In pursuit of an easy to use brain computer interface for domestic use in a population with brain injury

Brain-Computer Interfaces (BCI) are systems that can be controlled by the user through harnessing their brain signals. Extensive research has been undertaken within a laboratory setting with healthy users to illustrate the usability of such systems. To bring these systems to users with severe disabilities it is necessary to develop simple, easy to use systems that can be operated b y non-experts outside of the lab and are evaluated by real end users preferably through a user centered design approach. This paper presents a user centered evaluation of a P300 BCI operated by non-expert users in a rehabilitation center with a control group of five healthy participants without acquired brain injury (ABI) and five end users with ABI. Each participant aimed to complete the 30-step protocol three separate times and rate his or her satisfaction from 0 to 10 on the Visual Analogue Scale after each session. Participants then rated their satisfaction with the BCI on the extended QUEST 2.0 and a customized usability questionnaire. The results indicated that end-users were able to achieve an average accuracy of 55% compared to the control group that reported an average of 78%. The findings indicated that participants were satisfied with the BCI but felt frustrated when it did not respond to their commands. This work was phase one of three to move the BCI system into end users homes. Key recommendations for advancing the P300 BCI towards an easy to use, home-based system were identified, including reducing the complexity of the setup, ensuring the system becomes more responsive and increasing the overall functionality.

[1]  G. R. Muller,et al.  Clinical application of an EEG-based brain–computer interface: a case study in a patient with severe motor impairment , 2003, Clinical Neurophysiology.

[2]  J. Wolpaw,et al.  A P300 event-related potential brain–computer interface (BCI): The effects of matrix size and inter stimulus interval on performance , 2006, Biological Psychology.

[3]  Alexander Kaplan,et al.  Adapting the P300-Based Brain–Computer Interface for Gaming: A Review , 2013, IEEE Transactions on Computational Intelligence and AI in Games.

[4]  Tobias Kaufmann,et al.  A User Centred Approach for Bringing BCI Controlled Applications to End-Users , 2013 .

[5]  Michael Tangermann,et al.  Brain-computer interface controlled gaming: Evaluation of usability by severely motor restricted end-users , 2013, Artif. Intell. Medicine.

[6]  J. DeLuca,et al.  Cognitive impairment in multiple sclerosis , 2008, The Lancet Neurology.

[7]  Helge J. Ritter,et al.  2009 Special Issue: The MindGame: A P300-based brain-computer interface game , 2009 .

[8]  G Pfurtscheller,et al.  EEG-based communication: improved accuracy by response verification. , 1998, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[9]  T. Vaughan,et al.  Toward independent home use of brain-computer interfaces: a decision algorithm for selection of potential end-users. , 2015, Archives of physical medicine and rehabilitation.

[10]  J. Wolpaw,et al.  A P300-based brain–computer interface for people with amyotrophic lateral sclerosis , 2008, Clinical Neurophysiology.

[11]  J. Wolpaw,et al.  Brain–computer interfaces in neurological rehabilitation , 2008, The Lancet Neurology.

[12]  John Williamson,et al.  User-centered design in brain-computer interfaces - A case study , 2013, Artif. Intell. Medicine.

[13]  A. Kübler,et al.  Brain Painting: First Evaluation of a New Brain–Computer Interface Application with ALS-Patients and Healthy Volunteers , 2010, Front. Neurosci..

[14]  Cornelia Herbert,et al.  Brain Painting: Usability testing according to the user-centered design in end users with severe motor paralysis , 2013, Artif. Intell. Medicine.

[15]  Josef Faller,et al.  Control or non-control state: that is the question! An asynchronous visual P300-based BCI approach , 2015, Journal of neural engineering.

[16]  Paul McCullagh,et al.  Realistic Expectations with Brain Computer Interfaces , 2012 .

[17]  Eloisa Vargiu,et al.  Cognitive Rehabilitation through BNCI: Serious Games in BackHome , 2014 .

[18]  O. Hardiman,et al.  The syndrome of cognitive impairment in amyotrophic lateral sclerosis: a population-based study , 2011, Journal of Neurology, Neurosurgery & Psychiatry.

[19]  Gernot R. Müller-Putz,et al.  Brain-controlled applications using dynamic P300 speller matrices , 2015, Artif. Intell. Medicine.

[20]  J. Wolpaw,et al.  Brain-computer communication: unlocking the locked in. , 2001, Psychological bulletin.

[21]  Gernot R. Müller-Putz,et al.  The auditory P300-based single-switch brain-computer interface: Paradigm transition from healthy subjects to minimally conscious patients , 2013, Artif. Intell. Medicine.

[22]  N. Birbaumer,et al.  Predictability of Brain-Computer Communication , 2004 .

[23]  Sarmad Alshawi,et al.  THE ROLE OF USER REQUIREMENTS RESEARCH IN MEDICAL DEVICE DEVELOPMENT , 2010 .

[24]  A. Kübler,et al.  Face stimuli effectively prevent brain–computer interface inefficiency in patients with neurodegenerative disease , 2013, Clinical Neurophysiology.

[25]  G. Pfurtscheller,et al.  ‘Thought’ – control of functional electrical stimulation to restore hand grasp in a patient with tetraplegia , 2003, Neuroscience Letters.

[26]  Jonathan R Wolpaw,et al.  A brain-computer interface for long-term independent home use , 2010, Amyotrophic lateral sclerosis : official publication of the World Federation of Neurology Research Group on Motor Neuron Diseases.

[27]  Donatella Mattia,et al.  A Brain-Computer Interface as Input Channel for a Standard Assistive Technology Software , 2011, Clinical EEG and neuroscience.

[28]  Patrick Carmichael,et al.  BNCI systems as a potential assistive technology: ethical issues and participatory research in the BrainAble project , 2014, Disability and rehabilitation. Assistive technology.

[29]  Selina Wriessnegger,et al.  The Evaluation of a Brain Computer Interface System with Acquired Brain Injury End Users , 2014 .

[30]  Selina Wriessnegger,et al.  A P300 BCI for e - inclusion, cognitive rehabilitation and smart home control , 2014 .

[31]  J. Polich Updating P300: An integrative theory of P3a and P3b , 2007, Clinical Neurophysiology.

[32]  R. Kreis,et al.  Neuropsychological impairments and the impact of dystrophin mutations on general cognitive functioning of patients with Duchenne muscular dystrophy , 2011, Journal of Clinical Neuroscience.

[33]  A. Kübler,et al.  Flashing characters with famous faces improves ERP-based brain–computer interface performance , 2011, Journal of neural engineering.

[34]  Louise Demers,et al.  The Quebec User Evaluation of Satisfaction with Assistive Technology (QUEST 2.0): An overview and recent progress , 2002 .

[35]  Suzanne Martin,et al.  P300 Brain Computer Interface Control after an Acquired Brain Injury , 2015 .

[36]  Brendan Z. Allison,et al.  Comparison of Dry and Gel Based Electrodes for P300 Brain–Computer Interfaces , 2012, Front. Neurosci..