Vaibhav joined the department of Design Engineering & Mathematics, Faculty of Science & Technology, Middlesex University London in 2013 where he is currently the Director of Programmes for Product Design and Engineering programmes. Vaibhav has previously served as Programme Leader for BEng MEng Design Engineering suite of programmes and BEng/MEng Electronic Engineering (2016-21). His research interests include brain-computer interfaces, biomedical signal processing, computational intelligence, computational neuroscience, user-centric graphical user interfaces, and assistive robotics. Vaibhav is a Senior Fellow of The Higher Education Academy, a Life Member of the Indian Society for Technical Education (ISTE), and is also a Consultant for Doctorate in Professional Studies (DProf).
Vaibhav received a First Class (Dist.) degree in Instrumentation & Control engineering in 2000, a First Class (Dist.) Masters degree in Electrical engineering in 2002 and a PhD degree in Computing & Engineering in 2012. He was a recipient of the UK-India Education & Research Initiative (UKIERI) scholarship for his PhD research in the area of Brain-Computer Interface for assistive robotics carried out at the Intelligent Systems Research Center, University of Ulster, UK, and partly at IIT Kanpur, India. His PhD research was focused on quantum mechanics motivated EEG signal processing and an intelligent adaptive user-centric human-computer interface design for real-time control of a mobile robot for the BCI users. His post-doctoral research involved work on shadow-hand multi-fingered mobile robot control using the EMG/muscle signals, with contributions also in the 3D printing aspects of a robotic hand.
More information (and videos) about his research on Brain-Computer Interfaces for assistive robotics can be found on his book website http://booksite.elsevier.com/9780128015438/index.php. His current research and publications can be found on the tab Research Outputs and Interests.
English, Hindi, Gujarati, Bengali.
As Director of Programmes for Product Design and Engineering, I have the responsibility for the design, development, assessment, management, and quality enhancement processes for the below programmes.
I also contribute to teaching the following UG and PG modules within the Design Engineering department:
PDE2420 (Control Systems) (2013 - )
PDE2440 (Robotics & Mechatronics) (2013-2019/20)
PDE3400 (Design Engineering Major Project) (2013 - )
PDE3422 (Industrial Automation & Control) (2013-2019/20)
PDE3853 (Design Engineering Dissertation) (2018-2019)
PDE4400 (Team Project) (2017 - )
PDE4421 (Robotic Systems and Control) (2017 - 2020)/21 MSc Robotics
PDE4422 (Group Project) (2017 - 2020/21) MSc Robotics
PDE4603 (Thesis) MSc Robotics
Doctoral and Masters (Research) students are welcome in the areas of Brain-Computer Interface (BCI), physiological signal processing, neural network design, graphical user interfaces, assistive robotics and similar interdisciplinary research areas.
Below is a brief of my area of research.
My areas of research involves investigation into intelligent systems which facilitate development of a low-cost assistive robotic device for people with severe movement disability. The objective is to investigate a brain-computer interface (BCI) that allows a highly disabled person to control a smart wheelchair and robotic manipulator combination by thought alone. This involves developing advanced signal processing algorithms to extract information from BCI tasks-related electroencephalogram (EEG), commonly known as brain-waves, and required investigations into signal pre-processing, feature extraction and classification aspects using innovative statistical methods and computational intelligence approaches. Dealing with the unknown embedded noise within the raw EEG and the inherent lower bandwidth of BCI are still two of the major challenges in making BCI practical for day-to-day use. My research involves working on quantum mechanics (QM) motivated alternative neural information processing architecture using the Schrodinger wave equation (SWE) to filter and thereby enhance the information from the otherwise noisy EEG signals. These QM filtered EEG signal is more easily classified than the raw signal. An intelligent and adaptive user interface (UI), which plays a very important role as a front-end display for the BCI user has been developed. The framework, referred to as the intelligent Adaptive User Interface (iAUI) is consistent for a range of applications e.g., for controlling either a mobile robot or arobotic arm. The iAUI for mobile robot offers a real-time prioritized list of all the options for selection by the user.
I am interested into the practical implementation of EEG for assistive robotic applications such as wheelchair control while using use-centric graphical user interfaces.
Click this link to see the http://booksite.elsevier.com/9780128015438/video.php
1. V. Gandhi, "Brain-Computer Interfacing for Assistive Robotics: Electroencephalograms, Recurrent Quantum Neural Networks and User-centric Graphical User Interfaces", Elsevier, October 2014. (ISBN: 978-0-12-801543-8)
V Mehta, V Gandhi, and G Mapp (2021), "Traffic Prediction and minimising delays at junction", In: Third UK Mobile, Wearable and Ubiquitous Systems Research Symposium, 5th Jul – 6th July 2021, Department of Computer Science, University of Oxford, UK.
M. Cenit, V. Gandhi "Design and development of the sEMG-based exoskeleton strength enhancer for the legs", Journal of Mechatronics, Electrical Power, and Vehicular Technology, Vol 11 (2), 2020.
Onyeulo EB, V Gandhi, "What makes a social robot good at interacting with humans?", Journal: Information, Special Issue: Advances in Social Robots, 2020, 11, 43.
Miller, O.G., Gandhi, V., A Survey of Modern Exogenous Fault Detection and Diagnosis Methods for Swarm Robotics, Journal of King Saud University - Engineering Sciences (2019), doi: https://doi.org/10.1016/j.jksues.2019.12.005
V Mehta, V Gandhi, and G Mapp (2019) “Developing traffic prediction and congestion algorithms for a C-ITS network”, In: Second UK Mobile, Wearable and Ubiquitous Systems Research Symposium, 1st Jul – 2nd July 2019, Department of Computer Science, University of Oxford, UK.
V Mehta, V Gandhi, and G Mapp (2018) “Exploring real time traffic signalling using probabilistic approach in intelligent transport system”, In: 3rd CommNet2 PhD Autumn School, 17th – 19th Sept. 2018, University of Sheffield, UK.
V Mehta, V Gandhi, and G Mapp (2018) “Exploring real time traffic signalling using probabilistic approach in intelligent transport system”, In: Mobi-UK 2018, 12th – 13th Sept. 2018, University of Cambridge, Cambridge, UK.
M Krawczyk, Z Yang, V Gandhi, Mehmet Karamanoglu, Felipe MG França, Priscila MV Lima, Xiaochen Wang and Tao Geng, “Wrist Movement Detector for ROS Based Control of the Robotic Hand”, Advances in Robotics and Automation, January 2018. DOI: 10.4172/2168-9695.1000182.
V Gandhi, Z Yang, M Aiash, "Project-based Cooperative Learning to Enhance Competence while Teaching Engineering Modules", International Journal of Continuing Engineering Education and Life-Long Learning, Vol. 27, No. 3, 2017 ( DOI: 10.1504/IJCEELL.2017.10003462).
N Singh, C Huyck, V Gandhi, A Jones, "Neuron Based Control Mechanisms for a Robotic Arm and Hand," International Journal of Biomedical and Biological Engineering Vol:4, No:2, 2017.
Kalyani G.K., Yang Z., Gandhi V., Geng T. (2017) Using Robot Operating System (ROS) and Single Board Computer to Control Bioloid Robot Motion. In: Gao Y., Fallah S., Jin Y., Lekakou C. (eds) Towards Autonomous Robotic Systems. TAROS 2017. Lecture Notes in Computer Science, vol 10454. Springer. (DOI: 10.1007/978-3-319-64107-2_4)
G K Kalyani, Z. Yang, V. Gandhi, and T Geng, "ROS based Autonomous Control of a Humanoid Robot", in the 25th International Conference on Artificial Neural Networks (ICANN), Barcelona, 6 - 9 September 2016.
P. Parmar, A. Joshi, V. Gandhi, "Brain Computer Interface: A Review", 5th Nirma University International Conference on Engineering (NUiCONE), India, Nov. 2015.
V. Krasim, V. Gandhi, Z. Yang, and M. Karamanoglu, "EMG based elbow joint powered exoskeleton for biceps brachii strength augmentation”, IEEE International Joint Conference on Neural Network, Killarney, Republic of Ireland, 11th – 17th July 2015.
Z. Yang, V. Gandhi, M. Karamanoglu, B. Graham, "Characterizing Information Correlation in a Stochastic Izhikevich Neuron", IEEE International Joint Conference on Neural Network, Killarney, Republic of Ireland, 11th – 17th July 2015.
V. Gandhi, G. Prasad, D. Coyle, L. Behera, and T. M. McGinnity, "Evaluating Quantum Neural Network filtered motor imagery brain-computer interface using multiple classification techniques", Neurocomputing, Vol. 170, pp. 161–167, 25 December 2015. (Impact factor 2.00)
V. Gandhi, G. Prasad, D. Coyle, L. Behera, and T. M. McGinnity, "EEG based mobile robot control through an adaptive brain-robot interface", IEEE Transactions on Systems Man & Cybernetics: Systems, Vol. 44, No. 9, pp. 1278-1285, 2014 (Impact factor 2.18)
V. Gandhi, G. Prasad, D. Coyle, L. Behera, and T. M. McGinnity, "Quantum neural network based EEG filtering for a Brain-computer interface", IEEE Transactions On Neural Networks and Learning Systems, Vol. 25, No. 2, pp 278- 288, 2014. (DOI: 10.1109/TNNLS.2013.2274436) (Impact factor 4.37)
V. Gandhi and T. M. McGinnity, "Quantum neural network based surface EMG signal filtering for control of robotic hand", 2013 IEEE International Joint Conference on Neural Network, 4 - 9 Aug., 2013, USA.
V. Gandhi, G. Prasad, D. Coyle, L. Behera, and T. M. McGinnity, "Intelligent adaptive user interfaces for BCI based robotic control", 5th International BCI meeting 2013, 3 - 7 June, 2013, USA. (DOI: 10.3217/978-3-85125-260-6-130)
V. Gandhi, L. Behera, G. Prasad, D Coyle, and T. M. McGinnity, "EEG filtering with Quantum neural networks for a Brain-computer interface", in 'Young Researchers Futures Meeting on Neural Engineering' (event sponsored by Royal Academy of Engineering), 19-21 Sept. 2012, UK.
V. Gandhi, V Arora, L. Behera, G. Prasad, D Coyle, and M. McGinnity, "EEG denoising with Recurrent Quantum Neural Network for a Brain-Computer Interface," in IEEE International Joint Conference on Neural Network, 31 July - 5 Aug., 2011, USA. (DOI: 10.1109/IJCNN.2011.6033413) (ISBN:978-1-84919-469-3)
V. Gandhi, G. Prasad, D. Coyle, L. Behera, and T. M. McGinnity, "An intelligent Adaptive User Interface (iAUI) for enhancing the communication in a Brain-Computer Interface (BCI)", in 2011 International UKIERI workshop on Fusion of BCI and Assistive Robotics, 7-8 July, 2011, UK.
V. Gandhi, V Arora, L. Behera, G. Prasad, D Coyle, and M. McGinnity, "A Recurrent Quantum Neural Network model enhances the EEG signal for an improved brain-computer interface", in The Institution of Engineering and Technology (IET), 6 April, 2011, UK. (DOI: 10.1049/ic.2011.0028)
V. Gandhi, L. Behera, G. Prasad, D Coyle, and M. McGinnity, "EEG Signal Enhancement using a Recurrent Quantum Neural Network for a Brain-Computer Interface," in Technically Assisted Rehabilitation – TAR 2011, 3rd European Conference, Germany, 17-18 March, 2011, Germany.
V. Gandhi, G. Prasad, D. Coyle, L. Behera, and T. M. McGinnity, "A Novel Paradigm for Multiple Target Selection Using a two class Brain Computer Interface", in Irish Signals and Systems Conference, Ireland, 10-11 June, 2009, Ireland. (DOI: 10.1049/cp.2009.1690)
V. Gandhi, D. Coyle, G. Prasad, C. Bharti, L. Behera, and T. M. McGinnity, "Interfacing a dynamic Interface Paradigm for Multiple Target Selection Using a Two Class Brain Computer Interface", in Indo - US Workshop on System of Systems Engineering, 26-28 Oct. 2009, IIT Kanpur, India.
V. Gandhi and D.N.Priyadarshi, "Foundation Fieldbus - Introduction of Digital Control Network for Industrial Automation", in Networking: Technology and Applications, IETE, India, 2008.
V. Gandhi, "Analyzing the performance of backpropagation neural network in Image classification problem", in Smart Computing and Communication, IETE, India, 2007.
V. Gandhi, "Image classification based on textural features using unsupervised neural Network", in 1st International Indian Geographical Congress, India, 2006.
S.K.Shah and V. Gandhi, "Image classification based on textural features using Artificial Neural Network ", in Institution of Engineers (I), Vol 4, pp. 72-77, 2004. (DOI: 10.1.1.134.3537)
Marcin Krawczyk, Vaibhav Gandhi, Zhijun Yang, "ROS controlled anthropomorphic robot hand".
N Sharma, P Mahapatra, V Gandhi, "Comparative Analysis of Various Machine Learning Techniques for Classification of Speech Disfluencies"
Zhijun Yang, Haibin Liu, Xu Liu, Vaibhav Gandhi, Tao Geng and Felipe Franca "Neuromorphic Building Blocks for Locomotion Pattern Generation".
S Raju, C Okpaluba, J Ismail, T Mangozho, V Gandhi, A Demosthenous, R Bayford, "Prototype of a Wireless Multi-Sensor System for Adaptive Deep Brain Stimulation".
"Enhancing the performance of a practical Brain-Computer Interface (BCI) system" at Intelligent Systems Research Centre, University of Ulster, UK on 14th March 2012.
"Brain-Computer Interface: Challenges and Advancements" at Ruhr-Universität Bochum, Germany from 28-30th Aug. 2011.
"Programmable Logic Controller (PLC) Hardware" in a Training programme for Industrial officials on "PLC and SCADA" organized at Nirma University of Science & Technology, Gujarat, India on 23rd - 24th October 2004.
"Neural-Fuzzy system" in a one-day workshop on "Instrumentation" at the Indian Navy School - Valsura, Gujarat, India in May 2003.
Reviewer for IEEE Transactions on Computational Intelligence and AI in Games, Neurocomputing (Elsevier), Autosoft Journal, Journal of Information Science & Engineering, Biomedical Signal Processing and Control (Elsevier), Modelling and Simulation in Engineering (Hindawi Publishing Corporation), IEEE Symposium Series in Computational Intelligence, IEEE International Symposium on Robot and Human Interactive Communication (IEEE RO-MAN), IEEE RAS/EMBS International Conference on Biomedical Robotics and Biomechatronics, IEEE co-sponsored International Conference on Advances in Computing, Communications and Informatics (ICACCI) etc.
Consultant for Doctorate in Professional Studies (DProf).
Mehta, Vatsal Jitendra and Gandhi, Vaibhav and Mapp, Glenford E. (2021) Developing traffic predictions from source to destination using probabilistic modelling. In: Third UK Mobile, Wearable and Ubiquitous Systems Research Symposium, 05-06 Jul 2021, Online via Zoom.
Cenit, Mikecon and Gandhi, Vaibhav (2020) Design and development of the sEMG-based exoskeleton strength enhancer for the legs. Journal of Mechatronics, Electrical Power, and Vehicular Technology , 11 (2). pp. 64-74. ISSN 2087-3379
Onyeulo, Eva Blessing and Gandhi, Vaibhav (2020) What makes a social robot good at interacting with humans? Information , 11 (1). pp. 1-13.
Graham Miller, Olivier and Gandhi, Vaibhav (2020) A survey of modern exogenous fault detection and diagnosis methods for swarm robotics. Journal of King Saud University – Engineering Science , 33 (1). pp. 43-53. ISSN 1018-3639
Mehta, Vatsal and Gandhi, Vaibhav and Mapp, Glenford E. (2019) Developing traffic prediction and congestion algorithms for a C-ITS network. In: Second UK Mobile, Wearable and Ubiquitous Systems Research Symposium, 01 Jul 2019, Dept of Computer Science, University of Oxford, UK.
List of PhD student projects:
1) Nishant Singh - Neuron based control mechanisms for robot arm movement (2015-2020)
2) Vatsal Mehta - Developing Traffic Prediction and Congestion Algorithms for a Cooperative Intelligent Transport System (C-ITS) (2018 - )
Smart cities have been developing swiftly over the past few years and Intelligent Transport will be is a key part of this brave new world. A Cooperative Intelligent Transport System (C-ITS) is a fusion of transport and communication facilities, which allows vehicles to communicate with each other and with the transport infrastructure. This project focuses on how traffic prediction can be incorporated and how congestion can be reduced by using C-ITS. Probability and mathematical modelling based on traffic flow, average speed etc. will be used to predict traffic and minimise traffic congestion.
3) Nitin Sharma - Detection and Remediation of Dysfluencies in Speech (2019 - ) (AcSIR/CSIO, India)
Speech is an effective way to express ideas, feelings, and thoughts by humans. However, speech is not always without disruptions. Disruptions in speech can lead to serious problems like stuttering. All people are dysfluent to some degree in speaking, but about 5% of children and 1% of adults are more dysfluent in their speech, which is perceived as stuttering to listeners. This project focuses on automatically detecting and classifying different speech dysfluencies in the speech with improved accuracy and providing remediation to the dysfluent speech signals.
Call for Special issue (IJCNN-2020SS): Neural Mechanisms for Artificial and Natural Locomotions
Previous Special Issue call "Brain-Computer Interfaces: Current Trends and Novel Applications".