Brain Computer Interfaces on Track to Home: Results and Lessons Learnt

Eloisa Vargiu

EURECATBarcelona

Rome, July 31, 2015

Brain Computer Interfaces on Track to Home: Results and

Lessons Learnt

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The BackHome project

FP7/2007-2013grant 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

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BackHome is aimed at… …producing applied results, developing

o new and better integrated practical electrode systems

o friendlier and more flexible BNCI softwareo better telemonitoring and home support tools

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Practical Electrodes

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Practical electrode systems

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

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Flexible BNCI software

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Flexible BNCI softwareSmart Home Control

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Flexible BNCI softwareSmart Home Control Speller

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Web Browsing, e-mail and social networks

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Web Browsing, e-mail and social networksMultimedia player

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Brain Painting

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Brain PaintingCognitive Rehabilitation Games

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Telemonitoring and Home Support

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Home

4 in 1:DoorMotionTemperatureLuminosity

3 in 1:MotionTemperatureLuminosity

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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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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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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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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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 homeo going back to homeo receiving a visito remaining alone after a visito going to the bathroomo going out of the bathroomo going to sleepo awaking from sleep

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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 providedo Historicalo Actual

A QoL assessment system is also provided to assess a specific QoL itemso Mobilityo Sleepingo Mood

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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

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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)

End-users

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Lessons Learnt

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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 tocaregivers

Therapists and engineers don’t speak thesame language

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 BaustistaEloi CasalsJosé Alejandro CorderoJuan Manuel FernándezJoan ProtaAlexander Steblin

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[email protected]

Healthcare

Brain Computer Interfaces on Track to Home: Results and Lessons Learnt