12 participants. Different machine learning classification models, including the decision tree, the support vector machine (SVM), the K-nearest neighbor (KNN) method, and the bagged and boosted tree models, were trained based on the seven selected features. These models were further optimized to improve their performance. We were able to show that features from EEG ocular artifacts are able to classify drowsy and alert states, with the optimized ensemble-boosted trees yielding the highest accuracy of 91.10% among all classic machine learning models.

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