Deep convolutional neural network for drowsy student state detection

Zhao, Gang; Liu, Shan; Wang, Qi; Hu, Tao; Chen, Yawen; Lin, Luyu; Zhao, Dasheng

doi:10.1002/cpe.4457

Cited by 10 publications

(2 citation statements)

References 27 publications

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“…In the era of “big data,” transformation of large quantities of data into valuable knowledge has become increasingly important in various domains, such as the image recognition, speech recognition, and the EEG signals are also included in it. With the current exponential growth of the amount of data available, the large number of different formats, and the increasing computational power, and taking into account the expectations generated by Artificial Intelligence, as a new powerful tool to the service of humans and companies, many studies began focusing on EEG's research.…”

Section: Introductionmentioning

confidence: 99%

Deep learning for EEG data analytics: A survey

Lee

Jung

et al. 2019

Concurrency and Computation

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Summary In this work, we conducted a literature review about deep learning (DNN, RNN, CNN, and so on) for analyzing EEG data for decoding the activity of human's brain and diagnosing disease and explained details about various architectures for understanding the details of CNN and RNN. It has analyzed a word, which presented a model based on CNN and LSTM methods, and how these methods can be used to both optimize and set up the hyper parameters of deep learning architecture. Later, it is studied how semi‐supervised learning on EEG data analytics can be applied. We review some studies about different methods of semi‐supervised learning on EEG data analytics and discussing the importance of semi‐supervised learning for analyzing EEG data. In this paper, we also discuss the most common applications for human EEG research and review some papers about the application of EEG data analytics such as Neuromarketing, human factors, social interaction, and BCI. Finally, some future trends of development and research in this area, according to the theoretical background on deep learning, are given.

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Section: Introductionmentioning

confidence: 99%

Deep learning for EEG data analytics: A survey

Lee

Jung

et al. 2019

Concurrency and Computation

View full text Add to dashboard Cite

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“…Attribute features that contribute to the modeling not only encode the color and shape information of low‐level features but also associate the semantics with the features. A deep CNN is introduced in the work of Zhao et al for drowsy student state detection, which combines with the AdaBoost face detection algorithm and PERCOLS drowsy judgment . Liu et al extract features from the EEG recordings based on a hybrid dimension feature reduction scheme for emotion detection.…”

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confidence: 99%