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Original Article
Spatiotemporal Feature Extraction for Pilot State Monitoring: A Hybrid CNN-LSTM Approach
Swapnil Wanjare1
Dr. Vishwas Gaikwad2
1 2 Department of Electronics and Telecommunications, Sipna College of Engineering and Technology, Amravati, Maharashtra, India.
Published Online: November-December 2025
Pages: 66-69
Cite this article
↗ https://www.doi.org/10.59256/ijsreat.20250506011
References
1. H. A. K. Al-Saegh et al., "Enhanced Pilot Attention Monitoring: A Time-Frequency EEG Analysis Using CNN-LSTM Networks for Aviation
Safety," IEEE Access, vol. 13, pp. 1–15, 2025.
2. A. Bussolan, S. Baraldo, O. Avram, et al., "MultiPhysio-HRC: A Multimodal Dataset for Human-Robot Collaboration," IEEE Trans. Hum.-
Mach. Syst., submitted for publication, 2025.
3. J. Chen, "Spatiotemporal Feature Extraction in EEG using CNN-LSTM Autoencoders," in Proc. 5th Int. Conf. Artif. Intell. Comput. Eng.
(ICAICE), 2024, pp. 1–6.4. F. Del Pup, A. Zanola, L. F. Tshimanga, et al., "The More, the Better? Evaluating the Role of EEG Preprocessing for Deep Learning
Applications," IEEE Trans. Neural Syst. Rehabil. Eng., vol. 33, pp. 1–10, Mar. 2025.
5. S. Elalamy et al., "Multi-Modal Emotion Recognition Using Deep Learning," IEEE Access, vol. 10, pp. 1–12, 2022.
6. M. A. Khan, "Preprocessing Techniques for EEG Data," IEEE Trans. Biomed. Eng., vol. 68, no. 8, pp. 1–13, 2021.
7. A. Kumar, "SeizureSight: A CNN-LSTM Hybrid Model for EEG-Based Seizure Prediction," in Proc. 3rd Int. Conf. Appl. Artif. Intell.
Comput. (ICAAIC), 2024, pp. 1–6.
8. W. Li, Q. Chen, Z. Hou, et al., "Enhancing Automated Seizure Detection via Self-Calibrating Spatial-Temporal EEG Features with SC-
LSTM," IEEE J. Biomed. Health Inform., vol. PP, no. 99, pp. 1–12, Sep. 2025.
9. Y. Li et al., "Spatial-Temporal Feature Fusion Neural Network for EEG-Based Emotion Recognition," IEEE Trans. Instrum. Meas., vol. 71,
pp. 1–12, 2022.
10. C. Massaroni et al., "Contactless respiratory rate monitoring using thermal and visible imaging: A pilot study," IEEE Trans. Instrum. Meas.,
vol. 72, pp. 1–12, 2023.
11. A. Mishra, K. K. Shrivastava, A. B. Anto, and N. A. Quadir, "Reducing Commercial Aviation Fatalities Using Support Vector Machines,"
in Proc. Int. Conf. Smart Syst. Inventive Technol. (ICSSIT), Tirunelveli, India, 2019, pp. 360–364.
12. N. Modi, "Physiological signal-based mental stress detection using hybrid deep learning models," Discover Artif. Intell., vol. 5, no. 1, Jul.
2025.
13. Z. J. Wang et al., "Emotion Recognition from Multi-Modal Biosignals," in Proc. IEEE Int. Conf. Bioinform. Biomed. (BIBM), 2022, pp. 1–
5.
Safety," IEEE Access, vol. 13, pp. 1–15, 2025.
2. A. Bussolan, S. Baraldo, O. Avram, et al., "MultiPhysio-HRC: A Multimodal Dataset for Human-Robot Collaboration," IEEE Trans. Hum.-
Mach. Syst., submitted for publication, 2025.
3. J. Chen, "Spatiotemporal Feature Extraction in EEG using CNN-LSTM Autoencoders," in Proc. 5th Int. Conf. Artif. Intell. Comput. Eng.
(ICAICE), 2024, pp. 1–6.4. F. Del Pup, A. Zanola, L. F. Tshimanga, et al., "The More, the Better? Evaluating the Role of EEG Preprocessing for Deep Learning
Applications," IEEE Trans. Neural Syst. Rehabil. Eng., vol. 33, pp. 1–10, Mar. 2025.
5. S. Elalamy et al., "Multi-Modal Emotion Recognition Using Deep Learning," IEEE Access, vol. 10, pp. 1–12, 2022.
6. M. A. Khan, "Preprocessing Techniques for EEG Data," IEEE Trans. Biomed. Eng., vol. 68, no. 8, pp. 1–13, 2021.
7. A. Kumar, "SeizureSight: A CNN-LSTM Hybrid Model for EEG-Based Seizure Prediction," in Proc. 3rd Int. Conf. Appl. Artif. Intell.
Comput. (ICAAIC), 2024, pp. 1–6.
8. W. Li, Q. Chen, Z. Hou, et al., "Enhancing Automated Seizure Detection via Self-Calibrating Spatial-Temporal EEG Features with SC-
LSTM," IEEE J. Biomed. Health Inform., vol. PP, no. 99, pp. 1–12, Sep. 2025.
9. Y. Li et al., "Spatial-Temporal Feature Fusion Neural Network for EEG-Based Emotion Recognition," IEEE Trans. Instrum. Meas., vol. 71,
pp. 1–12, 2022.
10. C. Massaroni et al., "Contactless respiratory rate monitoring using thermal and visible imaging: A pilot study," IEEE Trans. Instrum. Meas.,
vol. 72, pp. 1–12, 2023.
11. A. Mishra, K. K. Shrivastava, A. B. Anto, and N. A. Quadir, "Reducing Commercial Aviation Fatalities Using Support Vector Machines,"
in Proc. Int. Conf. Smart Syst. Inventive Technol. (ICSSIT), Tirunelveli, India, 2019, pp. 360–364.
12. N. Modi, "Physiological signal-based mental stress detection using hybrid deep learning models," Discover Artif. Intell., vol. 5, no. 1, Jul.
2025.
13. Z. J. Wang et al., "Emotion Recognition from Multi-Modal Biosignals," in Proc. IEEE Int. Conf. Bioinform. Biomed. (BIBM), 2022, pp. 1–
5.
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