Brain, Body, and Bytes CHI 2010 Workshop Presentations

BBB Workshop Overview
Audrey Girouard, Erin Treacy Solovey,
Regan Mandryk, Desney Tan,
Lennart Nacke, Robert J.K. Jacob

BBB Workshop: Bringing Together
BCI& Physiological Computing

Brain, Body and Bytes: Psychophysiological User Interaction Workshop at CHI 2010 3

Workshop Goals
1. Provide a platform for creating synergies
between two related and emerging HCI
disciplines (PC and BCI).

2. Kick-start BBB research in HCI by identifying
key research questions and application areas

3. Enhancing HCI methodologies by adding these
new methods and techniques to the toolbox of
HCI researchers


About Us


About you
• 25 Participants • X EEG papers
• Y fNIRS papers
• Z EMG papers
• 9 Brain papers
• A GSR
• 19 Body papers
• B Eye tracking
Brain Body

32%

68%


Workshop Discussion Questions

<on your tables>



What terms/acronyms should we use to
describe our work?
Psychophysiological computing seems a bit long…
BBB?
BBI?
Other suggestions?


Today’s Schedule
09:00-10:30 Introduction of organizers & participants, CHI madness style

10:30-11:00 Break

11:00-12:30 Group Discussion (assign groups + topic)

12:30-14:00 Lunch

14:00-15:30 Small group discussion (1h of discussion + 30 minutes of reporting)

15:30-16:00 Break

16:00-17:00 Report from small groups, recap, group discussion – barriers to
publication in HCI


Staying connected
Website http://www.eecs.tufts.edu/~agirou01/workshop/

Twitter http://twitter.com/bbbCHI2010

Twitter #bbbCHI2010
Hashtag
Facebook http://www.facebook.com/home.php#/pages/Atlanta-GA/Brain-Body-and-
Bytes-Psychophysiological-User-Interaction-at-CHI-2010/136581171786

Flickr http://www.flickr.com/groups/bbbchi/

Slide Share http://www.slideshare.net/group/brain-body-and-bytes-psychophysiological-
user-interaction


And now… 2 Minute Introductions

• About your research:
– Application or domain space? (e.g. video games)
– Signal you are measuring? (e.g. fNIRS)
– Device/hardware you are using? (e.g. ISS, Inc)

• What would you like to get out of workshop?
– What problems are you facing?
– What do you see in common with others here?
– What device would you like to know more about?


List of Presentations
1. Adriane Randolph 16. Katarzyna Wac
2. Krzysztof Z. Gajos 17. Feng Tian
3. Saraswathi Bellur 18. Veronica Zammitto
4. Danny Plass-Oude Bos 19. Johanna Octavia
5. Kai Kuikkaniemi 20. Bob Wray
6. Mitchel Benovoy 21. Marjolein van der Zwaag
7. Yee Chieh Chew
8. Martha Crosby
9. Jan Kallenbach
10. Eva Oliveira
11. Lucia Filgueiras
12. Marc Grootjen
13. Melody Moore Jackson
14. Johann Schrammel
15. Daniel Sjölie


Brain-Computer Interfaces as Child’s
Play: Using Mental Training to Improve
Motor Skills in Children

Adriane B. Randolph, Ph.D.
Ashley Ingraham
Kennesaw State University

Outline
• Research Position
– Examine ties between sensorimotor area and physical ability to uncover a
training tool to aid children’s motor skill development

• Pilot Study
– 3 able-bodied adults (2 Male, 1 Female)
– Acquire baseline Wii tennis score
– Conduct mental training with BCI2000 mu training protocol for 5 days
– Retest Wii tennis score for improvements Participant Session One Session Five

– Mixed results AZ 98 60
CM 70 64
MW 103 118
• Future Work
– Extend study with 20 able-bodied adults (currently in progress)
– Analyze link between training and baseline ability
– Test with elementary-aged children


Screening for Mu
Ashley prepares participant to use mental imagery of hand and foot
movements to determine his best channels for use in mu BCI training before
he plays a match of Wii Tennis.


Thank you

Adriane B. Randolph, Ph.D.
Kennesaw State University BrainLab

(770) 423-6083
brainlab@kennesaw.edu
http://coles.kennesaw.edu/brainlab/


Understanding How to Design Complex
Brain-Controlled Applications

Krzysztof Z. Gajos

How to Design Brain-Controlled
Applications?
• Basic operations


Applications?


Applications?
• Basic operations


Applications?
• Interaction between primary task and BCI
performance


Applications?
• Error prevention, recovery, and actions
with side effects


Thank you
Krzysztof Gajos
kgajos@eecs.harvard.edu
http://www.eecs.harvard.edu/~kgajos/


Psychophysiological Responses
to Media Interfaces

Saraswathi Bellur S. Shyam Sundar

Media Effects Research Lab
http://www.psu.edu/dept/medialab
Pennsylvania State University

Biopac MP 35 & MP 150
ACQknowledge software
 Skin Conductance (EDA)
 Heart Rate (ECG)
 Facial EMG
 Brain Wave (EEG)
 Eye Movement (EOG)

 Application Areas
Interface presentation
Information Processing
Usability Testing &
Evaluation
 User Experience


Physiological Measures: Affect, Arousal & Attention
3. ECG: Heart
Rate recording

1. EEG: Bipolar recordings Occipital Alpha Blocking
Orienting
Response via
BPM change

2. Skin Conductance (EDA) Tonic arousal levels 4. Facial EMG:
Corrugator &
Zygomatic
recordings


Theoretical Frameworks
• The Media Equation: Mediated Life = Real Life (Reeves & Nass,
1996).
• Structural aspects of media technologies: Automatic or
Controlled processing? Voluntary or Involuntary responses?
• Specific cues triggered via interactions with four types of
media affordances, MAIN model (Sundar, 2008)

Animation
M: MODALITY Customization Heuristics

A: AGENCY
Information Perceptual
Scent Bandwidth
I: INTERACTIVITY

N: NAVIGABILITY Contingency UGC & Sourceness


Thank you
Saras Bellur (saras@psu.edu)
S. Shyam Sundar (sss12@psu.edu)

Media Effects Research Lab
http://www.psu.edu/dept/medialab
Pennsylvania State University


BCI for Games

Danny Plass-Oude Bos
Boris Reuderink, Bram van de Laar,
Hayrettin Gürkök, Christian Mühl,
Mannes Poel, Anton Nijholt, Dirk Heylen
HMI, University of Twente

Target Group: the general population


BCI: issues


BCI: new information, new features


Acceptance: Usability & Realistic Settings


Subjects  Users


Thank you
d.plass@ewi.utwente.nl
http://hmi.ewi.utwente.nl/~oudebos


Designing Biofeedback for
Games and Playful Applications
Kai Kuikkaniemi
Helsinki Institute for Information
Technology

Emoshooter


Emoshigeru - cinema games
Real-time
Public display
Biosignal adaptation
Massive co-located
Shooter / Strategy
Part of Shigeru-games
local
wlan

Public display
Connected to game
server

Brain, Body and Bytes: Psychophysiological User Interaction Workshop at CHI 2010

Presemo – presentation engine


Emolisten – biosignal sonification


Research statement
• Social Games and Playful Applications,
which Utilize Biosignals
• Grey Area between Adaptation and
Biofeedback
– Implicit and Explicit Biofeedback
• Biosignal Synchrony (Inter-Personal)


Our team and projects
Professor Marko Turpeinen
Professor Timo Saari
Ilkka Kosunen, Toni Laitinen, Petri Lievonen, Pauli Ojala

Fun of Gaming (EU FP6) (2006-2009)
Enactive Social Media and Games (Aalto) (2009)
Emokeitai (Tekes – National Innovation Agency) (2009-2011)
Thanks:
CKIR / Niklas Ravaja


Psychophysiological signal
analysis and classification
Mitchel Benovoy
Centre for Intelligent Machine, McGill University

Outline
• Signals

• Hardware

• Application domains


Signals
Galvanic Skin Response Phalange Temperature
1.5
∆ Cond. (µS)

1

0.5

0

-0.5
0.5 1 1.5 2 2.5
Sample 4
x 10

Electromyogram

42
Blood Volume Pulse – HR & Amp. Respiration – Rate & Amp.
40 2.2
2
38
Refl.

Exp.
1.8
36 1.6
34 1.4
32 1.2
1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000
7
1
0.8
Amp.

Amp.
6 0.6
0.4
5 0.2
1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000
105
30
HR (bpm)

RR (bpm)

100
25
95 20

90 15
1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000


Hardware
• Thought Technology ProCom Infinity

Images via www.thoughttechnology.com

Application Domains
• Affective Computing

– Interaction evaluation (UI, devices, latency)
– Music & emotion research
– Psychoneurology

Thank you

benovoym@cim.mcgill.ca


BCI and Creativity

Yee Chieh (Denise) Chew
Georgia Institute of Technology

Outline
• fNIR and Creative Expression
• EEG and Music


Character Templates for fNIR
Created with a mouse, these are templates that subjects traced
using fNIR


Thank you
ychew@gatech.edu


Physiological Measures Used for
Identification of Cognitive States and
Continuous Authentication
Curtis S. Ikehara & Martha E. Crosby
University of Hawaii at Manoa

Outline
• Overview of our Research Using
Physiological sensors
– Assessing Cognitive States
– Continuous Authentication
• Possible Applications


Multi-modal Physiological Sensors System
Eye Tracker
(Gaze Position, Fixation Number, Fixation
Duration, Repeat Fixations, Search Patterns,
MTF Pupil Size, Blink Rate, Blink Duration)
(Moving Target Fractions) HUMAN
Pressure Mouse
(Pressures on Mouse Button and Body)

Laptop
(Data Collection, Analysis & Display)
EDA / TSA Finger Sensors
(Electrodermal and Temperature Sensor Activity)

Oximeter
(Pulse, Blood Oxygen)

Custom Electronics
(Analog to Digital Converter)
Brain, Body and Bytes: Psychophysiological User Interaction Workshop at CHI 2010 56 56

A Composite of Over 20 Mouse Clicks of
Three People (Click Signature)
US Patent # 7,245,218

Subject 4

Subject 5 Subject 7
57

Possible Applications
• The student’s confidence can be assessed by
evaluating the distortions of the mouse click
signature.
• High Value Vehicle Control
– Pressure and other physiological sensors can be
attached to a steering (e.g. aircraft yoke or
steering wheel) .
– Sensors can be used to indicate that:
• The authorized pilot or driver is in control.
• The cognitive state of the pilot is consistent with the
vehicle’s activity.


Thank you
Curtis S. Ikehara Martha E. Crosby
cikehara@hawaii.edu crosby@hawaii.edu

University of Hawaii at Manoa
1680 East-West Road - POST 317
Honolulu, Hawaii 96822 USA


Measuring Interaction Experiences:
Integration of Multiple
Psychophysiological Methods
Jan Kallenbach
Aalto University
Helsinki, Finland

Outline
User Experience
=
Choice & Interaction
+
Cost-Benefit Evaluations

Measuring Interaction Experiences?

Software Framework

Multimodal
Measurements

Client Application for UX Research
Sampling frequency: max 1000Hz
Psychophysiological Measures: Gaze, Pupil Size, EDA, EMG, EEG, …
Behavioral Measures: Keyboard, Mouse, Slider, …
System Measures: Window Events


Thank you
Jan Kallenbach
Research Scholar

Aalto University
School of Science & Technology
Department of Media Technology
P.O. Box 15500
FIN - 00076 Aalto


Towards enhanced video access and
recommendation through emotions

Eva Oliveira
Nuno Ribeiro
Teresa Chambel
IPCA | LASIGE

We Want
• To access to videos based on emotions
– user actual emotional (psychophysiological) state
– video emotional content
– director’s perspective
• To recommend videos based on emotions
– create a set of rules from every affective
classification perspective (user and video, director)


Affective video recommendation
system requirements ()
• Every video must be classified affectively from the user
and from the content perspective.
• Every user has an emotional profile constituted by all his
implicit (physiological) classification of every classified
movie.
• User physiological signals can change the way we watch
the videos


User Classification Issues
• Automatic Classification
– Users emotional state
• emotional model
– Classification techniques
• Result output
• Accuracy
• Lab contexts


Current State
• Capturing users phychophysiological signals
(BIOPAC System) while watching movies

This is my exciting picture

• Next step: several classification techniques to
convert signal result into emotional categories

Thank you
• Eva Oliveira :

• eva.oliveira@gmail.com


Your users can’t talk!
How can you tell they liked
your system?
Lucia Filgueiras and Claudia Tambascia
Escola Politecnica
Universidade de São Paulo

What we want to do
• Develop products for cerebral palsy users
• Perform participatory design
• Understand non-verbal communication


Direct measures and usability evaluation
HRV and workload measures Eye tracking in iTV apps
in control room operability evaluation
evaluation
Comparison of eye tracker
NASA TLX, SWAT, Heart Rate
findings to conventional
Variability usability tests results
Variabilidade da frequência cardíaca OP13

LF nu / HF nu LSC Média

18,0
Operador analisa sistema após vários
Índice do Balanço Simpático? Parassimpático

Ação do operador para alarmes: temperatura alta (10:29); Ação do operador para
16,0
correção nível alto (10:32) e segurança correção
14,0 (10:32; 10:37; 10:43) 14,1

12,0 Alarme de 11,7
temperarura alta:
10,0 10:15
9,1
8,3 8,5
8,0
7,3
6,6
6,0 5,9
5,2 5,3 5,6
4,6 5,0
4,0 3,9 3,8 4,0 4,4 4,1
2,9 3,3
2,6 2,6 2,6 2,3 2,4
2,0

0,0
5

0

5

0

5

0

5

0

5

0

5

0

5

0

5

0

5

0

5

0

5

0

5

0

5
:3

:4

:4

:5

:5

:0

:0

:1

:1

:2

:2

:3

:3

:4

:4

:5

:5

:0

:0

:1

:1

:2

:2

:3

:3
09

09

09

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10

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10

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10

10

10

11

11

11

11

11

11

11

11

HORA

Heart Rate Variability of an operator in a unstable unit situation


Our questions:
• How personalized are body expressions for cerebral palsy patients?

• Are there general body expressions that can mean the same for everyone?

• Is it possible to determine a generic way of measuring these expressions?

• Is there any other liaison besides gestures and facial expressions , like an
unconscious connection of limbic systems that can be responsible for the
mutual sensitive communication?

• Is there a reference body signal that can be used as a reliable parameter to
evaluate body expressions?

• How can we establish a correlation between body expressions and
patient´s opinion of approval or disapproval of a design solution?


Thank you!
Contact us:

Lucia V. L. Filgueiras
Escola Politecnica, Universidade de São Paulo
lucia.filgueiras@poli.usp.br

Claudia A.Tambascia
Fundação CPqD
claudiat@cpqd.com.br

http://lts-i.pcs.usp.br


Human Computer Symbiosis
by Mutual Understanding
Marc Grootjen
EagleScience-TNO

Outline
• Symbiosis & mutual understanding
• Information models
• Interfaces


Maximum task performance

Symbiotic entity
Disturbance
Mutual
human understanding computer

Tasks (goals)

Human-computer symbiosis with mutual understanding
as key issue for maximum task performance


Domesticating Brain-
Computer Interfaces: Systems
for the Home
Melody Moore Jackson
BrainLab
School of Interactive Computing
Georgia Tech

Extended Home-based BCI study

• fNIR-based BCI for communication
• 32 People with ALS (so far) over 1 year
• 78% (25) could operate the device on
initial visit with accuracy > 70%
• Accuracy to 100%, average 76.5%
• Issues:
– Motivation
– Data Collection
– Remote Support

The Kokoro Gatari
Brain Computer Interface based on Functional Near Infrared
imaging


Thank you
Melody Moore Jackson
melody@cc.gatech.edu
404-277-1324

Ian McClendon
imclend@cc.gatech.edu
404-641-7712


Get up, move on! Using
Electromyography to Explore
the Relationship of Experience
and Motion
Johann Schrammel
Centre for Usability Research and
Engineering

Outline
• Compare experiences and emotions in
conditions with and without movements
with Electromyography(EMG) and other
methods


Outline
• Can EMG successfully be used to
estimate a user‘s emotional experience in
motion contexts (artefacts, feasibility,
etc.)?


Outline
• Is a more embodied way of controlling
interaction actually amplifying experience?


Results
• EMG is suitable for motion contexts.
• Both EMG and self-report data indicate
that natural body movement intensifies
emotions.
• It’s crucial that body movements are in
accordance with activity.


Thank you
schrammel@cure.at


Reality-Based Brain-
Computer Interaction
Daniel Sjölie
Umeå University, Umeå, Sweden

Reality-Based Brain-Computer
Interaction
• Complex, reality-based, interaction with the
ability to adapt to the user in real-time
– Using brain measurements to evaluate
• Direct brain-computer interaction
– The user can be unaware
• We’re using a combination of virtual
reality (VR) and functional MRI (fMRI)


HCI and cognition
• An improved understanding of the brain in this
interactive and complex context is needed
• Build on theories of cognition as a foundation
for the development of advanced HCI
– As interaction moves towards being reality-based
we move closer to “interacting with reality”, and
thus to general cognition
• Cognitive neuroscience and computational
models of the brain are important tools


Working with fMRI and Virtual Reality
Working with fMRI requires specialist competence and forces us to
deal with powerful magnetic fields and restricted mobility, but the
ability to measure the whole brain is an invaluable research tool.


Thank you
Daniel Sjölie, daniel@cs.umu.se


emoBAN:
Improving Quality of Life
via Psychophysiological Mobile
Computing
Katarzyna Wac, Anind K. Dey
Carnegie Mellon University, HCII

emoBAN idea
• “what always speaks silently is the body” (Brown, 1990)
– measure QoL via psychophysiology


emoBAN: Usable Useful Functional
• Body Area Network
– sensors
• vital signs, e.g., ECG, temp, resp
• context, e.g., location, time, activity
– actuators
• feedback via, e.g., AV, tactile, light


Thank you
Dr Katarzyna Wac
Carnegie Mellon University
Human-Computer Interaction Institute
katewac@cmu.cs.edu
www.cui.unige.ch/~wac


Leveraging Psychophysical Data in
Monitoring and Analyzing the
States of Badminton Players
Feng Tian, Wencan Luo, Jing Dai, Reza Naima, Yongmin Cheng, Hongan
Wang, Guozhong Dai

Institute of Software, Chinese Academy of Sciences
Physical Education Collage, Beijing Sport University
Berkeley Institute of Design, UC. Berkeley
China Badminton Team, General Administration of Sport of China
4/20/2010

Traces and physical
actions
- Camera, accelerometers

Psychophysical data
- Berkeley Tricorder
ECG, EMG, Pulse Oximetry,
BioImpedance Respiration


Exploring Quantitative Methods
for Evaluating Sports Games
Veronica Zammitto
Simon Fraser University
Magy Seif El-Nasr
Simon Fraser University
Paul Newton
Electronic Arts Canada

Current Study: NBA Live 10
• Eye tracking
– Eye Tech TM3
• Psycho-physiological data
– ProComp5 Infiniti
– EMG (corrugator supercilii and zygomatic major),
EKG (heart rate), GSR and BVP
• Gameplay Telemetry Data
– event-based RPC telemetry collection system
• Triangulation

What was the user
the
Data Triangulation - NBA Live 10 emotional response?
looking at?
Game user experience
Event, when
Was positive?
Where user’s
Was negative?
attention was?

Data Triangulation - NBA Live 10
Game user experience


Thank you
Veronica Zammitto
vzammitt@sfu.ca

Magy Seif El-Nasr
magy@sfu.ca

Paul Newton
pnewton@ea.com


Exploring Psycho-physiological Measures for
the Design and Behavior of Socially-Aware
Avatars in Ubicomp Environments

Johanna Renny Octavia
Hasselt University, Belgium

Avatars in Ubicomp Environment
 User’s representation  Mediator between system and user (companion)
 To design a socially-aware avatar that adapts its behavior according to user’s emotion
 Any incorrect actions of the avatar can lead to loss of identification of the user


Our informal experiment
 ProComp Infiniti device, measured GSR, EMG and BVP
 Higher physiological values when playing against a friend
 How different social relationships between players influence those values?


Thank you

Expertise Centre for Digital Media (EDM)
Web: www.edm.uhasselt.be
Email: johanna.octavia@uhasselt.be


Tailoring Practice for
Individualized Learning via
Cognitive State
Bob Wray
Soar Technology, Inc.

Why I need Psychophysiological
Computing
• Practice environments are becoming
common-place
• Adaptive tailoring of practice is important for
individualized experience/learning
• Need more than traditional adaptations (e.g.,
scaffolding)
– Adapting to engage learner / maintain interest
– Adaptation to reducing “gaming” in environment
– Adaptation to elicit learner affect/arousal (“real”)


Research Testbed
Narrative, conversational, inter-cultural training application


Thank you
Bob Wray
Soar Technology
wray@soartech.com, 919.967.5079

Brian Magerko
Georgia Institute of Technology
magerko@git.edu


Guidelines to biosignal
driven HCI
Marjolein van der Zwaag

Philips Research Europe, University of
Groningen

Guidelines to biosignal
driven HCI
Marjolein van der Zwaag
Egon L. van den Broek
Joris H. Janssen

Outline
• Signal(s) measured: psycho-physiological
• Application domain: Affective computing
• Device used: Nexus 10, mind media

• Guidelines for processing biosignals
– Triangulation
– Identification of users
– Biosignal characteristics
– Temporal construction


Unobtrusive affective computing
Music direct your mood concept


Thank you

Marjolein.van.der.zwaag@philips.com



What is brain/body computing good for?

How can we think systematically about this
space?



What are the major themes of HCI that can
be explored using brain/body interaction?



What specific brain & body sensing modalities
are interesting and how can we use them?

What are most innovative technologies in this
space?

How do these compare with existing practices
(NASA-TLX, Likert-style questionnaires, etc)?


How can BCI and PC be defined within an HCI
context?

How is a HCI context different from
neuropsychology, biomedicine or biomedical
engineering?

How does HCI contribute to
psychophysiological computing (or more
specifically neural engineering, bio-
engineering, etc)?

If we could measure anything on brain and
body, what would we want to measure and
why?

What cognitive states and emotions have
been investigated with sensors?

What methodologies are best for what
signals?


How can accurate physiological,
psychological and neurophysiological
measurements improve HCI?



With what software techniques and
hardware technologies could these signals
be optimally processed?

How can software account for the
combination of user actions taken to
accomplish a task versus actions that
would surprise the user, yet optimize their
experience?


What are the issues that must be considered
when using brain/body computing for
direct control, passive sensing, adaptation,
etc?


Groups
• How to go from raw data to something
else
– Emotions: TABLE 1
– Data Fusion: TABLE 4
• How to use these things in interactive
interfaces: TABLE 3


BBB Workshop: Bringing Together
BCI& Physiological Computing


Staying connected
Website http://www.eecs.tufts.edu/~agirou01/workshop/

Twitter http://twitter.com/bbbCHI2010

Twitter #bbbCHI2010
Hashtag
Facebook http://www.facebook.com/home.php#/pages/Atlanta-GA/Brain-Body-and-
Bytes-Psychophysiological-User-Interaction-at-CHI-2010/136581171786

Flickr http://www.flickr.com/groups/bbbchi/

Grouplist http://groups.google.com/group/brain-body-interfaces/

Slide Share http://www.slideshare.net/group/brain-body-and-bytes-psychophysiological-
user-interaction


BBB at CHI2010
Brains and Brawn session: Tuesday 11:30-1pm

Making Muscle-Computer Interfaces More Practical
T. Scott Saponas, Desney S. Tan, Dan Morris, Jim Turner, James A. Landay
University of Washington, USA
Microsoft Corporation, USA
A Novel Brain-Computer Interface Using a Multi-Touch Surface
Beste F. Yuksel, Michael Donnerer, James Tompkin, Anthony Steed.
University College London, UK
The Influence of Implicit and Explicit Biofeedback in First-Person Shooter Games
Kai Kuikkaniemi, Toni Laitinen, Marko Turpeinen, Timo Saari, Ilkka Kosunen, Niklas Ravaja,
Helsinki Institute for Information Technology, Finland
Center for Knowledge and Innovation Research, Finland
Effects of Interactivity and 3D-motion on Mental Rotation Brain Activity in an Immersive Virtual
Environment
Daniel Sjölie, Kenneth Bodin, Eva Elgh, Johan Eriksson, Lars-Erik Janlert, Lars Nyberg
Umeå University, Sweden


THANK YOU
• Keep in touch!


Brain, Body, and Bytes CHI 2010 Workshop Presentations

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Brain, Body, and Bytes CHI 2010 Workshop Presentations