Pallavi Kaushik

Pallavi Kaushik

Ph.D. student

Research Area

  • Brain Computer Interfaces
  • Deep Learning
  • Machine Learning

Contact Information


I am pursuing a double Ph.D. from the Indian Institute of Technology, Roorkee, and the University of Groningen, Netherlands. My research revolves around understanding depression by analyzing EEG and fMRI data using machine/deep learning. My research interests include Artificial Intelligence and major depressive disorder. I am also interested in understanding the cognitive states of the human brain by analyzing the EEG signals using deep learning to facilitate not only the development of robust Brain-Computer Interfaces but also to gain insights into how the brain works.


Tracking Depression and Elucidating its Mechanisms using Cognitive Neuroscience, EEG, and Machine Learning


Internships/Research Experience

  • Currently visiting University of Groningen, the Netherlands under the SPARC scheme to work on the project "Tracking Depression and Elucidating its Mechanisms using Cognitive Neuroscience, EEG, and Machine Learning" under the guidance of Dr. Marieke van Vugt.
  • Visited Osaka Prefecture University, Japan under the financial support of JST Sakura Science Plan to  have technical discussions in the areas of brain computer interface and deep learning.



  • Kaushik, P., Gupta, A., Roy, P. P., & Dogra, D. P. (2018). EEG-Based Age and Gender Prediction Using Deep BLSTM-LSTM Network Model. IEEE Sensors Journal, 19(7), 2634-2641.
  • Kumar, Pradeep, Subham Mukherjee, Rajkumar Saini, Pallavi Kaushik, Partha Pratim Roy, and Debi Prosad Dogra. "Multimodal Gait Recognition With Inertial Sensor Data and Video Using Evolutionary Algorithm." IEEE Transactions on Fuzzy Systems 27, no. 5 (2018): 956-965.
  • Kaushik, P., & Dutta, K. (2017, March). A Neural Network Model for Intrusion Detection Using a Game Theoretic Approach. In International Conference on Advanced Informatics for Computing Research (pp. 355-367). Springer, Singapore.


  • Presented a poster entitled "Machine Learning as a Tool to Disentangle Cognitive Processes in a Complex Setting of Tibetan Monastic Debate" in the 17th NVP conference on Brain and Cognition held in the Netherlands.