Brain-Computer Interfaces: Lab Experiments to Real-World Applications

Damien Coyle is a Professor of Neurotechnology at the School of Computing and Intelligent Systems, Ulster University. He is developing neurotechnology to enable movement-free communication for the physically impaired. These developments are underpinned by research in computational intelligence, bio-signal processing, computational neuroscience, neuroimaging and brain-computer interface (BCI) applications.Damien is a Fellow of the UK Higher Education Academy, and teaches at both undergraduate and postgraduate level in computer science and engineering and supervises a number of PhD students and researchers. Damien studied at Ulster University, graduating as a Bachelor of Engineering in Electronics and Computing with first class honours in 2002 and with a PhD in intelligent signal processing for BCI applications in 2006. He joined the School as Lecturer (2006-2012), becoming Senior Lecturer (2012-2013), Reader (2013-2014) and Professor (2015). Previously an Ulster University Distinguished Research Fellow, Damien has won international research awards including the 2008 IEEE Computational Intelligence Society (CIS) Outstanding Doctoral Dissertation Award and the 2011 International Neural Network Society (INNS) Young Investigator of the Year Award. His research was shortlisted for the 2015 Annual International BCI Research Award. He was a Royal Academy of Engineering/The Leverhulme Trust Senior Research Fellow in 2013 and is currently a Royal Academy of Engineering Enterprise Fellow 2016. Damien is a founding member of the International BCI Society, a Senior member of the IEEE and chairs the IEEE Computational Intelligence Society (CIS) (UKRI chapter) which was awarded best and outstanding chapter awards from the IEEE region 8 and the IEEE CIS in 2012 and 2013, respectively.

• ECoG studies1. Electrocorticographic (ECoG) Brain-Computer Interfaces and PlasticityKai Joshua MillerSignal processing 2. Neural and Cortical Analysis of Swallowing and Detection of Motor Imagery of Swallow for Dysphagia Rehabilitation-A ReviewCuntai Guan and Huijuan Yang Advanced non-invasive decoding of hand grasp and arm movements 3. Multisession, noninvasive closed-loop neuroprosthetic control of grasping by upper limb amputeesJose Contreras-Vidal4. From classic motor imagery to complex movement intention decoding: the non-invasive Graz-BCI approachGernot Müller-Putz5. 3d arm motion trajectory prediction from EEG Attila KorikPatients studies and clinical applications 6. Interfacing Brain with Computer to improve communication and rehabilitation after brain damageDonatella Mattia7. Brain Computer Interfaces in the completely locked in state and chronic strokeNiels Birbaumer8. Practical considerations in using brain-computer interfaces for real-world applications that assist individuals with disabilitiesDennis McFarland9. Brain-Computer Interfaces for neurorehabilitationJunichi UshibaNon-medical applications 10. A passive Brain-Computer Interface (p-BCI) application for the mental workload assessment on professional Air Traffic Controllers (ATCOs) during realistic ATC tasksFabio Babiloni11. Advances in User-Training for Mental-Imagery Based BCI Control: Psychological and Cognitive Factors and their Neural CorrelatesFabien Lotte12. Communicating with BCI using language related cognitive tasksFrancisco Sepulveda13. Brain-Computer Games Interfacing with Motion-onset Visual Evoked PotentialsDavid Marshall14. Brain-Computer Interfaces for Patients with Disorders of ConsciousnessDamian Cruse15. A Cognitive Brain-Computer Interface for Patients with Amyotrophic Lateral SclerosisMatthias R. Hohmann