A Deep Learning Approach to Detect Microsleep Using Various Forms of EEG Signal

Author:

Sangeetha S. K. B.1,Mathivanan Sandeep Kumar2,Muthukumaran V.3ORCID,Pughazendi N.4,Jayagopal Prabhu2,Uddin Md Salah5ORCID

Affiliation:

1. Department of Computer Science and Engineering, SRM Institute of Science and Technology, Chennai, Tamilnadu, India

2. School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India

3. Department of Mathematics, College of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur 603203, Tamilnadu, India

4. Department of Computer Science and Engineering, Panimalar Engineering College, Poonamallee, Chennai 600123, Tamilnadu, India

5. Department of Multimedia and Creative Technology, Daffodil International University, Dhaka-1207, Bangladesh

Abstract

Electroencephalography (EEG) is a reliable method for identifying the onset of sleepiness behind the wheel. Using EEG technology for driving fatigue detection still presents challenges in extracting informative elements from noisy EEG signals. Due to their extensive computational parallelism, which is similar to how the brain processes information, neural networks have been explored as potential solutions for extracting relevant information from EEG data. The existing machine learning frameworks suffer from high computing costs and slow convergence, both of which contribute to low classification accuracy and efficiency due to the large number of hyper parameters that need to be improved. It is necessary to automate this micronap detection process before it can be used in real-time scenarios. To distinguish between micronap and non-micronap states, a deep neural network (DNN) framework is developed in this research using different EEG representations as input. Additional EEG representations utilized in this investigation include cleaned EEG as a time series, log-power spectrum, 2D-spatial map of log-power spectrum, and raw EEG. Finally, traditional machine learning algorithms are evaluated for their effectiveness in detecting micronaps from these EEG inputs. The findings suggest that micronap detection can be greatly improved by combining cleaned EEG with DNN.

Publisher

Hindawi Limited

Subject

General Engineering,General Mathematics

Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Enhancing heart disease prediction with reinforcement learning and data augmentation;Systems and Soft Computing;2024-12

2. Addressing Imbalanced EEG Data for Improved Microsleep Detection: An ADASYN, FFT and LDA-Based Approach;Diyala Journal of Engineering Sciences;2024-09-01

3. In the Blink of an Eye: Insomnia Microexpression Classification Using CNN-LSTM;2024 4th International Conference on Innovative Practices in Technology and Management (ICIPTM);2024-02-21

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