Vaibhav joined the department of Design Engineering & Mathematics, School of Science & Technology, Middlesex University London in 2013, where he is currently a Senior Lecturer in Robotics, Embedded Systems and Real-time Systems. His research interests include brain-computer interfaces, biomedical signal processing, computational intelligence, computational neuroscience, use-centric graphical user interfaces, and assistive robotics. Vaibhav is a Fellow of The Higher Education Academy, 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 Ph.D. degree in Computing & Engineering in 2012. He was a recipient of the UK-India Education & Research Initiative (UKIERI) scholarship for his Ph.D. 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 Ph.D. research was focused on quantum mechanics motivated EEG signal processing, and an intelligent adaptive use-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 in his book website http://booksite.elsevier.com/9780128015438/index.php.
English, Hindi, Gujarati, Bengali.
I have taken over the Programme Leadership for BEng MEng Design Engineering suite of programmes (2016-17 onwards).
Our suite of Design Engineering courses share a common first year of study which enable students to experience all aspects of engineering. Depending on the student's interests, they can then continue with the broad Design Engineering degree or transfer to one of these specialist honours programmes:
I also contribute in teaching the following UG and PG modules within Design Engineering courses (2018-19):
PDE2420 (Control Systems)
PDE2440 (Robotics & Mechatronics)
PDE3400 (Design Engineering Major Project)
PDE3422 (Industrial Automation & Control)
PDE3853 (Design Engineering Dissertation)
PDE4400 (Team Project)
PDE4421 (Robotic Systems and Control)
PDE4422 (Group Project)
Doctoral and Masters (Research) students are welcome in the areas of brain-computer interface, physiological signal processing, neural network design, graphical user interfaces, assistive robotics and similar interdisciplinary research areas. Please see https://www.mdx.ac.uk/Assets/Novel%20Control%20Techniques.pdf for my project listed under https://www.mdx.ac.uk/research/applications/fees/bursaries/comp-sci.aspx and https://www.mdx.ac.uk/research/applications/fees/bursaries/elec-eng.aspx
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 QM based filtered EEG to control wheelchair / robotic devices 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)
Marcin Krawczyk, Zhijun Yang, Vaibhav 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", Int. J. 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)
"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).
Kalyani, Ganesh Kumar and Yang, Zhijun and Gandhi, Vaibhav and Geng, Tao (2017) Using robot operating system (ROS) and single board computer to control bioloid robot motion. In: 18th Towards Autonomous Robotic Systems (TAROS) Conference, 19-21 July 2017, Guildford, Surrey, UK.
Gandhi, Vaibhav and Yang, Zhijun and Aiash, Mahdi (2017) Project-based cooperative learning to enhance competence while teaching engineering modules. International Journal of Continuing Engineering Education and Life-Long Learning, 27 (3). pp. 198-208. ISSN 1560-4624
Singh, Nishant and Huyck, Christian R. and Gandhi, Vaibhav and Jones, Alexander (2017) Neuron-based control mechanisms for a robotic arm and hand. International Journal of Computer, Electrical, Automation, Control and Information Engineering, 11 (2). pp. 221-229. ISSN 2010-376X
Kalyani, Ganesh Kumar and Gandhi, Vaibhav and Yang, Zhijun and Geng, Tao (2016) ROS based autonomous control of a humanoid robot. In: 25th International Conference on Artificial Neural Networks (ICANN), 06-09 Sept 2016, Barcelona, Spain.
Gandhi, Vaibhav and Prasad, Girijesh and Coyle, Damien and Behera, Laxmidhar and McGinnity, Thomas Martin (2015) Evaluating quantum neural network filtered motor imagery brain-computer interface using multiple classification techniques. Neurocomputing, 170 . pp. 161-167. ISSN 0925-2312
List of PhD student projects:
1) Development of Novel Control Techniques in Assistive Technologies (2015-18)
2) Real-time Traffic Signalling using Probabilistic Approach in Intelligent Transport System (2018-)
Call for Special Issue "Brain-Computer Interfaces: Current Trends and Novel Applications".