Research efforts have improved Brain Computer Interface (BCI) technology in many ways and numerous applications have been prototyped. Motivated by the aim of restoring independence to individuals with severe disabilities, the focus has centred on developing applications for communication, movement control, environmental control, locomotion, as well as neurorehabilitation. Until recently, these BCI systems have been researched almost exclusively in laboratories. The EU project BackHome was aimed at moving BCIs from being laboratory devices for able-bodies users toward practical devices used at home by people with severe limited mobility. BackHome aimed to develop BCI systems into practical multimodal ATs to provide useful solutions for communication, Web access, leisure, cognitive stimulation and environmental control, and to provide this technology for home usage with minimal support. This talk presents the main outcomes of the BackHome project: (i) a modular and distributed architecture able to meet the requirements of a multi-functional BCI with remote home support; (ii) a novel BCI equipment with practical electrodes aimed at setting a new standard of lightness, autonomy, comfort and reliability; (iii) easy-to-use software tailored to people's needs to manage a complete range of multifunctional applications finely tuned for one-click command and adaptive usage; (iv) a telemonitoring and home support system to remotely monitor and assist BCI independent use; and (v) a Web-based application for therapists which offers remote services to plan and monitor BCI-based cognitive rehabilitation and pervasively assess the use of the system and the quality of life of the individual.

1. Eloisa Vargiu
EURECAT
Barcelona
Rome, July 31, 2015
Brain Computer Interfaces on Track
to Home: Results and Lessons Learnt

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The BackHome project
FP7/2007-2013
grant agreement n. 288566

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BackHome is the first European research project aimed at
delivering the ambitious, but critical, step to bring BNCI systems
to mainstream markets
The Objectives
 To study the transition from the hospital to the home
 To learn how different BNCIs and other assistive technologies
work together
 To reduce the cost and hassle of the
transition from the hospital to the home

4. BackHome is aimed at…
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 …producing applied results, developing
o new and better integrated practical electrode systems
o friendlier and more flexible BNCI software
o better telemonitoring and home support tools

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Practical Electrodes

6. Practical electrode systems
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Its design is completely different from all other
devices and it sets a new standard of usability
The dry electrode version is based on the worldwide proven
g.SAHARA electrodes
The tiny and lightweight
device is attached to the EEG
cap to avoid cable
movements and to allow
completely free movements

7. Practical electrode systems
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8. Practical electrode systems
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9. Practical electrode systems
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Flexible BNCI software

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Flexible BNCI software

12. Flexible BNCI software
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Smart Home Control

13. Flexible BNCI software
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Smart Home Control Speller

14. Flexible BNCI software
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Smart Home Control Speller
Web Browsing, e-mail and social networks

15. Flexible BNCI software
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Smart Home Control Speller
Web Browsing, e-mail and social networks
Multimedia player

16. Flexible BNCI software
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Smart Home Control Speller
Web Browsing, e-mail and social networks
Multimedia player
Brain Painting

17. Flexible BNCI software
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Smart Home Control Speller
Web Browsing, e-mail and social networks
Multimedia player
Brain Painting
Cognitive Rehabilitation Games

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Telemonitoring and Home Support

19. Telemonitoring and Home Support
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Home
4 in 1:
Door
Motion
Temperature
Luminosity
3 in 1:
Motion
Temperature
Luminosity
z-wave
smartphone
Raspberry pi

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Therapist Station

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Therapist Station

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Therapist Station

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Therapist Station

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Therapist Station

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Therapist Station

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Intelligent Monitoring

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Intelligent Monitoring
 PP
 Its goal is to preprocess the data iteratively sending a chunk
c to both ED and RA according to a sliding window
approach
 Starting from the overall data streaming, the system
sequentially considers a range of time |ti - ti+1| between a
sensor measure si at time ti and the subsequent measure
si+1 at time ti+1
 The output of PP is a window c from ts to ta, where ts is the
starting time of a given period and ta is the actual time

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Intelligent Monitoring
 ED
 It aims to detect and inform about emergency situations for
the end-users and about sensor-based system critical
failures
 Regarding the critical situations for the end-users, simple
rules are defined and implemented to raise an emergency,
when specific values appear on c
 Regarding the system failures, ED is able to detect
whenever user’s home is disconnected from the middleware
as well as when a malfunctioning of a sensor occurs
 Each emergency is a pair <si; lei> composed of the sensor
measure si and the corresponding label lei that indicates the
corresponding emergency

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Intelligent Monitoring
 AD
 Its goal is to recognize
activities performed by the
user
 To recognize if the user is at
home or away and if s/he is
alone, we implemented a
solution based on machine
learning techniques
 The output is a triple <ts;
te; l>

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Intelligent Monitoring
 EN
 It is able to detect events to be notified
 Each event is defined by a pair <ti; l> corresponding to the
time ti in which the event happens together with a label l
that indicates the kind of event
 Currently, this module is able to detect the following events:
o leaving the home
o going back to home
o receiving a visit
o remaining alone after a visit
o going to the bathroom
o going out of the bathroom
o going to sleep
o awaking from sleep

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Intelligent Monitoring
 SC
 Once all the activities and events have been classified,
measures aimed at representing the summary of the user’s
monitoring during a given period are performed
 Two kinds of summary are provided
o Historical
o Actual
 A QoL assessment system is also provided to assess a
specific QoL items
o Mobility
o Sleeping
o Mood

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Intelligent Monitoring
 RA
 It is aimed at advising therapist about one or more risky
situations before they happen
 The module executes the corresponding rules, defined by
therapists through the healthcare center, at runtime
 A rule is a quadruple <i; v; o; ar>

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Results

35.  Cedar Foundation (Belfast)
 Control Group: N= 5
 End User Group: N=5
(1 F, M= 37 yrs ± 8.7, Post ABI M= 9.8 yrs, ±3.7)
 Home Users: N=3
 University of Würzburg
 Control User Group (gel-based): N=10
(6 F, M: 24.5 yrs ±3.4)
 Control User Group (dry electrodes): N=10
(9 F, M: 24.4 yrs ±2.7)
 End User Group: N=6
(2 F, M=47.3 yrs ± 11)
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End-users

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Practical Electrodes

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Practical Electrodes

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Flexible BNCI software

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Flexible BNCI software

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Telemonitoring and Home Support

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Telemonitoring and Home Support

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Telemonitoring and Home Support

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Telemonitoring and Home Support

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Lessons Learnt

45.  BCI can now be considered as an assistive technology
 To move a technology from the lab to a real home is a very
difficult task
 Testing in a controlled environment is essential
 Data are nothing if you don’t know how to read them
 A user center design approach helps in building a system
accepted by end-users
 A continuous assistance must be given to
caregivers
 Therapists and engineers don’t speak the
same language
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Lessons learned

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BackHome
Acknowledgements
 Web
• www.Backhome-FP7.eu
 LinkedIn
• BackHome-FP7-Research-Innovation
 Twitter
• @BackHomeFP7
 Youtube
• BackHomeFP7
Consortium EURECAT/BDigital Team
And also…
Javier Baustista
Eloi Casals
José Alejandro Cordero
Juan Manuel Fernández
Joan Prota
Alexander Steblin

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eloisa.vargiu@eurecat.org