Riccardo Poli is a full professor in School of Computer Science and Electronic Engineering of the University of Essex where he is the director of postgraduate research studies, the coordinator of the BCI-NE Lab and the UK team leader in our recent US-UK MURI grant (see projects section for more info). Prof Poli is a biomedical engineer (by first degree, PhD and subsequent research) and an expert in genetic and evolutionary computation, and more generally machine learning and computational intelligence. He has over 320 refereed publications (and two books), of which 60+ in bioengineering, BCI and related areas and the rest in computational intelligence. Prof Poli is an advisory board member of Evolutionary Computation and was an associate editor of the Journal of Genetic Programming and Evolvable Machines published by Springer, of the Applied Soft Computing Journal published by Elsevier and of Swarm Intelligence published by Springer, until early 2014 when he resigned from these roles to free up research time. Prof Poli has been chair of numerous international conferences, a tutorial/keynote speaker at 30+ international conferences and Summer schools, a programme committee member of approximately 80 international workshops and conferences and a reviewer for 15 international journals. As PI and Co-I he received funding for approximately €5M in his career. According to Google Scholar he has well over 20,000 citations and an H-index of 61. His work on collaborative BCIs has recently appeared in the New Scientist, the Financial Times magazine and many others.
Reinhold Scherer is professor of brain-computer interfaces and neural engineering in the School of Computer Science and Electronic Engineering at the University of Essex. He received the Diplom-Ingenieur (M.Sc) and the Dr.techn (Ph.D.) degree in computer science from the Technische Universität Graz (TU-Graz), Austria. During his doctoral studies, he was member of the Graz-BCI lab at TU-Graz and worked on non-invasive electroencephalogram-based (EEG) brain-computer interfaces (BCIs). He spend the years from 2008 to 2010 as postdoctoral researcher at the Department for Computer Science & Engineering, University of Washington, Seattle, USA, and was member of the Neural Systems and the Neurobotics Laboratories at the University of Washington. In 2010, he re-joined TU-Graz and the Graz-BCI lab as Assistant professor. He received his habilitation (venia docendi) and became Associate professor (tenure) in 2016. Since 2011, he was deputy director of the Institute of Neural Engineering at TU-Graz. He joined the University of Essex and the Brain-Computer Interfaces and Neural Engineering laboratory in 2019. His research interests include brain-computer interfacing, statistical and adaptive signal processing, mobile brain and body imaging, technology-mediated rehabilitation and assistive technologies.
Francisco Sepulveda is a full Professor of Neural Engineering and Intelligent Systems. He was the coordinator for 10 years and a co-Founder of the Essex BCI group. He has been involved in efforts that have secured c£2.5million in external funds from the UK’s Research Councils, HEFCE, and MTHR, including receiving an EPSRC First Grant for early career researchers. Since 2004, the Essex BCI-NE group has become the largest in the UK and has attracted more funding than all other UK-based BCI groups combined. Prior to joining the University in 2002, Dr. Sepulveda was an Assistant Professor of Biomedical Engineering at Aalborg University, Denmark, where he was involved in projects on the use of implanted cuff electrodes for the extraction of afferent signals related to joint angles and foot pressure, and in pattern recognition applied to implanted EMG for control of hand prostheses. He received his PhD Summa Cum Laude in Biomedical Engineering from the University of Campinas, Brazil (with a fellowship at the Bioengineering Unit, University of Strathclyde, UK) in 1996, an MSc (with Distinction) in Bioengineering from Clemson University (USA) in 1990, and a BSc in Nuclear Engineering from the University of California, Santa Barbara, in 1988, having received the ‘The Outstanding Student’ award at graduation. His PhD was on the development of an artificial neural controller for FES-generated gait in individuals with spinal cord injury, which gave him the best paper award at the World Congress on Medical Physics and Biomedical Engineering in 1994. In the UK, he has been very active in public dissemination of science, with many appearances in the media and public venue. Dr. Sepulveda has more than 160 peer reviewed publications (>120 in neural engineering), is a member of the Peer Review College of the EPSRC and of the editorial board of Frontiers in Human Neuroscience.
Dr Luca Citi's primary research focus is on the application of machine learning and statistics to neural signal decoding. He has a laurea degree (corresponding to a MSc) in Electronic Engineering with major in Biomedical Engineering from Università di Firenze (Italy). He joined the Essex BCI lab for the first time in 2004 as a visiting student. Under the supervision of Prof Riccardo Poli, he worked on his master's thesis about an ERP-based brain-computer interface mouse. In 2009, he obtained his PhD under the supervision of Prof Silvestro Micera and Dr Oliver Tonet at IMT Lucca and Scuola Superiore Sant'Anna (Pisa, Italy). The topic of the PhD was the development of an algorithm to translate the neural activity recorded from the nerve residuum of an amputee into control commands for a robotic hand prosthesis. Afterwards, he worked for three years as post-doctoral researcher in the “Neuroscience Statistics” laboratory led by Prof Emery Brown at MGH / Harvard Medical School and MIT (Cambridge, USA), where he accumulated a wealth of experience on the point-process analysis of biological signals, including neural spike trains. He worked with Dr Riccardo Barbieri on the study of heart-rate variability and computational physiology. They entered the “PhysioNet 2012 challenge”, a machine learning competition about the prediction of in-hospital mortality, and their algorithm came first and second in the two events organized. Since 2012, Dr Citi is a lecturer in computational intelligence at the university of Essex and a member of the Essex BCI lab. In 2014, Dr Citi was selected to participate to the 2nd Heidelberg laureate forum where young researchers met Abel, Fields and Turing laureates for a week of scientific exchange.
John Q. Gan received the BSc degree in electronic engineering from Northwestern Polytechnic University, China, in 1982 and the MEng degree in automatic control and the PhD degree in biomedical electronics from Southeast University, China, in 1985 and 1991, respectively. He is a professor in the School of Computer Science and Electronic Engineering of the University of Essex. His research interests include neurofuzzy computation, machine learning, machine intelligence, brain-computer interface, robotics and intelligent systems, pattern recognition, signal processing, data fusion, computer vision and multimodal human-machine interaction. He has coauthored a book and published more than 200 research papers. He is an associate editor for the IEEE Transactions on Systems, Man, and Cybernetics—Part B and serves on the editorial board of other journals. He is a senior member of the IEEE.
My research interests include attention, multisensory perception, memory, brain-computer interfaces and assisted living. I obtained my first degree in Cognitive Psychology from University of Padova (Italy), where I started doing neuropsychology research based on single-case studies (aphasia and number processing). I then continued my studies at the University of Birmingham (UK), first as an Erasmus student, then with a master degree in Cognitive Science and a PhD in Cognitive Psychology. During that time (1996-2001) I investigated feature binding in visual and cross-modal perception in normal and brain-damaged population. Since 2002 I work at the University of Essex as a research assistant. Here I am currently investigating mechanisms of retrieval and forgetting in the Psychology Department and I am also a member of the BCI group (since 2004). With the BCI group I have contributed at the development of P300-based BCIs (BCI speller and mouse) and am now engaged at exploring collaborative BCI based on EEG and other physiological measures. I am also involved in a study investigating the possibility of reducing Parkinson’s disease tremor through brainwave entrainment.
Ian Daly received the M.Eng. degree in Computer science and the Ph.D. degree in Cybernetics from the University of Reading, Reading, U.K. Between May 2011 – 2013 he was a post-doctoral researcher in the Laboratory of Brain-Computer Interfaces, Graz University of Technology, Graz, Austria, where he researched Brain-computer interfaces for individuals with stroke and cerebral palsy. He was then a post-doctoral researcher in the University of Reading until October 2016 when he joined the University of Essex (and the BCI-NE lab) as a Lecturer. His research interests focus on BCIs, nonlinear dynamics, machine learning, signal processing, bio-signal analysis, meta-heuristic search techniques, and connectivity analysis in the EEG and fMRI. He is also interested in the neurophysiological correlates of motor control, emotion, and stimuli perception and how they differ between healthy individuals and individuals with neurological and physiological impairments.
Ana obtained her BEng + MEng in Telecommunication Engineering from the Universidad de Valladolid (Spain) and her MSc in Biomedical Engineering from the University of Surrey (awarded Distinction). She joined the BCI-NE lab in 2012 to do her PhD in the topic of collaborative BCIs under the supervision of Prof. Riccardo Poli. As part of her PhD she visited NASA JPL as a fellow researcher. She worked as a senior research officer in the H2020 DeTOP European project before joining the Institute for Analytics and Data Science as an Artificial Intelligence Industry Fellow. Her research interests are in the area of biomedical signal processing, and uses EEG to study memory and emotion in response to complex visual and audio-visual stimuli.
Sebastian Halder received his MSc degree in Bioinformatics from the University of Tübingen, Germany in 2006 and his PhD in Computer Science from the same institution in 2011. From 2002 to 2006 as a student and from 2006 to 2012 he worked in the group of Prof. Niels Birbaumer in particular on brain-computer interfaces (BCIs) for communication with persons severe motor impairments. During this time he worked on the online processing of electroencephalography data, the design of new communication paradigms and the analysis of electrocorticography and functional magnetic resonance data with a focus on clinical applications. Since then he has held positions at the University of Würzburg in Germany, the National Rehabilitation Center for Persons with Disabilities in Japan and the University of Oslo in Norway. His research interests include auditory BCIs for communication, the neural mechanisms of learning with BCIs, the neural signature of pain and disorders of consciousness. He has authored or co-authored over 40 publications in peer reviewed journals that have been cited over 4500 times (according to Google Scholar). He joined the BCI-NE lab in January 2019.
Serafeim Perdikis was born in Thessaloniki, Greece in 1984. He received the M.E. degree in Electrical and Computer Engineering from the Aristotle University of Thessaloniki (AUTH) in 2007 and the Ph.D. degree in brain-computer interaction (BCI) from the Swiss Federal Institute of Technology in Lausanne (EPFL) in 2014, where he subsequently served as a post-doctoral researcher until May 2015. In June 2015 he joined the Wyss Center for Bio- and Neuro-engineering at Campus Biotech, Geneva, Switzerland working on BCI-based stroke rehabilitation. As of February 2019 he is serving as a Lecturer of the Neural Engineering and BCI Laboratory at the University of Essex, School of Computer Science and Electronic Engineering, UK. His main areas of research interest include theoretical and applied machine learning, bio-signal processing, mutual learning and co-adaptation in BCI, as well as translational and rehabilitation applications of BCI.
Delaram completed her PhD in Biomedical Signal Processing at the University of Surrey, UK, in 2011. Her research was focused on processing of brain signals using electroencephalography. She has been involved in development of algorithms for motion analysis using body-worn sensors at Imperial College London and unobtrusive heart rate monitoring from peripheral sites at University of Manchester. She was also a member of computational health informatics lab at University of Oxford, and worked on wearable sensors to contribute to the design of a wearable multivariate patient monitoring system for use at scale in clinical practice. Her research interests include time-series analysis, adaptive signal processing and machine learning for bio-signal processing.
Junhua Li received his PhD from the Department of Computer Science and Engineering at the Shanghai Jiao Tong University, China. He currently is a lecturer in the School of Computer Science and Electronic Engineering at the University of Essex, UK, and was a senior research fellow at the National University of Singapore, Singapore. His research interests include brain-computer interfaces, neurophysiological signal processing, machine learning, computational neuroscience, and neuroimaging data analytics. Particularly, he is recently focusing on the understanding of mental diseases such as Schizophrenia and Alzheimer’s disease from the perspective of neuroimaging and the applications of passive brain-computer interfaces to mental states. He is a Senior Member of the IEEE. As of May 2019, he has published 48 peer-reviewed papers.
Dr. Anirban Chowdhury is a Lecturer in Computer Science and Artificial Intelligence, School of Computer Science and Electronic Engineering (CSEE), University if Essex (UoE). 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 current research interests are in the areas of robotic rehabilitation, brain-computer interfaces, assistive technologies, human-robot co-operation, and autonomous mobile robotics. He has published several journal papers in the top journals of the field, including transactions and journals of the IEEE and Elsevier.
Haider Raza received the bachelor's degree in Computer Science & Engineering from the Integral University, India, in 2008, the master's degree in Computer Engineering from the Manav Rachna International University, India, in 2011, and the PhD degree in computer science from Ulster University, Derry~Londonderry, U.K., in 2016, PhD title "Adaptive learning for modelling non-stationarity in EEG-based brain-computer interfacing". He worked (Dec 2015 to June 2016) as Post-Doctoral Research Assistant in Brain-Computer Interface (BCI) for both Magnetoencephalography (MEG) and Electroencephalography (EEG) systems at University of Ulster, Northern Ireland, UK. Later, he worked (July 2016 to Nov 2017) as a Research Officer (Data Science) in the Farr Institute of Health Informatics Research, Swansea University Medical School, U.K. Since, Nov-2017, he is currently a Research Fellow at Institute for Analytics and Data Science, Essex University UK. His research interests include machine learning, non-stationary learning, BCI for neuro-rehabilitation, domain adaptation, and deep learning.
Dr Dimitri Ognibene has joined University of Essex as Lecturer in Computer Science and Artificial Intelligence in October 2017. In February 2015 he received from University Pompeu Fabra (Barcelona, Spain) and FP7 Marie Curie Actions COFUND Grants Programme a UPFellows Research Grant worth €150k awarded to one winner over more than one hundred participants on the topic of Embodied Bounded Rational Agents, focusing on the development of algorithms for intelligent social agents with bounded computational and sensory resources. Before he has been developing algorithms for active vision in industrial robotic tasks as a Research Associate (RA) at Centre for Robotics Research, Kings College London; devising Bayesian methods and robotic models for attention in social and dynamic environments as a RA at the Personal Robotics Laboratory in Imperial College London; studying interaction between active vision and autonomous learning in neuro-robotic models as a RA at Institute of Cognitive Science and Technologies of the Italian Research Council (ISTC CNR). He also collaborated with Wellcome Trust Centre for Neuroimaging (UCL) to address the exploration issue in the currently dominant neurocomputational modelling paradigm. Dr Ognibene has also been Visiting Researcher at Bounded Resource Reasoning Laboratory in UMass and at University of Reykjavik (Iceland) exploring the symmetries between active sensor control and active computation or metareasoning. Dr Ognibene presented his work in several international conferences on artificial intelligence (IJCAI), adaptation (SAB), and development (ICDL) and published on international peer-reviewed journals. Dr Ognibene was invited to speak at the International Symposium for Attention in Cognitive Systems (2013 and 2014) as well as in other various neuroscience, robotics and machine-learning international venues. In 2017, he organised a workshop on Active Vision in Human Robot Collaboration at ICIAP2017. Dr Ognibene is Associate Editor of Paladyn, Journal of Behavioral Robotics, and has been part of the Program Committee of several conferences and symposia.
I am a Lecturer at the School of the School of Computer Science and Electronic Engineering (CSEE) at the University of Essex, I held a postdoctoral fellow at the National Library of Medicine - National Institutes of Health (NLM/NIH), USA. I received my doctorate degree in Computer Science from the University of Geneva, Switzerland, in 2015. I received a Master's in Science in Telemedicine and Bioengineering from the Technical University of Madrid in 2009 and a Diploma in Mathematics at the Complutense University in Madrid in 2008.
Vito De Feo is a lecturer of Intelligent Systems for Brain and Mental Health in the School of Computer Science and Electronic Engineering at the University of Essex. His research interests include embedded neuromorphic control systems for bidirectional closed-loop Brain Machine Interfaces (BMIs), application of machine learning and statistics to neural signal encoding/decoding, state-dependent neural encoders and decoders, technology-mediated rehabilitation and assistive technologies, and novel measure of information transfer in the brain. Vito De Feo holds a MSc in Electronic Engineering from Politecnico di Torino (Italy). He obtained a PhD in Telecommunication from Politecnico di Torino (Italy). During his PhD, he spent 14 months at the University of Stanford (US). He also studied Neuropsychology at the University of Turin (Italy). In 2004 he was a post-doc at Politecnico di Torino (Italy) on signal processing. In 2009 he was a post-doc at Trinity College Dublin (Ireland) on optical communication. From 2013, he worked for two years as post-doc at the Clinical University of Hamburg-Eppendorf specializing on signal processing and causal analysis of biological signals (Local Field Potential) and point processes (neural spikes). He applied his research to modelling of the information transfer in the brain of healthy and pathological subjects (e.g. schizophrenics). From 2015 he worked for four years as a post-doc at the Italian Institute of Technology specializing on neural population coding, neuroelectronic and information theory. His research was focused on embedded neuromorphic control systems for bidirectional closed-loop Brain Machine Interfaces (SI-CODE FP7 FET), on state-dependent neural encoders and decoders, on novel measure of information transfer in the brain, and on a BCI for non-responsive patients. In November 2019, he joined the School of Computer Science and Electronic Engineering at the University of Essex as a Lecturer. He has recently successfully coordinated and led the Covid-19 efforts of the university, having established research links (both clinical and data/IT related) in the East Suffolk and North Essex NHS Foundation Trust (ESNEFT).
Tasos is a clinician and computer scientist turned biomedical engineer; his research interests are a direct result of his combined clinical and academic experience. He obtained his DPhil in 2017 at the University of Oxford in the field of biomedical engineering and biomedical image analysis, under the 'CDT in Healthcare Innovation' programme; his thesis dealt with the validation and uncertainty of fuzzy and probabilistic segmentations in medical imaging. He also holds a Neuroscience BSc, a Bachelors in Medicine and Surgery (MBChB), and a Masters in Advanced Computer Science (Machine Learning and Data Mining) from the University of Bristol. He has worked as a clinician in the NHS in a variety of disciplines. During his DPhil he co-founded Sentimoto Ltd, a company dealing with novel biomedical signal analysis and health monitoring from mobile and wearable devices aimed at older adults. He is currently working on the Nevermind Project at the University of Essex, looking at the role of biological signals in the management and prediction of depression secondary to a range of primary clinical conditions.
Jacobo Fernandez Vargas received the B.Sc. and M.S degrees in Computer Science from the Universidad Autonoma de Madrid (Spain). Then, obtained his PhD in Engineering from Chiba University (Japan) in 2018 with a thesis based on the prediction of the position of position of the hand using multimodal EEG+EMG recordings. Since then, he has done a postdoc and collaborated with Tsukuba University, Tokyo University and Kyoto University (Japan), focused on the detection of Sense of Agency from fMRI and EcoG recordings. From 2018 he works in the University of Essex as senior research officer on studies based on detection of the decision making using EEG.
Christoph obtained his Diplom-Ingenieur (B.Eng + M.Eng) in Mechanical Engineering with a focus on Mechantronic and Biomedical Engineering from the Karlsruhe Institute of Technology (Germany). In Spring 2019, he obtained his PhD in Biomedical Engineering at Old Dominion University (USA) as well as published his dissertation based on estimating cognitive workload in an interactive virtual reality environment using electrophysiological and kinematic activity. In June 2019 he joined the BCI-NE Lab and is currently working as a senior research officer in the US-UK MURI project.
Saideh received her PhD in biomedical signal processing at University of Surrey in 2012. Her main research interest is exploring the application of signal processing methods on EEG and fMRI data of human brain. In addition to EEG and fMRI, Saideh has also proposed some techniques for MRS and fNIRS analysis. She has developed effective algorithms for artifact removal, feature extraction and data fusion in the field of biomedical data processing. Blind source separation (BSS), statistical analysis, dictionary learning, and deep learning are her research interests too. The outcomes of her researches have been published in highly ranked peer reviewed journals such as IEEE transactions on biomedical engineering and Neurocomputing. She joined the BCI-NE lab in July 2019 to work as a senior research officer for POTION project.
Rodrigo Ramele is a Computer Engineering from the Universidad Nacional de La Matanza (Argentina). He holds a Graduate Specialization in Cryptography from the Instituto Enseñanza Superior M.Savio (Argentina) and Graduate Research Specialization in Robotics and Bioengineering from Tohoku University (Japan). He completed his Ph.D in Brain Computer Interfaces at the Instituto Tecnológico de Buenos Aires (Argentina) in 2018, working on the analysis of EEG using Computer Vision techniques. His interests encompass Assistive Robotics and Brain Computer Interfaces. He has experience working professionally as a software engineer for several companies and as Assistant Professor in Artificial Intelligence, Computer Vision, Robotics and Signal Processing.
Eirini Christinaki holds a B.Sc in Applied Informatics & Multimedia (2013) and M.Sc in Informatics & Multimedia (2016) from Technological Educational Institute of Crete (Greece). She is currently a PhD student in Computer Science at University of Essex and her research focuses on Machine Learning and Biomedical Signal Processing. In particular, she works on transfer learning techniques for online learning and prediction from sparse and multimodal biomedical data. Her PhD is funded through the NEVERMIND Horizon 2020 project.
Milan Rybar received his master degree in Theoretical computer science with a focus on artificial intelligence in 2015 and his bachelor degree in General computer science in 2012 from Charles University in Prague. He also spent one year at the University of Paderborn in Germany while working on his master thesis via Erasmus exchange program. After his graduation, he joined startup SpaceKnow in its early stage in October 2015 as machine learning researcher and software engineer where he focused on deep learning methods for semantic segmentation in satellite imagery. He left SpaceKnow at the end of 2017 to focus more on scientific research. He started his PhD at the University of Essex in April 2018 under the supervision of Dr. Ian Daly and Prof. Riccardo Poli. He works on a brain-computer interface based on identifying activity in the brain related to semantic concepts.
Lena obtained her BSc and MSc in Theoretical and Experimental Psychology at Ghent University in Belgium. During her master years she completed a 6-month research internship at the University of Oxford with the Attention and Cognitive Control lab. She is fascinated by the human brain and how (cognitive) behaviour emerges from specific neural activity and processing. Joining the University of Essex in January 2019 as a cross departmental PhD student (CSEE and Psycholgy), she investigates the neural basis and dynamics of motor learning and applies this knowledge to create more effective BCI protocols for movement rehabilitation, under the supervision of Dr. Ian Daly and Dr. Gethin Hughes.
Ekin is a Ph.D. student in Computer Science and Electrical Engineering at the University of Essex. She majored in Electrical Engineering at the Koc University and holds an M.Sc degree from Sabanci University, Istanbul. She works as a research assistant in Nevermind project under supervision of Dr. Luca Citi and Dr. Alba Garcia. Her research interests include medical image processing, machine learning, and computer vision. She is recently focusing on the detection of neurodegeneretive diseases such as Parkinson’s and Alzheimer’s diseases using machine learning.
Yiyuan obtained her B. Eng. in Information Engineering in 2019 from Southern University of Science and Technology in China. During her undergraduate study, she was working on human speech perception and acoustic signal processing. In October 2019, she started her PhD in Computer Science at the University of Essex, under the supervision of Dr Sebastian Halder and Dr Elia Valentini. Now she is investigating the novel approach to assess pain in unresponsive patients based on electrophysiology
Federica obtained her Bachelor degree in Biomedical Laboratory Techniques at the University of Verona in 2015 and her Master degree in Neuroscience at the University of Trieste in 2018 discussing a thesis about multi-sensory integration and decision making in rodents. She then joined the BCI laboratory at the Institute of Neural Engineering (TU Graz) where she investigated the neural correlates of mental workload and fatigue in humans using fNIRS under the supervision of Dr. Wriessnegger. In 2019 she worked as research fellow at the University of Verona on a project about EEG correlates of craving in smokers during Virtual Reality immersions. She started her cross departmental PhD (Computer Science, Psychology and Mathematics) in October 2019 under the supervision of Prof. Scherer, Dr. Daly, Dr. Gillmeister and Dr. Vernitski; her research focuses on developing an adaptive learning environment for mathematical education which exploits brain signals of the user to improve the learning outcome.
Zilu Wang is a doctoral student in the group of Brain-Computer Interface at the School of Computer Science and Electronic Engineering, University of Essex. He obtained his double-bachelor degree in mechanical engineering from the Beihua University, China, and Teesside University (First-class honours degree). During his undergraduate studies, he worked part-time at Inner Mongolia Bostar Co., Ltd in China for four years, mainly responsible for mechanical design and drawing review. His research interests are brain-computer interface and machine learning. In particular, he is focusing on the investigation of the neural mechanisms associated with lower limbs’ movements and the classification of movement categories based on EEG signal.
Undergraduate and MSc Students
Damian obtained his BEng in Computer Science at the West Pomeranian University of Technology in Szczecin (Poland). Since late 2014, he has worked as a Software Developer at Oxford Computer Consultants, delivering finance solutions within social care domain. He is now an MSc student in Computer Science and Electronic Engineering at the University of Essex, working on decomposition methods for neural signals, under the supervision of Dr Ana Matran-Fernandez and Prof. Luca Citi. Apart from biomedical signal processing, he is also interested in areas comprising machine learning and causality.
Kevin obtained his B. Eng. in Biomedical Engineering in 2018 from Universidad Autonoma de Occidente in Cali, Colombia. On completion of his studies, he worked on heart sounds for digital stethoscopes at a leading hospital in Cardiovascular disease in Bogota. Following this, he moved to London to continue his career in Machine Learning and AI with an emphasis on medical technology. In 2019 he started his own start-up; AI Diagnostics which focuses on biomedical signal processing and Machine Learning applications for physiological signals. He commenced his Master's in AI in 2020 and his thesis will focus on conscious detection from EEG signals.