Continuous EEG Decoding of Pilots’ Mental States Using Multiple Feature Block-Based Convolutional Neural Network

Lee, Dae Hyeok; Jeong, Ji-Hoon; Kim, Kiduk; Yu, Baek Woon; Lee, Seong Whan

doi:10.1109/access.2020.3006907

Cited by 61 publications

(31 citation statements)

References 64 publications

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“…It is worth mentioning that we employed the ASFM transfer learning algorithm to our data as well but did not achieve promising results. Even though it is difficult to directly compare our study to [48,49] due to differences in the experimental (e.g., type of task, number/length of trials) and analytical (e.g., preprocessing techniques, EEG features used, and classification algorithms) methods employed, the accuracies obtained are similar. The fact that our study involved a potentially much more complex scenario-that is, simultaneous classification of two states, where both states were confounding one another-makes our results even more encouraging.…”

Section: Discussionmentioning

confidence: 96%

“…Actually, online BCI studies are generally rather scarce. Recently, [48] presented an EEG-based classification of four mental states (fatigue, workload, distraction, and the normal state) for seven pilots in both offline and pseudo-online analyses. They proposed a multiple feature block-based convolutional neural network (MFB-CNN) with spatio-temporal EEG filters to recognize the pilot's current mental states.…”

Section: Discussionmentioning

confidence: 99%

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Simultaneous Classification of Both Mental Workload and Stress Level Suitable for an Online Passive Brain–Computer Interface

Bagheri

Power

2022

Sensors

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Research studies on EEG-based mental workload detection for a passive BCI generally focus on classifying cognitive states associated with the performance of tasks at different levels of difficulty, with no other aspects of the user’s mental state considered. However, in real-life situations, different aspects of the user’s state such as their cognitive (e.g., level of mental workload) and affective (e.g., level of stress/anxiety) states will often change simultaneously, and performance of a BCI system designed considering just one state may be unreliable. Moreover, multiple mental states may be relevant to the purposes of the BCI—for example both mental workload and stress level might be related to an aircraft pilot’s risk of error—and the simultaneous prediction of states may be critical in maximizing the practical effectiveness of real-life online BCI systems. In this study we investigated the feasibility of performing simultaneous classification of mental workload and stress level in an online passive BCI. We investigated both subject-specific and cross-subject classification approaches, the latter with and without the application of a transfer learning technique to align the distributions of data from the training and test subjects. Using cross-subject classification with transfer learning in a simulated online analysis, we obtained accuracies of 77.5 ± 6.9% and 84.1 ± 5.9%, across 18 participants for mental workload and stress level detection, respectively.

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Section: Discussionmentioning

confidence: 96%

Section: Discussionmentioning

confidence: 99%

Simultaneous Classification of Both Mental Workload and Stress Level Suitable for an Online Passive Brain–Computer Interface

Bagheri

Power

2022

Sensors

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“…With the development of neural network technology, there are some mental state recognition methods based on neural networks. In (21), four mental states were classified by electroencephalogram (EEG), and multi-feature block-based convolutional neural network combined with space-time EEG filter were used for recognizing the current mental state of the pilot. In (22), a connected structure of deep recurrent and 3D CNNs were proposed for learning EEG features without a priori knowledge crossing different tasks.…”

Section: Related Studymentioning

confidence: 99%

AI-Based Publicity Strategies for Medical Colleges: A Case Study of Healthcare Analysis

Wang

2022

Front. Public Health

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The health status and cognition of undergraduates, especially the scientific concept of healthcare, are particularly important for the overall development of society and themselves. The survey shows that there is a significant lack of knowledge about healthcare among undergraduates in medical college, even among medical undergraduates, not to mention non-medical undergraduates. Therefore, it is a good way to publicize healthcare lectures or electives for undergraduates in medical college, which can strengthen undergraduates' cognition of healthcare and strengthen the concept of healthcare. In addition, undergraduates' emotional and mental state in healthcare lectures or electives can be analyzed to determine whether undergraduates have hidden illnesses and how well they understand the healthcare content. In this study, at first, a mental state recognition method of undergraduates in medical college based on data mining technology is proposed. Then, the vision-based expression and posture are used for expanding the channels of emotion recognition, and a dual-channel emotion recognition model based on artificial intelligence (AI) during healthcare lectures or electives in a medical college is proposed. Finally, the simulation is driven by TensorFlow with respect to mental state recognition of undergraduates in medical college and emotion recognition. The simulation results show that the recognition accuracy of mental state recognition of undergraduates in a medical college is more than 92%, and the rejection rate and misrecognition rate are very low, and false match rate and false non-match rate of mental state recognition is significantly better than the other three benchmarks. The emotion recognition of the dual-channel emotion recognition method is over 96%, which effectively integrates the emotional information expressed by facial expressions and postures.

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“…The recent emergence of low cost EEG headsets has driven new researches (such as interaction with home devices, teachinglearning educative methods, and mentally control robotic arms) further than the medical screening of neurological disorders. In the particular case of cognitive state assessment, EEG alone is becoming the preferred sensor for addressing its characterization [11][12][13]. However, there is not enough evidence in the literature to validate how well models generalize to new subjects performing tasks of a workload similar to the ones included during the model's training.…”

Section: Introductionmentioning

confidence: 99%

Recognition of the Mental Workloads of Pilots in the Cockpit Using EEG Signals

et al. 2022

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The commercial flightdeck is a naturally multi-tasking work environment, one in which interruptions are frequent come in various forms, contributing in many cases to aviation incident reports. Automatic characterization of pilots’ workloads is essential to preventing these kind of incidents. In addition, minimizing the physiological sensor network as much as possible remains both a challenge and a requirement. Electroencephalogram (EEG) signals have shown high correlations with specific cognitive and mental states, such as workload. However, there is not enough evidence in the literature to validate how well models generalize in cases of new subjects performing tasks with workloads similar to the ones included during the model’s training. In this paper, we propose a convolutional neural network to classify EEG features across different mental workloads in a continuous performance task test that partly measures working memory and working memory capacity. Our model is valid at the general population level and it is able to transfer task learning to pilot mental workload recognition in a simulated operational environment.

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Continuous EEG Decoding of Pilots’ Mental States Using Multiple Feature Block-Based Convolutional Neural Network

Cited by 61 publications

References 64 publications

Simultaneous Classification of Both Mental Workload and Stress Level Suitable for an Online Passive Brain–Computer Interface

Simultaneous Classification of Both Mental Workload and Stress Level Suitable for an Online Passive Brain–Computer Interface

AI-Based Publicity Strategies for Medical Colleges: A Case Study of Healthcare Analysis

Recognition of the Mental Workloads of Pilots in the Cockpit Using EEG Signals

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