Abstract
Pilot's cognitive states induced by mental fatigue, distraction, and workload could be a cause of catastrophic accidents. Therefore, many methods for the detection of pilot cognitive states have been proposed in previous studies. Especially, neuro-and peripheral physiological measures (PPMs) such as electroencephalogram (EEG), electrocardiogram (ECG), respiration, and electrodermal activity (EDA) were employed to develop the novel flight assistant technologies for assurance of pilot's safety. However, each study investigated only one kind of state. Also, they did not consider the feature optimization for each subject. In this paper, we propose a method for the recognition of pilot's diversified mental states during simulated flight. The method selects the most fitted features for each subject based on the statistical analysis. The results show that the proposed method is superior to previous methods. Consequently, it shows that the pilot assistant system based on human-computer interaction (HCI) technologies could be facilitated in real-world.
| Original language | English |
|---|---|
| Title of host publication | 7th International Winter Conference on Brain-Computer Interface, BCI 2019 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| ISBN (Electronic) | 9781538681169 |
| DOIs | |
| Publication status | Published - 2019 Feb 1 |
| Event | 7th International Winter Conference on Brain-Computer Interface, BCI 2019 - Gangwon, Korea, Republic of Duration: 2019 Feb 18 → 2019 Feb 20 |
Publication series
| Name | 7th International Winter Conference on Brain-Computer Interface, BCI 2019 |
|---|
Conference
| Conference | 7th International Winter Conference on Brain-Computer Interface, BCI 2019 |
|---|---|
| Country | Korea, Republic of |
| City | Gangwon |
| Period | 19/2/18 → 19/2/20 |
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Keywords
- electroencephalography
- passive brain computer interface
- peripheral physiological meausrescomponent
- pilot inattention
- pilot safety
- pilot's mental states
ASJC Scopus subject areas
- Artificial Intelligence
- Human-Computer Interaction
- Signal Processing
- Neuroscience (miscellaneous)
Cite this
Recognition of Pilot's Cognitive States based on Combination of Physiological Signals. / Han, Soo Yeon; Kim, Jeong Woo; Lee, Seong Whan.
7th International Winter Conference on Brain-Computer Interface, BCI 2019. Institute of Electrical and Electronics Engineers Inc., 2019. 8737317 (7th International Winter Conference on Brain-Computer Interface, BCI 2019).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Recognition of Pilot's Cognitive States based on Combination of Physiological Signals
AU - Han, Soo Yeon
AU - Kim, Jeong Woo
AU - Lee, Seong Whan
PY - 2019/2/1
Y1 - 2019/2/1
N2 - Pilot's cognitive states induced by mental fatigue, distraction, and workload could be a cause of catastrophic accidents. Therefore, many methods for the detection of pilot cognitive states have been proposed in previous studies. Especially, neuro-and peripheral physiological measures (PPMs) such as electroencephalogram (EEG), electrocardiogram (ECG), respiration, and electrodermal activity (EDA) were employed to develop the novel flight assistant technologies for assurance of pilot's safety. However, each study investigated only one kind of state. Also, they did not consider the feature optimization for each subject. In this paper, we propose a method for the recognition of pilot's diversified mental states during simulated flight. The method selects the most fitted features for each subject based on the statistical analysis. The results show that the proposed method is superior to previous methods. Consequently, it shows that the pilot assistant system based on human-computer interaction (HCI) technologies could be facilitated in real-world.
AB - Pilot's cognitive states induced by mental fatigue, distraction, and workload could be a cause of catastrophic accidents. Therefore, many methods for the detection of pilot cognitive states have been proposed in previous studies. Especially, neuro-and peripheral physiological measures (PPMs) such as electroencephalogram (EEG), electrocardiogram (ECG), respiration, and electrodermal activity (EDA) were employed to develop the novel flight assistant technologies for assurance of pilot's safety. However, each study investigated only one kind of state. Also, they did not consider the feature optimization for each subject. In this paper, we propose a method for the recognition of pilot's diversified mental states during simulated flight. The method selects the most fitted features for each subject based on the statistical analysis. The results show that the proposed method is superior to previous methods. Consequently, it shows that the pilot assistant system based on human-computer interaction (HCI) technologies could be facilitated in real-world.
KW - electroencephalography
KW - passive brain computer interface
KW - peripheral physiological meausrescomponent
KW - pilot inattention
KW - pilot safety
KW - pilot's mental states
UR - http://www.scopus.com/inward/record.url?scp=85068344963&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85068344963&partnerID=8YFLogxK
U2 - 10.1109/IWW-BCI.2019.8737317
DO - 10.1109/IWW-BCI.2019.8737317
M3 - Conference contribution
AN - SCOPUS:85068344963
T3 - 7th International Winter Conference on Brain-Computer Interface, BCI 2019
BT - 7th International Winter Conference on Brain-Computer Interface, BCI 2019
PB - Institute of Electrical and Electronics Engineers Inc.
ER -