In this paper, we summarise the state of development of the Strathclyde Brain Computer Interface (S-BCI) and what has been so far achieved. We also briefly discuss our next steps for translation to spinal cord injured patients and the challenges we envisage in this process and how we plan to address some of them. Projections of the S-BCI project for the coming few years are also presented

Allan, David B.

Conway, Bernard A

Lakany, Heba

English

Name not available

The Strathclyde Brain Computer Interface (S-BCI) : the road to clinical translation

University of Strathclyde Institutional Repository

Strathprints Institutional RepositoryLakany, Heba and Conway, Bernard A and Allan, David B. (2011) The Strathclyde Brain ComputerInterface (S-BCI) : the road to clinical translation. International Journal of Bioelectromagnetism, 13(2). pp. 84-85. ISSN 1456-7865Strathprints is designed to allow users to access the research output of the University of Strathclyde.Copyright c© and Moral Rights for the papers on this site are retained by the individual authorsand/or other copyright owners. You may not engage in further distribution of the material for anyprofitmaking activities or any commercial gain. You may freely distribute both the url (http://strathprints.strath.ac.uk/) and the content of this paper for research or study, educational, ornot-for-profit purposes without prior permission or charge.Any correspondence concerning this service should be sent to Strathprints administrator:mailto:strathprints@strath.ac.ukhttp://strathprints.strath.ac.uk/84    International Journal of Bioelectromagnetism  www.ijbem.org Vol. 13, No. 2, pp. 84 - 85, 2011    The Strathclyde Brain Computer Interface (S-BCI): the road to clinical translation 	Lakany, H.a,c , Conway, B.A.a,c and  Allan, D.B.b,c  a University of Strathclyde, Bioengineering Unit, Glasgow, Scotland b Queens Elizabeth National Spinal Injuries Unit , Southern General Hospital , Glasgow, Scotland c Scottish Centre for Innovation in Spinal Cord Injury, Scotland Correspondence:  Dr H Lakany , University of Strathclyde, Bioengineering, Wolfson Centre, Rottenrow, Glasgow, G4 0NW, Scotland  E-mail: heba.lakany@strath.ac.uk, phone +44 141 548 3487, fax +44 141 5526098   Abstract. In this abstract we summarise the state of development of the Strathclyde Brain Computer Interface (S-BCI) and what has been so far achieved. We also briefly discuss our next steps for translation to spinal cord injured patients and the challenges we envisage in this process and how we plan to address some of them.  Projections of the S-BCI project for the coming few years are also presented.  Keywords: Translational issues, brain computer interface, spinal-cord injured, assistive and augmentative devices 1. Introduction  The interplay between neuroscience, technology and clinical drivers has created the realistic potential for human machine interactions to be formed around neural interfaces. These interfaces can be sensitive and reliable indicators of user intention and when  matched with appropriate assistive technology can offer ways to provide functional capacity to the intended user. In this project we are developing a non-invasive brain computer interface for use by severely paralysed spinal cord injured patients. The Strathclyde BCI system and translational process we aim to follow are outlined here. 1.1 Aims and Objectives  Our goal has been to develop multidimensional BCI control derived from surface EEG potentials and apply it through a virtual environment (VE).This scenario offers, on one hand opportunities for safe motor rehabilitation through training with realistic visual feedback and opportunities for the development of assistive and augmentative devices for communication and control of external devices via the BCI. 1.2. Approach The approach we have adopted 1-12 comprises  1. the use of a high density electrode montage to record surface EEG,     2. the design of experiments to explore three movement paradigms based on rapid goal directed wrist movements  (actual, imaginary and observed movements), 3. the development of robust signal processing methods to identify and classify signals,  4. the development of a Virtual Reality (VR) simulator of an electric wheelchair and VE to provide a  training test-bed and a source of environmentally relevant visual feedback for BCI use (closed system) and 5. real-time analysis providing adaptation to learning through immersion within a VE 1.3. State of development of the S-BCI The S-BCI  has so far been  tested on healthy volunteers, the system provides accurate decoding of movement intent related to direction of motion and subjects are able  to steer a VR simulated electric wheelchair by thought1.  2. Clinical Translation We are currently poised to move the S-BCI into SCI user tests with the collaboration of the Queen Elizabeth national Spinal Injuries Unit (QENSIU) in Glasgow. The major objective is to provide patients who are paralysed  new means to access their environment with a device that can be controlled by thought.  Initially, we shall be focusing on wheelchair navigation with a 85  VE. We believe that we currently are in an ideal position to turn the performance that we achieved in the laboratory1,10-12  into functional gains for paralyzed patients. 2.1. Target population Our target population will be recruited from the QENSIU. This is because of the experience of the investigators with this population and the long and established collaboration between the Bioengineering Unit of University of Strathclyde and the clinical centre. The clinical centre is an ideal base as many patients remain for significant periods as in-patients (6 months or more post injury) and are first introduced to and learn to use assistive technologies during their stay in hospital. 2.2. Challenges and Obstacles We envisage three main technical challenges (1) Understanding the temporal and spatial resolution of coding mechanisms of intention, planning and execution of action in the brain of SCI patients in contrast to healthy volunteers (2) Information extraction from recorded data and (3) Feedback and adaptation of both the software implementation and the brain itself. The first challenge remains un-explored territory but is central to the translation of BCI technology to SCI patients.. 2.3. Our Approach to Clinical Translation We expect to make considerable changes in our system during the evolution from the laboratory experiments on healthy volunteers to clinical studies on SCI patients. Our inclusion/exclusion recruitment criteria will initially only allow patients with some level of residual movement and good cognition to be included. This is to potentially ensure subjects will understand instructions and will be able to train to use the S-BCI. We are encouraged by our preliminary experiments and results6,7 on extracting spatio-temporal features from spinal cord injured patients during imagined or attempted movements. Our experimental design will be simplified to binary cued tasks with visual feedback. The task initially will be to turn the wheelchair in the virtual environment in only one direction and to stop it. Increasing the number of degrees of freedom with the BCI control will follow. 3.Where we should be in the next few years  In the next few years, we will be focusing on our pilot clinical trials identifying a clear path for translation of S-BCI for use in clinical scenarios to achieve two inter dependent objectives (1) development of an assistive/augmentative system for people with severe motor disability (2) further our understanding of neural mechanisms responsible for generation and control of movement in healthy and motor  impaired subjects.  As the world-wide burden of brain-related illness exceeds  $2 trillion/year, in the next decade there will be greater unmet need to develop assistive and augmentative devices for the motor disabled and ageing population. The role of BCI technologies for use as brain training tools and systems to encourage plasticity and brain re-organisation will attain greater significance.  In addition to clinical applications, we foresee a role for non-invasive BCI in pervasive and embedded control systems.   Acknowledgements The authors acknowledge the support of  MRC, GRPE and EPSRC for the S-BCI project  References 1. Grychtol B, Lakany H, Valsan G,  Conway BA. Human Behavior Integration Improves Classification Rates In Real-Time BCI.  IEEE Trans Neural   Syst Rehabil Eng. 2010 Aug;18(4):362-8 2. Lakany, H.,  Worrajiran, P., Valsan, G. and Conway, B.A., EEG Classification Based On Movement Direction And Displacement.  Society for  Neuroscience  Annual Meeting 2005. Presentation number 520.15 3. Lakany, H., Steering A Wheelchair By Thought.  In Proceedings of IET Workshop on Intelligent Environments, 2005. pp: 199 – 202. 4. Lakany H. and Conway BA.  Classification of  Wrist Movements Using EEG-Based Wavelets Features. Conf Proc IEEE Eng Med Biol Soc. 2005;  5:5404-7. 5. Lakany, H. and Conway, B.A., EEG Signal Processing and Classification for Wrist Movements: A Case Study,  Journal of Advanced Technology on  Digital Imaging and Graphics, vol. 24, pp. 295-304, September 2005 6. Lakany, H.,  Izzeldin, I.,  Allan, D.B.  and  Conway, B.A. Optimal Kernel Time-Frequency Representation Of EEG Signals In Patients With Spinal  Cord Injury, Society for Neuroscience Annual meeting 2006. Presentation number 256.20/Z3 7. Lakany, H.;   Izzeldin, I.;   Allan, DB;   Conway, B.A., Sensorimotor Cortex Activation In Spinal Cord Injured Patients: Spatio-Temporal Evidence.    In Proceedings of the IET 3rd International Conference on Advances in Medical, Signal and Information Processing, MEDSIP 2006     8. Valsan, G.;   Worrajiran, P.;   Lakany, H.;   Conway, B.A.;  Predicting Intention and Direction of Wrist Movement from EEG. In Proceedings of IET 3rd International Conference On Advances in Medical, Signal and Information Processing,  MEDSIP 2006       9. Lakany, H., On Feature Selection For Brain Computer Interfaces, Clinical Neurophysiology, vol. 117, pp. 170, 2006 10. Lakany, H. and Conway, B.A. Understanding Intention Of Movement From Electroencephalograms,  Expert Systems, Volume 4, Issue 5, pages 295–304, November 2007 11. Lakany, H.; Valsan, G.; Conway, B.A.; Single Trial EEG Classification Of Observed Wrist Movements,  In Proceedings of the 4th International  IEEE/EMBS Conference on Neural Engineering 2009, pp 359 - 362    12.    Valsan G., Grychtol B, Lakany H. and Conway BA.  The Strathclyde Brain Computer Interface.  Conf Proc IEEE Eng Med Biol Soc. 2009;2009:606-9. 

Classification of Wrist Movements Using EEG-Based Wavelets Features.

EEG Classification Based On Movement Direction And Displacement. Society for Neuroscience Annual Meeting

EEG Signal Processing and Classification for Wrist Movements: A Case Study,

Human Behavior Integration Improves Classification Rates In

On Feature Selection For Brain Computer Interfaces,

Optimal Kernel Time-Frequency Representation Of EEG Signals In Patients With Spinal Cord Injury, Society for Neuroscience Annual meeting

Predicting Intention and Direction of Wrist Movement from EEG.

Sensorimotor Cortex Activation In Spinal Cord Injured Patients: Spatio-Temporal Evidence.

Single Trial EEG Classification Of Observed Wrist Movements,

Steering A Wheelchair By Thought.

Understanding Intention Of Movement From Electroencephalograms, Expert Systems,

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The Strathclyde Brain Computer Interface (S-BCI) : the road to clinical translation

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