Dr Anirban Chowdhury
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Email
a.chowdhury@essex.ac.uk -
Location
5B.536, Colchester Campus
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Academic support hours
Tue 12.00-13.00 Wed 11.00-12.00
Profile
Biography
Dr. Anirban Chowdhury is a Lecturer in Neural Engineering and Robotics, School of ComputerScience and Electronic Engineering (CSEE), University if Essex (UoE) and a member of the Brain-computer interface and Neural Engineering (BCI-NE) Group, and Robotics Group at University of Essex. Prior to the joining at UoE Anirban was a postdoctoral Research Associate at the Northern Ireland Functional Brain Mapping Facility at Ulster University, Northern Ireland, UK. He holds a Ph.D. in Mechatronics from the Centre for Mechatronics at IIT Kanpur, India, M. Tech in Mechatronics from the School of Mechatronics and Robotics at IIEST, Shibpur, India, and B. Tech in Electronics and Communication Engineering from Kalyani Govt. Engineering College, India. He has contributed to two UK-India thematic partnership projects funded by the British Council, UK, and Department of Science and technology in India under grant UKIERI-DST-2013-14/126, DST-UKIERI-2016-17-0128, and DST-UKIERI2016-17-0128. He has also led the successful completion of two clinical trials on post-stroke robot-assisted neuro-rehabilitation, one in India (CTRI/2018/05/013876) and the other in the UK (ISRCTN1313909). His currentresearch interests are in the areas of robotic rehabilitation, brain-computerinterfaces, assistive technologies, human-robot co-operation, and autonomousmobile robotics. He has published several journal papers in the top journals of the field, including transactions and journals of the IEEE and Elsevier.
Qualifications
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Ph.D. Indian Institute of Technology Kanpur, (2018)
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M. Tech Indian Institute of Engineering Science and Technology, Shibpur, (2013)
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B.Tech Kalyani Govt. Engieering College, (2010)
Appointments
University of Essex
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Lecturer(R), School of Computer Science and Electronic Engineering (CSEE), University of Essex (30/8/2019 - present)
Research and professional activities
Research interests
Brain-computer Interfaces
Assistive Technology
Neurorehabilitation
Human-robot Co-operation
Autonomous Mobile Robots
Teaching and supervision
Current teaching responsibilities
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Team Project Challenge (CE101)
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Team Project Challenge (CE201)
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Control theory and practice (CE269)
Publications
Journal articles (16)
Khanna, S., Chowdhury, A., Dutta, A. and Subramanian, VK., (2024). SCSP-3: A Spectrally Augmented Common Spatial Pattern Approach for Robust Motor Imagery-based Brain-Computer Interface. IEEE Sensors Journal. 24 (5), 6634-6642
Kundu, S., Tomar, AS., Chowdhury, A., Thakur, G. and Tomar, A., (2024). Advancements in Temporal Fusion: A New Horizon for EEG Based Motor Imagery Classification. IEEE Transactions on Medical Robotics and Bionics, 1-1
Saideepthi,, P., Gaur, P., Chowdhury, A. and Pachori, RB., (2023). Sliding Window along with EEGNet based Prediction of EEG Motor Imagery. IEEE Sensors Journal. 23 (15), 17703-17713
Youssofzadeh, V., Roy, S., Chowdhury, A., Izadysadr, A., Parkkonen, L., Raghavan, M. and Prasad, G., (2023). Mapping & decoding cortical engagement during motor imagery, mental arithmetic, and silent word generation using MEG. Human Brain Mapping. 44 (8), 3324-3342
Gaur, P., Chowdhury, A., McCreadie, K., Pachori, RB. and Wang, H., (2022). Logistic Regression with Tangent Space based Cross-Subject Learning for Enhancing Motor Imagery Classification. IEEE Transactions on Cognitive and Developmental Systems. 14 (3), 1188-1197
Gaur, P., Gupta, H., Chowdhury, A., McCreadie, K., Pachori, RB. and Wang, H., (2021). A Sliding Window Common Spatial Pattern for Enhancing Motor Imagery Classification in EEG-BCI. IEEE Transactions on Instrumentation and Measurement. 70, 1-9
Chowdhury, A. and Andreu-Perez, J., (2021). Clinical Brain-Computer Interface Challenge 2020 (CBCIC at WCCI2020): Overview, methods and results. IEEE Transactions on Medical Robotics and Bionics. 3 (3), 661-670
Chatterjee, D., Chowdhury, A., Gavas, R., Sinha, A. and Saha, SK., (2021). Real time estimation of task specific self-confidence level based on brain signals. Multimedia Tools and Applications. 80 (13), 19203-19217
Chowdhury, A., Dutta, A. and Prasad, G., (2020). Corticomuscular co-activation based hybrid brain-computer interface for motor recovery monitoring. IEEE Access. 8, 174542-174557
Roy, S., Rathee, D., Chowdhury, A., McCreadie, K. and Prasad, G., (2020). Assessing impact of channel selection on decoding of motor and cognitive imagery from MEG data. Journal of Neural Engineering. 17 (5), 056037-056037
Roy, S., Chowdhury, A., McCreadie, K. and Prasad, G., (2020). Deep Learning based Inter-subject Continuous Decoding of Motor Imagery for Practical Brain-Computer Interfaces. Frontiers in Neuroscience. 14, 918-
Rathee, D., Chowdhury, A., Meena, YK., Dutta, A., McDonough, S. and Prasad, G., (2019). Brain–Machine Interface-Driven Post-Stroke Upper-Limb Functional Recovery Correlates With Beta-Band Mediated Cortical Networks. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 27 (5), 1020-1031
Chowdhury, A., Nishad, SS., Meena, YK., Dutta, A. and Prasad, G., (2019). Hand-Exoskeleton Assisted Progressive Neurorehabilitation Using Impedance Adaptation Based Challenge Level Adjustment Method. IEEE Transactions on Haptics. 12 (2), 128-140
Chowdhury, A., Raza, H., Meena, YK., Dutta, A. and Prasad, G., (2019). An EEG-EMG Correlation-based Brain-Computer Interface for Hand Orthosis Supported Neuro-Rehabilitation. Journal of Neuroscience Methods. 312, 1-11
Chowdhury, A., Meena, YK., Raza, H., Bhushan, B., Uttam, AK., Pandey, N., Hashmi, AA., Bajpai, A., Dutta, A. and Prasad, G., (2018). Active Physical Practice Followed by Mental Practice Using BCI-Driven Hand Exoskeleton: A Pilot Trial for Clinical Effectiveness and Usability. IEEE Journal of Biomedical and Health Informatics. 22 (6), 1786-1795
Chowdhury, A., Raza, H., Meena, YK., Dutta, A. and Prasad, G., (2018). Online Covariate Shift Detection based Adaptive Brain-Computer Interface to Trigger Hand Exoskeleton Feedback for Neuro-Rehabilitation. IEEE Transactions on Cognitive and Developmental Systems. 10 (4), 1070-1080
Conferences (11)
Sudhakar, A. and Chowdhury, A., (2023). 3D localization of objects of daily living activities for rehabilitation applications
Raza, H., Chowdhury, A. and Bhattacharyya, S., (2020). Deep Learning based Prediction of EEG Motor Imagery of Stroke Patients' for Neuro-Rehabilitation Application
Raza, H., Chowdhury, A., Bhattacharyya, S. and Samothrakis, S., (2020). Single-Trial EEG Classification with EEGNet and Neural Structured Learning for Improving BCI Performance
Chowdhury, A., Dutta, A. and Prasad, G., (2019). Can Corticomuscular Coupling be Useful in Designing Hybrid-Brain Robot Interfaces Towards Hand Functional Recovery?
Chowdhury, A., Raza, H., Dutta, A. and Prasad, G., (2017). EEG-EMG based Hybrid Brain Computer Interface for Triggering Hand Exoskeleton for Neuro-Rehabilitation
Chowdhury, A., Ansari, S. and Bhaumik, S., (2017). Earthworm like modular robot using active surface gripping mechanism for peristaltic locomotion
Chowdhury, AMM., Kashem, FB., Hossan, A. and Hasan, MM., (2017). Brain controlled assistive buzzer system for physically impaired people
Meena, YK., Chowdhury, A., Cecotti, H., Wong-Lin, K., Nishad, SS., Dutta, A. and Prasad, G., (2016). EMOHEX: An eye tracker based mobility and hand exoskeleton device for assisting disabled people
Hossan, A. and Chowdhury, AMM., (2016). Real time EEG based automatic brainwave regulation by music
Chowdhury, A., Raza, H., Dutta, A., Nishad, SS., Saxena, A. and Prasad, G., (2015). A Study on Cortico-muscular Coupling in Finger Motions for Exoskeleton Assisted Neuro-Rehabilitation
Chowdhury, A., Kumar, J., Majumder, A. and IEEE, (2014). A Solution to Speeding Related Problem in Road Vehicles Using Passive RFID Tags
Grants and funding
2024
Developing socially intelligent human-machine interactive system for advancing the state-of-the-art of assistive technologies for the disabled.
Colchester Catalyst Charity
2020
NeuRestore: Brain Computer Interface driven rehabilitation of upper limb weakness of stroke survivors
Innovate UK (formerly Technology Strategy Board)
Contact
Academic support hours:
Tue 12.00-13.00 Wed 11.00-12.00
