A Robust Framework for Driver Fatigue Detection From Eeg Signals Using Enhancement of Modified Z-Score and Multiple Machine Learning Architectures

Abstract

Physiological signals, such as electroencephalogram (EEG), are used to observe a driver’s brain activities. A portable EEG system provides several advantages, including ease of operation, cost-effectiveness, portability, and few physical restrictions. However, it can be challenging to analyse EEG signals as they often contain various artefacts, including muscle activities, eye blinking, and unwanted noises. This study utilised an independent component analysis (ICA) approach to eliminate such unwanted signals from the unprocessed EEG data of 12 young, physically fit male participants between the ages of 19 and 24 who took part in a driving simulation. Furthermore, driver fatigue state detection was carried out using multichannel EEG signals obtained from O1, O2, Fp1, Fp2, P3, P4, F3, and F4. An enhanced modified z-score was utilised with features extracted from a time-frequency domain continuous wavelet transform (CWT) to elevate the reliability of driver fatigue classification. The proposed methodology offers several advantages. First, multichannel EEG analysis improves the accuracy of sleep stage detection, which is vital for accurate driver fatigue detection. Second, an enhanced modified z-score in feature extraction is more robust than conventional z-score techniques, making it more effective for removing outlier values and improving classification accuracy. Third, the proposed approach for detecting driver fatigue employs multiple machine learning classifiers, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Artificial Neural Networks (ANNs) that utilise Long Short-Term Memory (LSTM), and also machine learning techniques like Support Vector Machines (SVM). The evaluation of five classifiers was performed through 5-fold cross-validation. The outcomes indicate that the suggested framework attains exceptional precision in identifying driver fatigue, with an average accuracy rate of 96.07%. Among the classifiers, the ANN classifier achieved the most significant precision of 99.65%, and the SVM classifier ranked second with an accuracy of 97.89%. Based on the results of the receiver operating characteristic (ROC) and area under the curve (AUC) analysis, it was observed that all the classifiers had an outstanding performance, with an average AUC value of 0.95. This study’s contribution lies in presenting a comprehensive and effective framework that can accurately detect driver fatigue from EEG signals. ABSTRAK: Isyarat fisiologi, seperti elektroencefalogram (EEG), digunakan bagi memerhati aktiviti otak pemandu. Sistem EEG mudah alih menyediakan beberapa kelebihan, termasuk kemudahan operasi, keberkesanan kos, mudah alih dan sedikit sekatan fizikal. Namun, isyarat EEG mungkin sukar dianalisis kerana ia sering mengandungi pelbagai artifak, termasuk aktiviti otot, mata berkedip dan bunyi yang tidak diingini. Kajian ini menggunakan pendekatan analisis komponen bebas (ICA) bagi membuang isyarat tidak diperlukan daripada data EEG yang belum diproses daripada 12 peserta lelaki muda, cergas fizikal berumur 19 hingga 24 tahun yang mengambil bahagian dalam simulasi pemanduan. Tambahan, pengesanan keadaan lesu pemandu telah dijalankan menggunakan isyarat EEG berbilang saluran yang diperoleh dari O1, O2, Fp1, Fp2, P3, P4, F3, dan F4. Penambah baik skor z digunakan dengan ciri diekstrak daripada transformasi wavelet berterusan (CWT) domain frekuensi masa bagi meningkatkan kebolehpercayaan klasifikasi keletihan pemandu. Metodologi yang dicadangkan menawarkan beberapa kelebihan. Pertama, analisis EEG berbilang saluran meningkatkan ketepatan pengesanan peringkat tidur, penting bagi pengesanan keletihan pemandu secara tepat. Kedua, penambah baik skor z dalam pengekstrak ciri adalah lebih teguh daripada teknik skor z konvensional, menjadikannya lebih berkesan bagi membuang unsur luaran dan meningkatkan ketepatan pengelasan. Ketiga, pendekatan yang dicadangkan bagi mengesan keletihan pemandu menggunakan pelbagai pengelas pembelajaran mesin, seperti Rangkaian Neural Konvolusi (CNN), Rangkaian Neural Berulang (RNN), Rangkaian Neural Buatan (ANN) yang menggunakan Memori Jangka Pendek Panjang (LSTM), dan juga teknik pembelajaran mesin seperti Mesin Vektor Sokongan (SVM). Penilaian lima pengelas dilakukan melalui pengesahan silang 5 kali ganda. Dapatan kajian menunjukkan cadangan rangka kerja ini mencapai ketepatan yang luar biasa dalam mengenal pasti keletihan pemandu, dengan kadar ketepatan purata 96.07%. Antara kesemua pengelas, pengelas ANN mencapai ketepatan paling ketara sebanyak 99.65%, dan pengelas SVM menduduki tempat kedua dengan ketepatan 97.89%. Berdasarkan keputusan analisis ciri operasi penerima (ROC) dan kawasan di bawah lengkung (AUC), didapati semua pengelas mempunyai prestasi cemerlang, dengan purata nilai AUC 0.95. Sumbangan kajian ini adalah terletak pada rangka kerja yang komprehensif dan berkesan mengesan keletihan pemandu secara tepat melalui isyarat EEG.

A Novel Approach for Automatic Detection of Driver Fatigue Using EEG Signals Based on Graph Convolutional Networks

“…In the following, recent research that is based on deep learning is reviewed. Abdubrani et al [17] presented a new model for automatic driver fatigue detection. These researchers used 12 participants and recorded the EEG signals of the participants.…”

“…As mental fatigue develops, the alpha rhythm’s intensity rises when the eyes are open and falls when they are closed [ 16 ]. Based on the above information, out of 19 electrodes related to EEG, only 4 electrodes, including P4, C3, O1, and O2, have been used to detect driver fatigue automatically [ 16 , 17 ]. Accordingly, the rest of the electrodes are not included in the processing process.…”