Research Publications

         Journals

1. Mishra, S., Seth, S., Jain, S., Pant, V., Parikh, J., Chugh, N., & Puri, Y. (2025). An emotionally intelligent haptic system–An efficient solution for anxiety detection and mitigation. Computer Methods and Programs in Biomedicine, 260, 108590.

2. Aditya Tomar, A., Saxena, A., Sharma, D., Chugh, N., & Joon, R. Extractive Text Summarization Using Latent Semantic Analysis and Diversity Constraints. Journal of Multi Disciplinary Engineering Technologies, 18(01). 

3. Arora, S., & Chugh, N. EEG Signal Classification for Epileptic Seizure Detection: A Review of Machine Learning Approaches. Journal of Multi Disciplinary Engineering Technologies, 18(01). 

4. Chugh, N., & Aggarwal, S. (2024). Spatial Decoding for Gaze Independent Brain–Computer Interface Based on Covert Visual Attention Shift Using Electroencephalography. Clinical EEG and Neuroscience, 55(4), 477-485.

5. Chugh, N., Aggarwal, S., & Balyan, A. (2024). The hybrid deep learning model for identification of attention-deficit/hyperactivity disorder using EEG. Clinical EEG and Neuroscience, 55(1), 22-33.

6. Chugh, N., & Aggarwal, S. (2023). Hybrid Brain–Computer Interface Spellers: A Walkthrough Recent Advances in Signal Processing Methods and Challenges. International Journal of Human–Computer Interaction, 39(15), 3096-3113. 

7. Aggarwal, S., & Chugh, N. (2022). Review of machine learning techniques for EEG based brain-computer interface. Archives of Computational Methods in Engineering, 29(5), 3001-3020. 

8. Aggarwal, S., & Chugh, N. (2020). Ethical Implications of Closed Loop Brain Device: 10-Year Review. Minds and Machines, 1-26.

9. Aggarwal, S., & Chugh, N. (2019). Signal processing techniques for motor imagery brain computer interface: A review. Array, 1, 100003.

10. Chugh, N., & Mishra, A. D. (2013). Assimilation of Four Layered Approach to NFR in Agile Requirement Engineering. International Journal of Computer Applications, 78(5).

     Conferences

1.  Aggarwal, S., Chugh, N., & Balyan, A. (2023, February). Identification of ADHD disorder in children using EEG based on visual attention task by ensemble deep learning. In Proceedings of International Conference on Data Science and Applications: ICDSA 2022, Volume 2 (pp. 243-259). Singapore: Springer Nature Singapore. 

2.  Aggarwal, S., Chugh, N., & Balyan, A. (2022). Decoding Visual Covert Attention Shift from EEG for Use in BCI. In ICT Systems and Sustainability: Proceedings of ICT4SD 2021, Volume 1 (pp. 883-893). Springer Singapore. 

3.  Aggarwal, S., & Chugh, N. (2020, January). A decade of EEG Analysis: Prospects & Challenges in Biometric System. In 2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence) (pp. 474-479). IEEE. 

4. Mishra, G., Joon, S. B. R., Parikh, J., & Chugh, N. TGT-Net: Graph-Transformer-Based Architecture for Drug-Target Interaction Prediction.