Korea University · 神経科学
Professor Dae-Hyeok Lee's research lab specializes in the development of advanced brain-computer interface (BCI) systems, with a strong focus on non-invasive electroencephalogram (EEG)-based mental state classification for aviation safety. The lab pioneers deep learning-driven approaches to detect and classify critical pilot mental states such as fatigue, workload, and distraction in realistic flight environments. Their work emphasizes real-time, reliable, and non-invasive monitoring of cognitive conditions to prevent human-error-related accidents.
Figures are computed from collected data and may differ slightly.
Various kinds of nanostructured materials have been extensively investigated as lithium ion battery electrode materials derived from their numerous advantageous features including enhanced energy and power density and cyclability. However, little is known about the microscopic origin of how nanostructures can enhance lithium storage performance. Herein, we identify the microscopic origin of enhanced lithium storage in anatase TiO<sub>2</sub> nanostructure and report a reversible and stable route
Non-invasive brain-computer interface (BCI) has been developed for recognizing and classifying human mental states with high performances. Specifically, classifying pilots' mental states accurately is a critical issue because their cognitive states, which are induced by mental fatigue, workload, and distraction, may be fundamental in catastrophic accidents. In this study, we present an electroencephalogram (EEG) classification of four mental states (fatigue, workload, distraction, and the normal
Detection of the pilots’ mental states is particularly critical because their abnormal mental states (AbSs) could cause catastrophic accidents. In this study, we presented the feasibility of classifying the various specific AbSs (namely, low fatigue, high fatigue, low workload, high workload, low distraction, and high distraction) by applying the deep learning method. To the best of our knowledge, this study is the first attempt to classify multiple AbSs of pilots. We proposed the hybrid deep ne
Brain-computer interface (BCI) enables the communication between humans and devices by reflecting humans' intentions and status. Endogenous BCI is the imagined-based BCI and it has the advantage that the fatigue level of the body, especially the eyes, is relatively low and no additional equipment for offering stimulation is required. When conducting imagined speech, one of the endogenous BCI paradigms, the users imagine the pronunciation as if actually speaking. In contrast, overt speech is that
Non-invasive brain-computer interface technology has been developed for detecting human mental states with high performances. Detection of the pilots’ mental states is particularly critical because their abnormal mental states could cause catastrophic accidents. In this study, we presented the feasibility of classifying distraction levels (namely, normal state, low distraction, and high distraction) by applying the deep learning method. To the best of our knowledge, this study is the first attem
The detection of pilots' mental states is critical, as abnormal mental states have the potential to cause catastrophic accidents. This study demonstrates the feasibility of using deep learning techniques to classify different fatigue levels, specifically a normal state, low fatigue, and high fatigue. To the best of our knowledge, this is the first study to classify fatigue levels in pilots. Our approach employs the hybrid deep neural network comprising five convolutional blocks and one long shor
The detection of pilots’ mental states is important due to the potential for their abnormal mental states to result in catastrophic accidents. This study introduces the feasibility of employing deep learning techniques to classify different work-load levels, specifically normal state, low workload, and high workload. To the best of our knowledge, this study is the first attempt to classify workload levels of pilots. Our approach involves the hybrid deep neural network that consists of five convo
Brain-computer interface (BCI) is the technology that enables the communication between humans and devices by reflecting status and intentions of humans. When conducting imagined speech, the users imagine the pronunciation as if actually speaking. In the case of decoding imagined speech-based EEG signals, complex task can be conducted more intuitively, but decoding performance is lower than that of other BCI paradigms. We modified our previous model for decoding imagined speech-based EEG signals
The detection of pilots' mental states is critical, as abnormal mental states have the potential to cause catastrophic accidents. This study demonstrates the feasibility of using deep learning techniques to classify different fatigue levels, specifically a normal state, low fatigue, and high fatigue. To the best of our knowledge, this is the first study to classify fatigue levels in pilots. Our approach employs the hybrid deep neural network comprising five convolutional blocks and one long shor
Non-invasive brain-computer interface (BCI) has been developed for\nunderstanding users' intentions by using electroencephalogram (EEG) signals.\nWith the recent development of artificial intelligence, there have been many\ndevelopments in the drone control system. BCI characteristic that can reflect\nthe users' intentions led to the BCI-based drone control system. When using\ndrone swarm, we can have more advantages, such as mission diversity, than using\na single drone. In particular, BCI-base
Brain-computer interface (BCI) is the technology that enables the\ncommunication between humans and devices by reflecting status and intentions of\nhumans. When conducting imagined speech, the users imagine the pronunciation as\nif actually speaking. In the case of decoding imagined speech-based EEG\nsignals, complex task can be conducted more intuitively, but decoding\nperformance is lower than that of other BCI paradigms. We modified our previous\nmodel for decoding imagined speech-based EEG s
Non-invasive brain-computer interface technology has been developed for detecting human mental states with high performances. Detection of the pilots' mental states is particularly critical because their abnormal mental states could cause catastrophic accidents. In this study, we presented the feasibility of classifying distraction levels (namely, normal state, low distraction, and high distraction) by applying the deep learning method. To the best of our knowledge, this study is the first attem
The detection of pilots' mental states is important due to the potential for their abnormal mental states to result in catastrophic accidents. This study introduces the feasibility of employing deep learning techniques to classify different workload levels, specifically normal state, low workload, and high workload. To the best of our knowledge, this study is the first attempt to classify workload levels of pilots. Our approach involves the hybrid deep neural network that consists of five convol
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