SlideShare a Scribd company logo
Measuring the User Experience
of Adaptive User Interfaces
using EEG: A Replication Study
Daniel Gaspar-Figueiredo <dagasfi@epsa.upv.es>
Jean Vanderdonckt <jean.vanderdonckt@uclouvain.be>
Silvia Abrahao <sabrahao@dsic.upv.es>
Emilio Insfran <einsfran@dsic.upv.es>
EASE2023
Overview
Motivation
Baseline Experiment
Replication Study
Data Analysis
Study Results
2
Motivation
Baseline Experiment
Replication Study
Data Analysis
Study Results
• Adaptive User Interfaces (AUI) – UI that can be changed considering the Context of use to
enhance the User eXperience (UX)
• Type of changes:
• Layout
• Colours
• Modalities
• Element interactions
• Element styling
• …
Motivation
3
…
User Interface
Context of
use
User
Platform
Environment
• Too wide spectrum of possibilities
Motivation Baseline Experiment Replication Study Data Analysis Study Results
Motivation
4
Different UI designs produce different UX
Measure UX through physiological
data such as brain activity (EEG)
Motivation Baseline Experiment Replication Study Data Analysis Study Results
Overview
Motivation
Baseline Experiment
Replication Study
Data Analysis
Study Results
5
Motivation
Baseline Experiment
Replication Study
Data Analysis
Study Results
Baseline Experiment: Summary
6
• Analyse a set of adaptive graphical menus for the purpose of comparing them with respect to
their UX produced in terms of cognitive load, engagement, memorisation, and attraction from
the point of view of both researchers and user interface designer in the context of end-users of
applications with graphical menus
• 20 graphical adaptive menus were compared (mail manager and web browser applications)
• 40 participants aged from 18 to 63 years old (mean 42)
• Cognitive load, engagement, memorisation, and attraction were computed using EEG analysis
• RQ: Do graphical adaptive menus have a different influence on the UX?
Motivation Baseline Experiment Replication Study Data Analysis Study Results
Baseline Experiment: Summary
7
Statistically significant differences were found between the menus regarding the Cognitive load,
engagement, memorisation, and attraction
May be due to diversity of users
• Multiple backgrounds
• Very wide range of ages
Motivation Baseline Experiment Replication Study Data Analysis Study Results
Overview
Motivation
Baseline Experiment
Replication Study
Data Analysis
Study Results
8
Motivation
Baseline Experiment
Replication Study
Data Analysis
Study Results
Replication Study: Goal
9
• Analyse a set of adaptive graphical menus
• For the purpose of comparing them
• with respect to their user experience produced in terms of cognitive load, engagement,
memorisation, and attraction
• From the point of view of both researchers and user interface designers
• In the context of and end-users' group with a background in computer science
RQ1: Do the 20 graphical adaptive menus have a different influence on the UX?
RQ2: Does the UX measured using EEG signals correlate with the subjective ratings obtained using traditional
questionnaires?
Goal
Experimental
objects
Variables Hypotheses
Participants Study design Instrumentation
Motivation Baseline Experiment Replication Study Data Analysis Study Results
Replication Study: Experimental objects
10
Vanderdonckt, J., Bouzit, S., Calvary, G., & Chêne, D. (2019). Exploring a
design space of graphical adaptive menus: normal vs. small screens. ACM
Transactions on Interactive Intelligent Systems (TiiS), 10(1), 1-40.
Design space for graphical adaptive menus
(8 variables):
• Position
• Orientation
• Size
• Shape
• Value
• Color
• Texture
• Motion
Goal
Experimental
objects
Variables Hypotheses
Participants Study design Instrumentation
Motivation Baseline Experiment Replication Study Data Analysis Study Results
• We selected 20 different graphical adaptive menus from 
Replication Study: Experimental objects
11
Vanderdonckt, J., Bouzit, S., Calvary, G., & Chêne, D. (2019). Exploring a design
space of graphical adaptive menus: normal vs. small screens. ACM Transactions
on Interactive Intelligent Systems (TiiS), 10(1), 1-40.
Goal
Experimental
objects
Variables Hypotheses
Participants Study design Instrumentation
Motivation Baseline Experiment Replication Study Data Analysis Study Results
ItemA
MenuItem1
MenuItem2
MenuItem3
MenuItem4
MenuItem5
MenuItem6
MenuItem7
MenuItem1
MenuItem2
MenuItem3
MenuItem4
MenuItem5
MenuItem6
MenuItem7
ItemA
MenuItem1
MenuItem2
MenuItem3
MenuItem4
MenuItem5
MenuItem6
MenuItem7
ItemA ItemA
MenuItem1
MenuItem2
MenuItem3
MenuItem4
MenuItem5
MenuItem6
MenuItem7
MenuItem1
MenuItem2
MenuItem3
MenuItem4
MenuItem5
MenuItem6
MenuItem7
ItemA
MenuItem1
MenuItem2
MenuItem3
MenuItem4
MenuItem5
MenuItem6
MenuItem7
ItemA
MenuItem1
MenuItem2
MenuItem3
MenuItem4
MenuItem5
MenuItem6
MenuItem7
ItemA
MenuItem3
MenuItem6
MenuItem1
MenuItem2
MenuItem4
MenuItem5
MenuItem7
ItemA
Static menu Bolding
menu
Underlying
menu
Glowing
menu
Adaptive
activation-
area menu
Boxing
menu
Morphing
menu
Ephemeral
menu
Highlighting
menu
Split menu
without
replication
Greyscaling
menu
MenuItem1
MenuItem2
MenuItem3
MenuItem4
MenuItem5
MenuItem6
MenuItem7
ItemA
Replication Study: Experimental objects
12
MenuItem2 MenuItem4
MenuItem3
MenuItem6
MenuItem1
MenuItem8
MenuItem7
MenuItem5 ItemA
Leaf menu
MenuItem1
MenuItem2
MenuItem3
MenuItem4
MenuItem5
MenuItem6
MenuItem7
ItemA
Italicizing
menu
Blinking
menu
Pulsing
menu
Out-of-context
menu
Temporal menu Pink menu
MenuItem1
MenuItem2
MenuItem3
MenuItem4
MenuItem5
MenuItem6
MenuItem7
ItemA
Twisting
menu
Rotating
menu
• We represented these menus in two different contexts
Goal
Experimental
objects
Variables Hypotheses
Participants Study design Instrumentation
Motivation Baseline Experiment Replication Study Data Analysis Study Results
Goal
Experimental
objects
Variables Hypotheses
Participants Study design Instrumentation
Replication Study: Experimental objects
New tab
New window
Zoom
Favorites
New InPrivate window
History
Downloads
Apps
Extensions
Collections
Print
Web capture
Share
Find on page
Read aloud
More tools
Settings
Help and feedback
Close browser
>
>
>
Browser configuration
13
• Context A: Thunderbird Mail manager
• Context B: Microsoft Edge configuration menu
Motivation Baseline Experiment Replication Study Data Analysis Study Results
Mensaje
New Message
Reply
Forward
Forward As
Reply to All
Edit As New Message
Open Message
Open in Conversation
Attachements
Tag
Mark
Archive
Move to
Copy to
Move Again
Create Filter From Message…
Ignore Thread
Ignore Subthread
Watch Thread
>
>
>
>
>
>
Message
Replication Study: Participants
14
• Convenience sampling
• 40 students (5 female, 35 male, and 0 non-binary) enrolled in a Master’s degree in Computer
Science at the UPV
• A common background in computer science
• Aged from 20 to 40 years old (mean = 24)
Goal
Experimental
objects
Variables Hypotheses
Participants Study design Instrumentation
Motivation Baseline Experiment Replication Study Data Analysis Study Results
Replication Study: Variables
15
• Independent Variables:
• (20) Menu types
• Dependent Variables (Emotion-based):
• Attraction: positive/pleasant reaction to a negative/unpleasant reaction response to a situation
• Memorisation: intensity of cognitive processes related to the formation of future memories during an
experience.
Goal
Experimental
objects
Variables Hypotheses
Participants Study design Instrumentation
Motivation Baseline Experiment Replication Study Data Analysis Study Results
• Cognitive load: The use of cognitive resources to carry
out a task or visualize a stimulus
• Engagement: degree of involvement or connection
between the participant and the stimulus or task
Replication Study: Variables
16
• Performance-based Variables (Dependent):
• Completion-Time: Measures in seconds the time elapsed between the initial state of the task and the final
state, when the task is correctly completed
Goal
Experimental
objects
Variables Hypotheses
Participants Study design Instrumentation
Motivation Baseline Experiment Replication Study Data Analysis Study Results
• Perception-based Variables (Dependent):
• Perceived Attraction: Subjective perception of attraction in response to
stimuli or a situation
• Perceived Cognitive Load: Subjective perception of cognitive load or
mental effort required during the task
Replication Study: Hypotheses
17
• 𝐻𝑛11: There are no significant differences in the users’ attraction when using different graphical adaptive menus
• 𝐻𝑛12: There are no significant differences in the users’ memorisation when using different graphical adaptive menus
• 𝐻𝑛13: There are no significant differences in the users’ cognitive load when using different graphical adaptive menus
• 𝐻𝑛14: There are no significant differences in the users’ engagement when using different graphical adaptive menus
• 𝐻𝑛15: There are no significant differences in the users’ completion-time when using different graphical adaptive menus
• 𝐻𝑛21: There is no correlation between the Cognitive load (EEG) and the Perceived Cognitive Load (Questionnaire)
• 𝐻𝑛22: There is no correlation between the Attraction (EEG) and the Perceived Attraction (Questionnaire)
Goal
Experimental
objects
Variables Hypotheses
Participants Study design Instrumentation
Motivation Baseline Experiment Replication Study Data Analysis Study Results
Replication Study: Study Design
18
• Within Subjects design: Each participant used the 20 different menu types
• Each menu type was randomly assigned to a specific domain (Thunderbird’s mail manager or Web Browser settings)
• Each participant used the menus in a different order (also randomly assigned)
Subject 1 Menu_1 Menu_2 Menu_19 Menu_20
…
Subject 2 Menu_8 Menu_5 Menu_2 Menu_1
…
Subject 3 Menu_11 Menu_13 Menu_8 Menu_6
…
Subject n Menu_18 Menu_2 Menu_13 Menu_19
…
… … … … … …
Goal
Experimental
objects
Variables Hypotheses
Participants Study design Instrumentation
Motivation Baseline Experiment Replication Study Data Analysis Study Results
Replication Study: Instrumentation
19
• Interactive PowerPoint presentation representing the 20 different menu types
• EEG Headset to obtain brain activity
• UEQ-S and NASA-TLX questionnaires
Sensors are placed at
positions AF7, Fp1, Fp2, AF8,
F3, F4, P3, P4, PO7, O1, O2
and PO8 (10-10 standard)
Goal
Experimental
objects
Variables Hypotheses
Participants Study design Instrumentation
Motivation Baseline Experiment Replication Study Data Analysis Study Results
Replication Study: Instrumentation
20
Data obtained: Raw EEG Data. Brain activity
Goal
Experimental
objects
Variables Hypotheses
Participants Study design Instrumentation
Motivation Baseline Experiment Replication Study Data Analysis Study Results
Replication Study: Instrumentation
21
Attraction
Memorization
Cognitive load
Engagement
Raw EEG Data
Delta, 0-4 Hz Theta, 4-8 Hz
Alpha, 8-13 Hz Beta, 13-30 Hz
Goal
Experimental
objects
Variables Hypotheses
Participants Study design Instrumentation
Motivation Baseline Experiment Replication Study Data Analysis Study Results
Replication Study: Instrumentation
22
Subject n Menu_A Menu_B Menu_C Menu_D
…
Goal
Experimental
objects
Variables Hypotheses
Participants Study design Instrumentation
• Completion time: the time is measured from the beginning to the end of the interaction for each menu.
Motivation Baseline Experiment Replication Study Data Analysis Study Results
Replication Study: Instrumentation
23
Goal
Experimental
objects
Variables Hypotheses
Participants Study design Instrumentation
Motivation Baseline Experiment Replication Study Data Analysis Study Results
UEQ-Short
Nasa-TLX
Perceived Attraction: UEQ-Short Evaluation
Perceived Cognitive Load: NASA-TLX evaluation
Overview
Motivation
Baseline Experiment
Replication Study
Data Analysis
Study Results
24
Motivation
Baseline Experiment
Replication Study
Data Analysis
Study Results
Data Analysis
• Mean distance of the Dynamic Time Warping (DTW) for
each participant (40), menu (20), and EEG metric (4)
1. Group the time series by menu and metric
2. Compute the distance matrix
3. Calculate the mean distance for each
participant/menu
25
Motivation Baseline Experiment Replication Study Data Analysis Study Results
Cognitive load – Distance matrix for Blinking menu
Cognitive load per
participant - Blinking menu
1
2
3
40
D=3
D=10
Participant ID
Participant ID
Participant 3
Participant 2
Participant 1
Participant 2
Data Analysis
1. Descriptive Study of dependent variables
2. Kruskal-Wallis or ANOVA (hypotheses testing)
3. Pearson or Spearman (correlation)
4. Cliff′s δ (Statistical significances)
26
Motivation Baseline Experiment Replication Study Data Analysis Study Results
Overview
Motivation
Baseline Experiment
Replication Study
Data Analysis
Study Results
27
Motivation
Baseline Experiment
Replication Study
Data Analysis
Study Results
Study Results: Descriptive (emotion-based variables)
28
Motivation Baseline Experiment Replication Study Data Analysis Study Results
Study Results: Descriptive (emotion-based variables)
29
Motivation Baseline Experiment Replication Study Data Analysis Study Results
MenuItem1
MenuItem2
MenuItem3
MenuItem4
MenuItem5
MenuItem6
MenuItem7
ItemA
Out of context
Temporal
Coloured
Study Results: Descriptive (emotion-based variables)
30
Motivation Baseline Experiment Replication Study Data Analysis Study Results
MenuItem2 MenuItem4
MenuItem3
MenuItem6
MenuItem1
MenuItem8
MenuItem7
MenuItem5 ItemA
MenuItem1
MenuItem2
MenuItem3
MenuItem4
MenuItem5
MenuItem6
MenuItem7
ItemA
MenuItem1
MenuItem2
MenuItem3
MenuItem4
MenuItem5
MenuItem6
MenuItem7
ItemA
Italicizing
Underlying Leaf
Study Results: Descriptive (emotion-based variables)
31
Motivation Baseline Experiment Replication Study Data Analysis Study Results
MenuItem2 MenuItem4
MenuItem3
MenuItem6
MenuItem1
MenuItem8
MenuItem7
MenuItem5 ItemA
MenuItem1
MenuItem2
MenuItem3
MenuItem4
MenuItem5
MenuItem6
MenuItem7
ItemA
MenuItem1
MenuItem2
MenuItem3
MenuItem4
MenuItem5
MenuItem6
MenuItem7
ItemA
Italicizing
Underlying Leaf
MenuItem1
MenuItem2
MenuItem3
MenuItem4
MenuItem5
MenuItem6
MenuItem7
ItemA
Out of context
Temporal
Coloured
Study Results: Descriptive (perception-based variables)
32
Menu
Attraction
(EEG)
[-100, 100]
Attraction
(Perceived)
[-3,3]
Cognitive Load
(EEG)
[0, 100]
Cognitive Load
(Perceived)
[0, 100]
Blinking 11,336 1,306 29.420 29.983
Split without
replication
0.755 0.281 30.672 33.758
Out of Context
disapp
-1.159 -0.725 30.189 32.275
Morphing 4.225 -0.018 35.321 41.758
Italizing -1.002 -1.037 33.351 43.816
Leaf 3.331 1.020 35.444 66.933
Motivation Baseline Experiment Replication Study Data Analysis Study Results
MenuItem2 MenuItem4
MenuItem3
MenuItem6
MenuItem1
MenuItem8
MenuItem7
MenuItem5 ItemA
MenuItem1
MenuItem2
MenuItem3
MenuItem4
MenuItem5
MenuItem6
MenuItem7
ItemA
MenuItem3
MenuItem6
MenuItem1
MenuItem2
MenuItem4
MenuItem5
MenuItem7
ItemA
MenuItem1
MenuItem2
MenuItem3
MenuItem4
MenuItem5
MenuItem6
MenuItem7
ItemA
Study Results: Hypotheses testing
33
Hypotheses Method p-value Menu Difference Cliff’s Delta Effect Size
𝑯𝒏𝟏𝟏 Kruskal-Wallis .00 YES 0,302 Medium
𝑯𝒏𝟏𝟐 Kruskal-Wallis .00 YES 0,667 Large
𝑯𝒏𝟏𝟑 Kruskal-Wallis .00 YES 0,585 Large
𝑯𝒏𝟏𝟒 Kruskal-Wallis .00 YES 0,277 Medium
𝑯𝒏𝟏𝟓 Kruskal-Wallis .00 YES 0,575 Large
Hypotheses Normal Distr. Method Correlation p-value
𝑯𝒏𝟐𝟏 YES Pearson 0.819 .045
𝑯𝒏𝟐𝟐 YES Pearson 0.817 .046
Motivation Baseline Experiment Replication Study Data Analysis Study Results
Conclusions
• Baseline experiment results are confirmed with a more homogeneous group of users
• Effect size shows that these results could be generalised to other contexts
• Objective assessment of UX (cognitive load and attraction) through EEG signals
• Trends:
• Different font styles or structures  uncertainty on UX
• Different colour and temporal variance  predictable user reaction
• Menus that are preferred by the users do not necessarily perform well
• Future work:
• Experiments with more UI elements
• Provide recommendations for UI developers on the dimensions more relevant for
UX
• How to adapt UI to better fit with user needs in different contexts
34
Motivation Baseline Experiment Replication Study Data Analysis Study Results
Measuring the User Experience of Adaptive User
Interfaces using EEG: A Replication Study
Thanks for your attention
Daniel Gaspar-Figueiredo <dagasfi@epsa.upv.es>
Jean Vanderdonckt <jean.vanderdonckt@uclouvain.be>
Silvia Abrahao <sabrahao@dsic.upv.es>
Emilio Insfran <einsfran@dsic.upv.es>
EASE2023
Study Results: Descriptive (performance-based variables)
38
Motivation Baseline Experiment Replication Study Data Analysis Study Results

More Related Content

Similar to EASE Replication EEG experiment.pptx

research methodogy
research methodogyresearch methodogy
research methodogy
narman1402
 
Mixed-initiative recommender systems: towards a next generation of recommende...
Mixed-initiative recommender systems: towards a next generation of recommende...Mixed-initiative recommender systems: towards a next generation of recommende...
Mixed-initiative recommender systems: towards a next generation of recommende...
Katrien Verbert
 
Design and Application of Experiments and User Studies
Design and Application of Experiments and User StudiesDesign and Application of Experiments and User Studies
Design and Application of Experiments and User Studies
Victor Adriel Oliveira
 
Interactive Recommender Systems
Interactive Recommender SystemsInteractive Recommender Systems
Interactive Recommender Systems
Katrien Verbert
 
MELJUN CORTES research seminar_1_formulating_a_research_problem
MELJUN CORTES research seminar_1_formulating_a_research_problemMELJUN CORTES research seminar_1_formulating_a_research_problem
MELJUN CORTES research seminar_1_formulating_a_research_problem
MELJUN CORTES
 
Empirical user studies in Semantic Web contexts
Empirical user studies in Semantic Web contextsEmpirical user studies in Semantic Web contexts
Empirical user studies in Semantic Web contexts
Catia Pesquita
 
2015-11-11 research seminar
2015-11-11 research seminar2015-11-11 research seminar
2015-11-11 research seminar
ifi8106tlu
 
Summary Of An Evaluation Study Of Educational Technology 1
Summary Of An Evaluation Study Of Educational Technology 1Summary Of An Evaluation Study Of Educational Technology 1
Summary Of An Evaluation Study Of Educational Technology 1u068701
 
UX Design Process | Sample Proposal
UX Design Process | Sample Proposal UX Design Process | Sample Proposal
UX Design Process | Sample Proposal
Marta Fioni
 
Evaluating Educational Technology Project Using Actions Model
Evaluating Educational Technology Project Using Actions ModelEvaluating Educational Technology Project Using Actions Model
Evaluating Educational Technology Project Using Actions Model
athra29
 
Interactive Recommender Systems
Interactive Recommender SystemsInteractive Recommender Systems
Interactive Recommender Systems
Katrien Verbert
 
CoMo Game Dev - usability and user experience methods
CoMo Game Dev - usability and user experience methods CoMo Game Dev - usability and user experience methods
CoMo Game Dev - usability and user experience methods
Isa Jahnke
 
Tests
TestsTests
An Independent Study Comparing SPSS to Intellectus Statistics: Preliminary ...
 An Independent Study Comparing SPSS to  Intellectus Statistics: Preliminary ... An Independent Study Comparing SPSS to  Intellectus Statistics: Preliminary ...
An Independent Study Comparing SPSS to Intellectus Statistics: Preliminary ...
Statistics Solutions
 
A brief overview of ux research
A brief overview of ux researchA brief overview of ux research
A brief overview of ux research
Panagiotis Zaharias
 
Explainable AI for non-expert users
Explainable AI for non-expert usersExplainable AI for non-expert users
Explainable AI for non-expert users
Katrien Verbert
 
Benoit Visual Only Retrieval
Benoit Visual Only RetrievalBenoit Visual Only Retrieval
Collaborative learning presentation
Collaborative learning presentationCollaborative learning presentation
Collaborative learning presentation
ShiKia Carter
 
master_thesis.pdf
master_thesis.pdfmaster_thesis.pdf
master_thesis.pdf
EL MAJJODI Ayoub
 
Usability of Online Instruction
Usability of Online InstructionUsability of Online Instruction
Usability of Online Instruction
Michael Wilder
 

Similar to EASE Replication EEG experiment.pptx (20)

research methodogy
research methodogyresearch methodogy
research methodogy
 
Mixed-initiative recommender systems: towards a next generation of recommende...
Mixed-initiative recommender systems: towards a next generation of recommende...Mixed-initiative recommender systems: towards a next generation of recommende...
Mixed-initiative recommender systems: towards a next generation of recommende...
 
Design and Application of Experiments and User Studies
Design and Application of Experiments and User StudiesDesign and Application of Experiments and User Studies
Design and Application of Experiments and User Studies
 
Interactive Recommender Systems
Interactive Recommender SystemsInteractive Recommender Systems
Interactive Recommender Systems
 
MELJUN CORTES research seminar_1_formulating_a_research_problem
MELJUN CORTES research seminar_1_formulating_a_research_problemMELJUN CORTES research seminar_1_formulating_a_research_problem
MELJUN CORTES research seminar_1_formulating_a_research_problem
 
Empirical user studies in Semantic Web contexts
Empirical user studies in Semantic Web contextsEmpirical user studies in Semantic Web contexts
Empirical user studies in Semantic Web contexts
 
2015-11-11 research seminar
2015-11-11 research seminar2015-11-11 research seminar
2015-11-11 research seminar
 
Summary Of An Evaluation Study Of Educational Technology 1
Summary Of An Evaluation Study Of Educational Technology 1Summary Of An Evaluation Study Of Educational Technology 1
Summary Of An Evaluation Study Of Educational Technology 1
 
UX Design Process | Sample Proposal
UX Design Process | Sample Proposal UX Design Process | Sample Proposal
UX Design Process | Sample Proposal
 
Evaluating Educational Technology Project Using Actions Model
Evaluating Educational Technology Project Using Actions ModelEvaluating Educational Technology Project Using Actions Model
Evaluating Educational Technology Project Using Actions Model
 
Interactive Recommender Systems
Interactive Recommender SystemsInteractive Recommender Systems
Interactive Recommender Systems
 
CoMo Game Dev - usability and user experience methods
CoMo Game Dev - usability and user experience methods CoMo Game Dev - usability and user experience methods
CoMo Game Dev - usability and user experience methods
 
Tests
TestsTests
Tests
 
An Independent Study Comparing SPSS to Intellectus Statistics: Preliminary ...
 An Independent Study Comparing SPSS to  Intellectus Statistics: Preliminary ... An Independent Study Comparing SPSS to  Intellectus Statistics: Preliminary ...
An Independent Study Comparing SPSS to Intellectus Statistics: Preliminary ...
 
A brief overview of ux research
A brief overview of ux researchA brief overview of ux research
A brief overview of ux research
 
Explainable AI for non-expert users
Explainable AI for non-expert usersExplainable AI for non-expert users
Explainable AI for non-expert users
 
Benoit Visual Only Retrieval
Benoit Visual Only RetrievalBenoit Visual Only Retrieval
Benoit Visual Only Retrieval
 
Collaborative learning presentation
Collaborative learning presentationCollaborative learning presentation
Collaborative learning presentation
 
master_thesis.pdf
master_thesis.pdfmaster_thesis.pdf
master_thesis.pdf
 
Usability of Online Instruction
Usability of Online InstructionUsability of Online Instruction
Usability of Online Instruction
 

Recently uploaded

Design and Analysis of Algorithms-DP,Backtracking,Graphs,B&B
Design and Analysis of Algorithms-DP,Backtracking,Graphs,B&BDesign and Analysis of Algorithms-DP,Backtracking,Graphs,B&B
Design and Analysis of Algorithms-DP,Backtracking,Graphs,B&B
Sreedhar Chowdam
 
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)
MdTanvirMahtab2
 
Student information management system project report ii.pdf
Student information management system project report ii.pdfStudent information management system project report ii.pdf
Student information management system project report ii.pdf
Kamal Acharya
 
ethical hacking-mobile hacking methods.ppt
ethical hacking-mobile hacking methods.pptethical hacking-mobile hacking methods.ppt
ethical hacking-mobile hacking methods.ppt
Jayaprasanna4
 
AKS UNIVERSITY Satna Final Year Project By OM Hardaha.pdf
AKS UNIVERSITY Satna Final Year Project By OM Hardaha.pdfAKS UNIVERSITY Satna Final Year Project By OM Hardaha.pdf
AKS UNIVERSITY Satna Final Year Project By OM Hardaha.pdf
SamSarthak3
 
H.Seo, ICLR 2024, MLILAB, KAIST AI.pdf
H.Seo,  ICLR 2024, MLILAB,  KAIST AI.pdfH.Seo,  ICLR 2024, MLILAB,  KAIST AI.pdf
H.Seo, ICLR 2024, MLILAB, KAIST AI.pdf
MLILAB
 
Water Industry Process Automation and Control Monthly - May 2024.pdf
Water Industry Process Automation and Control Monthly - May 2024.pdfWater Industry Process Automation and Control Monthly - May 2024.pdf
Water Industry Process Automation and Control Monthly - May 2024.pdf
Water Industry Process Automation & Control
 
ASME IX(9) 2007 Full Version .pdf
ASME IX(9)  2007 Full Version       .pdfASME IX(9)  2007 Full Version       .pdf
ASME IX(9) 2007 Full Version .pdf
AhmedHussein950959
 
RAT: Retrieval Augmented Thoughts Elicit Context-Aware Reasoning in Long-Hori...
RAT: Retrieval Augmented Thoughts Elicit Context-Aware Reasoning in Long-Hori...RAT: Retrieval Augmented Thoughts Elicit Context-Aware Reasoning in Long-Hori...
RAT: Retrieval Augmented Thoughts Elicit Context-Aware Reasoning in Long-Hori...
thanhdowork
 
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptx
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptxCFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptx
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptx
R&R Consult
 
Architectural Portfolio Sean Lockwood
Architectural Portfolio Sean LockwoodArchitectural Portfolio Sean Lockwood
Architectural Portfolio Sean Lockwood
seandesed
 
MCQ Soil mechanics questions (Soil shear strength).pdf
MCQ Soil mechanics questions (Soil shear strength).pdfMCQ Soil mechanics questions (Soil shear strength).pdf
MCQ Soil mechanics questions (Soil shear strength).pdf
Osamah Alsalih
 
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...
Dr.Costas Sachpazis
 
HYDROPOWER - Hydroelectric power generation
HYDROPOWER - Hydroelectric power generationHYDROPOWER - Hydroelectric power generation
HYDROPOWER - Hydroelectric power generation
Robbie Edward Sayers
 
Nuclear Power Economics and Structuring 2024
Nuclear Power Economics and Structuring 2024Nuclear Power Economics and Structuring 2024
Nuclear Power Economics and Structuring 2024
Massimo Talia
 
Gen AI Study Jams _ For the GDSC Leads in India.pdf
Gen AI Study Jams _ For the GDSC Leads in India.pdfGen AI Study Jams _ For the GDSC Leads in India.pdf
Gen AI Study Jams _ For the GDSC Leads in India.pdf
gdsczhcet
 
Railway Signalling Principles Edition 3.pdf
Railway Signalling Principles Edition 3.pdfRailway Signalling Principles Edition 3.pdf
Railway Signalling Principles Edition 3.pdf
TeeVichai
 
Runway Orientation Based on the Wind Rose Diagram.pptx
Runway Orientation Based on the Wind Rose Diagram.pptxRunway Orientation Based on the Wind Rose Diagram.pptx
Runway Orientation Based on the Wind Rose Diagram.pptx
SupreethSP4
 
The Benefits and Techniques of Trenchless Pipe Repair.pdf
The Benefits and Techniques of Trenchless Pipe Repair.pdfThe Benefits and Techniques of Trenchless Pipe Repair.pdf
The Benefits and Techniques of Trenchless Pipe Repair.pdf
Pipe Restoration Solutions
 
Immunizing Image Classifiers Against Localized Adversary Attacks
Immunizing Image Classifiers Against Localized Adversary AttacksImmunizing Image Classifiers Against Localized Adversary Attacks
Immunizing Image Classifiers Against Localized Adversary Attacks
gerogepatton
 

Recently uploaded (20)

Design and Analysis of Algorithms-DP,Backtracking,Graphs,B&B
Design and Analysis of Algorithms-DP,Backtracking,Graphs,B&BDesign and Analysis of Algorithms-DP,Backtracking,Graphs,B&B
Design and Analysis of Algorithms-DP,Backtracking,Graphs,B&B
 
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)
 
Student information management system project report ii.pdf
Student information management system project report ii.pdfStudent information management system project report ii.pdf
Student information management system project report ii.pdf
 
ethical hacking-mobile hacking methods.ppt
ethical hacking-mobile hacking methods.pptethical hacking-mobile hacking methods.ppt
ethical hacking-mobile hacking methods.ppt
 
AKS UNIVERSITY Satna Final Year Project By OM Hardaha.pdf
AKS UNIVERSITY Satna Final Year Project By OM Hardaha.pdfAKS UNIVERSITY Satna Final Year Project By OM Hardaha.pdf
AKS UNIVERSITY Satna Final Year Project By OM Hardaha.pdf
 
H.Seo, ICLR 2024, MLILAB, KAIST AI.pdf
H.Seo,  ICLR 2024, MLILAB,  KAIST AI.pdfH.Seo,  ICLR 2024, MLILAB,  KAIST AI.pdf
H.Seo, ICLR 2024, MLILAB, KAIST AI.pdf
 
Water Industry Process Automation and Control Monthly - May 2024.pdf
Water Industry Process Automation and Control Monthly - May 2024.pdfWater Industry Process Automation and Control Monthly - May 2024.pdf
Water Industry Process Automation and Control Monthly - May 2024.pdf
 
ASME IX(9) 2007 Full Version .pdf
ASME IX(9)  2007 Full Version       .pdfASME IX(9)  2007 Full Version       .pdf
ASME IX(9) 2007 Full Version .pdf
 
RAT: Retrieval Augmented Thoughts Elicit Context-Aware Reasoning in Long-Hori...
RAT: Retrieval Augmented Thoughts Elicit Context-Aware Reasoning in Long-Hori...RAT: Retrieval Augmented Thoughts Elicit Context-Aware Reasoning in Long-Hori...
RAT: Retrieval Augmented Thoughts Elicit Context-Aware Reasoning in Long-Hori...
 
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptx
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptxCFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptx
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptx
 
Architectural Portfolio Sean Lockwood
Architectural Portfolio Sean LockwoodArchitectural Portfolio Sean Lockwood
Architectural Portfolio Sean Lockwood
 
MCQ Soil mechanics questions (Soil shear strength).pdf
MCQ Soil mechanics questions (Soil shear strength).pdfMCQ Soil mechanics questions (Soil shear strength).pdf
MCQ Soil mechanics questions (Soil shear strength).pdf
 
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...
 
HYDROPOWER - Hydroelectric power generation
HYDROPOWER - Hydroelectric power generationHYDROPOWER - Hydroelectric power generation
HYDROPOWER - Hydroelectric power generation
 
Nuclear Power Economics and Structuring 2024
Nuclear Power Economics and Structuring 2024Nuclear Power Economics and Structuring 2024
Nuclear Power Economics and Structuring 2024
 
Gen AI Study Jams _ For the GDSC Leads in India.pdf
Gen AI Study Jams _ For the GDSC Leads in India.pdfGen AI Study Jams _ For the GDSC Leads in India.pdf
Gen AI Study Jams _ For the GDSC Leads in India.pdf
 
Railway Signalling Principles Edition 3.pdf
Railway Signalling Principles Edition 3.pdfRailway Signalling Principles Edition 3.pdf
Railway Signalling Principles Edition 3.pdf
 
Runway Orientation Based on the Wind Rose Diagram.pptx
Runway Orientation Based on the Wind Rose Diagram.pptxRunway Orientation Based on the Wind Rose Diagram.pptx
Runway Orientation Based on the Wind Rose Diagram.pptx
 
The Benefits and Techniques of Trenchless Pipe Repair.pdf
The Benefits and Techniques of Trenchless Pipe Repair.pdfThe Benefits and Techniques of Trenchless Pipe Repair.pdf
The Benefits and Techniques of Trenchless Pipe Repair.pdf
 
Immunizing Image Classifiers Against Localized Adversary Attacks
Immunizing Image Classifiers Against Localized Adversary AttacksImmunizing Image Classifiers Against Localized Adversary Attacks
Immunizing Image Classifiers Against Localized Adversary Attacks
 

EASE Replication EEG experiment.pptx

  • 1. Measuring the User Experience of Adaptive User Interfaces using EEG: A Replication Study Daniel Gaspar-Figueiredo <dagasfi@epsa.upv.es> Jean Vanderdonckt <jean.vanderdonckt@uclouvain.be> Silvia Abrahao <sabrahao@dsic.upv.es> Emilio Insfran <einsfran@dsic.upv.es> EASE2023
  • 2. Overview Motivation Baseline Experiment Replication Study Data Analysis Study Results 2 Motivation Baseline Experiment Replication Study Data Analysis Study Results
  • 3. • Adaptive User Interfaces (AUI) – UI that can be changed considering the Context of use to enhance the User eXperience (UX) • Type of changes: • Layout • Colours • Modalities • Element interactions • Element styling • … Motivation 3 … User Interface Context of use User Platform Environment • Too wide spectrum of possibilities Motivation Baseline Experiment Replication Study Data Analysis Study Results
  • 4. Motivation 4 Different UI designs produce different UX Measure UX through physiological data such as brain activity (EEG) Motivation Baseline Experiment Replication Study Data Analysis Study Results
  • 5. Overview Motivation Baseline Experiment Replication Study Data Analysis Study Results 5 Motivation Baseline Experiment Replication Study Data Analysis Study Results
  • 6. Baseline Experiment: Summary 6 • Analyse a set of adaptive graphical menus for the purpose of comparing them with respect to their UX produced in terms of cognitive load, engagement, memorisation, and attraction from the point of view of both researchers and user interface designer in the context of end-users of applications with graphical menus • 20 graphical adaptive menus were compared (mail manager and web browser applications) • 40 participants aged from 18 to 63 years old (mean 42) • Cognitive load, engagement, memorisation, and attraction were computed using EEG analysis • RQ: Do graphical adaptive menus have a different influence on the UX? Motivation Baseline Experiment Replication Study Data Analysis Study Results
  • 7. Baseline Experiment: Summary 7 Statistically significant differences were found between the menus regarding the Cognitive load, engagement, memorisation, and attraction May be due to diversity of users • Multiple backgrounds • Very wide range of ages Motivation Baseline Experiment Replication Study Data Analysis Study Results
  • 8. Overview Motivation Baseline Experiment Replication Study Data Analysis Study Results 8 Motivation Baseline Experiment Replication Study Data Analysis Study Results
  • 9. Replication Study: Goal 9 • Analyse a set of adaptive graphical menus • For the purpose of comparing them • with respect to their user experience produced in terms of cognitive load, engagement, memorisation, and attraction • From the point of view of both researchers and user interface designers • In the context of and end-users' group with a background in computer science RQ1: Do the 20 graphical adaptive menus have a different influence on the UX? RQ2: Does the UX measured using EEG signals correlate with the subjective ratings obtained using traditional questionnaires? Goal Experimental objects Variables Hypotheses Participants Study design Instrumentation Motivation Baseline Experiment Replication Study Data Analysis Study Results
  • 10. Replication Study: Experimental objects 10 Vanderdonckt, J., Bouzit, S., Calvary, G., & Chêne, D. (2019). Exploring a design space of graphical adaptive menus: normal vs. small screens. ACM Transactions on Interactive Intelligent Systems (TiiS), 10(1), 1-40. Design space for graphical adaptive menus (8 variables): • Position • Orientation • Size • Shape • Value • Color • Texture • Motion Goal Experimental objects Variables Hypotheses Participants Study design Instrumentation Motivation Baseline Experiment Replication Study Data Analysis Study Results
  • 11. • We selected 20 different graphical adaptive menus from  Replication Study: Experimental objects 11 Vanderdonckt, J., Bouzit, S., Calvary, G., & Chêne, D. (2019). Exploring a design space of graphical adaptive menus: normal vs. small screens. ACM Transactions on Interactive Intelligent Systems (TiiS), 10(1), 1-40. Goal Experimental objects Variables Hypotheses Participants Study design Instrumentation Motivation Baseline Experiment Replication Study Data Analysis Study Results ItemA MenuItem1 MenuItem2 MenuItem3 MenuItem4 MenuItem5 MenuItem6 MenuItem7 MenuItem1 MenuItem2 MenuItem3 MenuItem4 MenuItem5 MenuItem6 MenuItem7 ItemA MenuItem1 MenuItem2 MenuItem3 MenuItem4 MenuItem5 MenuItem6 MenuItem7 ItemA ItemA MenuItem1 MenuItem2 MenuItem3 MenuItem4 MenuItem5 MenuItem6 MenuItem7 MenuItem1 MenuItem2 MenuItem3 MenuItem4 MenuItem5 MenuItem6 MenuItem7 ItemA MenuItem1 MenuItem2 MenuItem3 MenuItem4 MenuItem5 MenuItem6 MenuItem7 ItemA MenuItem1 MenuItem2 MenuItem3 MenuItem4 MenuItem5 MenuItem6 MenuItem7 ItemA MenuItem3 MenuItem6 MenuItem1 MenuItem2 MenuItem4 MenuItem5 MenuItem7 ItemA Static menu Bolding menu Underlying menu Glowing menu Adaptive activation- area menu Boxing menu Morphing menu Ephemeral menu Highlighting menu Split menu without replication Greyscaling menu MenuItem1 MenuItem2 MenuItem3 MenuItem4 MenuItem5 MenuItem6 MenuItem7 ItemA
  • 12. Replication Study: Experimental objects 12 MenuItem2 MenuItem4 MenuItem3 MenuItem6 MenuItem1 MenuItem8 MenuItem7 MenuItem5 ItemA Leaf menu MenuItem1 MenuItem2 MenuItem3 MenuItem4 MenuItem5 MenuItem6 MenuItem7 ItemA Italicizing menu Blinking menu Pulsing menu Out-of-context menu Temporal menu Pink menu MenuItem1 MenuItem2 MenuItem3 MenuItem4 MenuItem5 MenuItem6 MenuItem7 ItemA Twisting menu Rotating menu • We represented these menus in two different contexts Goal Experimental objects Variables Hypotheses Participants Study design Instrumentation Motivation Baseline Experiment Replication Study Data Analysis Study Results
  • 13. Goal Experimental objects Variables Hypotheses Participants Study design Instrumentation Replication Study: Experimental objects New tab New window Zoom Favorites New InPrivate window History Downloads Apps Extensions Collections Print Web capture Share Find on page Read aloud More tools Settings Help and feedback Close browser > > > Browser configuration 13 • Context A: Thunderbird Mail manager • Context B: Microsoft Edge configuration menu Motivation Baseline Experiment Replication Study Data Analysis Study Results Mensaje New Message Reply Forward Forward As Reply to All Edit As New Message Open Message Open in Conversation Attachements Tag Mark Archive Move to Copy to Move Again Create Filter From Message… Ignore Thread Ignore Subthread Watch Thread > > > > > > Message
  • 14. Replication Study: Participants 14 • Convenience sampling • 40 students (5 female, 35 male, and 0 non-binary) enrolled in a Master’s degree in Computer Science at the UPV • A common background in computer science • Aged from 20 to 40 years old (mean = 24) Goal Experimental objects Variables Hypotheses Participants Study design Instrumentation Motivation Baseline Experiment Replication Study Data Analysis Study Results
  • 15. Replication Study: Variables 15 • Independent Variables: • (20) Menu types • Dependent Variables (Emotion-based): • Attraction: positive/pleasant reaction to a negative/unpleasant reaction response to a situation • Memorisation: intensity of cognitive processes related to the formation of future memories during an experience. Goal Experimental objects Variables Hypotheses Participants Study design Instrumentation Motivation Baseline Experiment Replication Study Data Analysis Study Results • Cognitive load: The use of cognitive resources to carry out a task or visualize a stimulus • Engagement: degree of involvement or connection between the participant and the stimulus or task
  • 16. Replication Study: Variables 16 • Performance-based Variables (Dependent): • Completion-Time: Measures in seconds the time elapsed between the initial state of the task and the final state, when the task is correctly completed Goal Experimental objects Variables Hypotheses Participants Study design Instrumentation Motivation Baseline Experiment Replication Study Data Analysis Study Results • Perception-based Variables (Dependent): • Perceived Attraction: Subjective perception of attraction in response to stimuli or a situation • Perceived Cognitive Load: Subjective perception of cognitive load or mental effort required during the task
  • 17. Replication Study: Hypotheses 17 • 𝐻𝑛11: There are no significant differences in the users’ attraction when using different graphical adaptive menus • 𝐻𝑛12: There are no significant differences in the users’ memorisation when using different graphical adaptive menus • 𝐻𝑛13: There are no significant differences in the users’ cognitive load when using different graphical adaptive menus • 𝐻𝑛14: There are no significant differences in the users’ engagement when using different graphical adaptive menus • 𝐻𝑛15: There are no significant differences in the users’ completion-time when using different graphical adaptive menus • 𝐻𝑛21: There is no correlation between the Cognitive load (EEG) and the Perceived Cognitive Load (Questionnaire) • 𝐻𝑛22: There is no correlation between the Attraction (EEG) and the Perceived Attraction (Questionnaire) Goal Experimental objects Variables Hypotheses Participants Study design Instrumentation Motivation Baseline Experiment Replication Study Data Analysis Study Results
  • 18. Replication Study: Study Design 18 • Within Subjects design: Each participant used the 20 different menu types • Each menu type was randomly assigned to a specific domain (Thunderbird’s mail manager or Web Browser settings) • Each participant used the menus in a different order (also randomly assigned) Subject 1 Menu_1 Menu_2 Menu_19 Menu_20 … Subject 2 Menu_8 Menu_5 Menu_2 Menu_1 … Subject 3 Menu_11 Menu_13 Menu_8 Menu_6 … Subject n Menu_18 Menu_2 Menu_13 Menu_19 … … … … … … … Goal Experimental objects Variables Hypotheses Participants Study design Instrumentation Motivation Baseline Experiment Replication Study Data Analysis Study Results
  • 19. Replication Study: Instrumentation 19 • Interactive PowerPoint presentation representing the 20 different menu types • EEG Headset to obtain brain activity • UEQ-S and NASA-TLX questionnaires Sensors are placed at positions AF7, Fp1, Fp2, AF8, F3, F4, P3, P4, PO7, O1, O2 and PO8 (10-10 standard) Goal Experimental objects Variables Hypotheses Participants Study design Instrumentation Motivation Baseline Experiment Replication Study Data Analysis Study Results
  • 20. Replication Study: Instrumentation 20 Data obtained: Raw EEG Data. Brain activity Goal Experimental objects Variables Hypotheses Participants Study design Instrumentation Motivation Baseline Experiment Replication Study Data Analysis Study Results
  • 21. Replication Study: Instrumentation 21 Attraction Memorization Cognitive load Engagement Raw EEG Data Delta, 0-4 Hz Theta, 4-8 Hz Alpha, 8-13 Hz Beta, 13-30 Hz Goal Experimental objects Variables Hypotheses Participants Study design Instrumentation Motivation Baseline Experiment Replication Study Data Analysis Study Results
  • 22. Replication Study: Instrumentation 22 Subject n Menu_A Menu_B Menu_C Menu_D … Goal Experimental objects Variables Hypotheses Participants Study design Instrumentation • Completion time: the time is measured from the beginning to the end of the interaction for each menu. Motivation Baseline Experiment Replication Study Data Analysis Study Results
  • 23. Replication Study: Instrumentation 23 Goal Experimental objects Variables Hypotheses Participants Study design Instrumentation Motivation Baseline Experiment Replication Study Data Analysis Study Results UEQ-Short Nasa-TLX Perceived Attraction: UEQ-Short Evaluation Perceived Cognitive Load: NASA-TLX evaluation
  • 24. Overview Motivation Baseline Experiment Replication Study Data Analysis Study Results 24 Motivation Baseline Experiment Replication Study Data Analysis Study Results
  • 25. Data Analysis • Mean distance of the Dynamic Time Warping (DTW) for each participant (40), menu (20), and EEG metric (4) 1. Group the time series by menu and metric 2. Compute the distance matrix 3. Calculate the mean distance for each participant/menu 25 Motivation Baseline Experiment Replication Study Data Analysis Study Results Cognitive load – Distance matrix for Blinking menu Cognitive load per participant - Blinking menu 1 2 3 40 D=3 D=10 Participant ID Participant ID Participant 3 Participant 2 Participant 1 Participant 2
  • 26. Data Analysis 1. Descriptive Study of dependent variables 2. Kruskal-Wallis or ANOVA (hypotheses testing) 3. Pearson or Spearman (correlation) 4. Cliff′s δ (Statistical significances) 26 Motivation Baseline Experiment Replication Study Data Analysis Study Results
  • 27. Overview Motivation Baseline Experiment Replication Study Data Analysis Study Results 27 Motivation Baseline Experiment Replication Study Data Analysis Study Results
  • 28. Study Results: Descriptive (emotion-based variables) 28 Motivation Baseline Experiment Replication Study Data Analysis Study Results
  • 29. Study Results: Descriptive (emotion-based variables) 29 Motivation Baseline Experiment Replication Study Data Analysis Study Results MenuItem1 MenuItem2 MenuItem3 MenuItem4 MenuItem5 MenuItem6 MenuItem7 ItemA Out of context Temporal Coloured
  • 30. Study Results: Descriptive (emotion-based variables) 30 Motivation Baseline Experiment Replication Study Data Analysis Study Results MenuItem2 MenuItem4 MenuItem3 MenuItem6 MenuItem1 MenuItem8 MenuItem7 MenuItem5 ItemA MenuItem1 MenuItem2 MenuItem3 MenuItem4 MenuItem5 MenuItem6 MenuItem7 ItemA MenuItem1 MenuItem2 MenuItem3 MenuItem4 MenuItem5 MenuItem6 MenuItem7 ItemA Italicizing Underlying Leaf
  • 31. Study Results: Descriptive (emotion-based variables) 31 Motivation Baseline Experiment Replication Study Data Analysis Study Results MenuItem2 MenuItem4 MenuItem3 MenuItem6 MenuItem1 MenuItem8 MenuItem7 MenuItem5 ItemA MenuItem1 MenuItem2 MenuItem3 MenuItem4 MenuItem5 MenuItem6 MenuItem7 ItemA MenuItem1 MenuItem2 MenuItem3 MenuItem4 MenuItem5 MenuItem6 MenuItem7 ItemA Italicizing Underlying Leaf MenuItem1 MenuItem2 MenuItem3 MenuItem4 MenuItem5 MenuItem6 MenuItem7 ItemA Out of context Temporal Coloured
  • 32. Study Results: Descriptive (perception-based variables) 32 Menu Attraction (EEG) [-100, 100] Attraction (Perceived) [-3,3] Cognitive Load (EEG) [0, 100] Cognitive Load (Perceived) [0, 100] Blinking 11,336 1,306 29.420 29.983 Split without replication 0.755 0.281 30.672 33.758 Out of Context disapp -1.159 -0.725 30.189 32.275 Morphing 4.225 -0.018 35.321 41.758 Italizing -1.002 -1.037 33.351 43.816 Leaf 3.331 1.020 35.444 66.933 Motivation Baseline Experiment Replication Study Data Analysis Study Results MenuItem2 MenuItem4 MenuItem3 MenuItem6 MenuItem1 MenuItem8 MenuItem7 MenuItem5 ItemA MenuItem1 MenuItem2 MenuItem3 MenuItem4 MenuItem5 MenuItem6 MenuItem7 ItemA MenuItem3 MenuItem6 MenuItem1 MenuItem2 MenuItem4 MenuItem5 MenuItem7 ItemA MenuItem1 MenuItem2 MenuItem3 MenuItem4 MenuItem5 MenuItem6 MenuItem7 ItemA
  • 33. Study Results: Hypotheses testing 33 Hypotheses Method p-value Menu Difference Cliff’s Delta Effect Size 𝑯𝒏𝟏𝟏 Kruskal-Wallis .00 YES 0,302 Medium 𝑯𝒏𝟏𝟐 Kruskal-Wallis .00 YES 0,667 Large 𝑯𝒏𝟏𝟑 Kruskal-Wallis .00 YES 0,585 Large 𝑯𝒏𝟏𝟒 Kruskal-Wallis .00 YES 0,277 Medium 𝑯𝒏𝟏𝟓 Kruskal-Wallis .00 YES 0,575 Large Hypotheses Normal Distr. Method Correlation p-value 𝑯𝒏𝟐𝟏 YES Pearson 0.819 .045 𝑯𝒏𝟐𝟐 YES Pearson 0.817 .046 Motivation Baseline Experiment Replication Study Data Analysis Study Results
  • 34. Conclusions • Baseline experiment results are confirmed with a more homogeneous group of users • Effect size shows that these results could be generalised to other contexts • Objective assessment of UX (cognitive load and attraction) through EEG signals • Trends: • Different font styles or structures  uncertainty on UX • Different colour and temporal variance  predictable user reaction • Menus that are preferred by the users do not necessarily perform well • Future work: • Experiments with more UI elements • Provide recommendations for UI developers on the dimensions more relevant for UX • How to adapt UI to better fit with user needs in different contexts 34 Motivation Baseline Experiment Replication Study Data Analysis Study Results
  • 35. Measuring the User Experience of Adaptive User Interfaces using EEG: A Replication Study Thanks for your attention Daniel Gaspar-Figueiredo <dagasfi@epsa.upv.es> Jean Vanderdonckt <jean.vanderdonckt@uclouvain.be> Silvia Abrahao <sabrahao@dsic.upv.es> Emilio Insfran <einsfran@dsic.upv.es> EASE2023
  • 36. Study Results: Descriptive (performance-based variables) 38 Motivation Baseline Experiment Replication Study Data Analysis Study Results

Editor's Notes

  1. The catalogue had almost 40 menus, we chose 20 of them…
  2. The catalogue had almost 40 menus, we chose 20 of them…