This document provides an overview of affective computing, which is a field of research that deals with developing systems that can recognize, interpret, process, and simulate human emotions. It discusses several key aspects of affective computing including psychological theories of emotion, classes of facial expressions, components of emotions, models for representing emotions, and tools for measuring brain activity like electroencephalography. The document also covers applications of affective computing and areas of ongoing research like developing wearable devices that can recognize affective patterns and creating robots that can express emotions.

1. EMOTION BASED
COMPUTING
By,
SHILPA MARY GEORGE
Roll no : 81
Reg no : 12120082
Guide : Mrs. SHEENA S

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3. INDEX
1) What is Affective Computing?
2) Objectives
3) Psychological Theories of Emotion
4) Classes of Expressions
5) Components of Emotion
6) A-V-S Emotion Model
7) Electroencephlography (EEG)
1) Principles of EEG
2) Applications
3) Major Components
4) Limitations
8) Conclusion
9) References
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4. WHAT IS AFFECTIVE COMPUTING ?
Affective Computing :
 field of research in AI dealing with emotions and
machines.
 the study and development of systems and devices
that can
* recognize,
* interpret,
* process,
* and simulate human affects.
 an interdisciplinary field spanning computer science,
psychology, and cognitive science. 4

5. OBJECTIVES
 To develop a computing device with its capacity to
gather cues to user emotion from a variety of sources.
-produce “emotion aware machines”.
 Can you quantify Fear? Can you tell whether I am afraid?
 How often have you used Emoticons in chat messages?
Did you feel hampered without them?
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6. PSYCHOLOGICAL THEORIES OF EMOTION
AGGRESIVENESS REMOTE
ANGER DANGER
ANTICIPATION
ANTICIPATION
LOVE
SADNESS
JOY SURPRISE
ACCEPTANCE FEAR
SUBMISSION
AWE
DISSAPPOINTMENT
JOY
ANGER DANGER
SADNESS
ACCEPTANCE FEAR
SURPRISE
OPTIMISM
CONTEMPT
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7. CLASSES OF EXPRESSIONS
 Broadly classified into happy, sad, disgust, fear, anger,
surprise and neutral.
 Goal is to classify an unknown expression into one of
these classes
 Facial expression, posture, gesture, speech, force or
rhythm of key stroke, temperature change of hand on
mouse can signify changes in user’s. emotional state,
detected and interpreted by a computer
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8. COMPONENTS OF EMOTIONS
 Subjective experience (feeling of fear and so on).
 Physiological Changes in Autonomic Nervous
System(ANS) and Endocrine System (Glands and
Hormones released from them).
- e.g. trembling with fear precedes conscious control of
them
 Behavior evoked (such as running away or fainting due
to fear)
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9. [A,V,S] EMOTION MODEL
[Arousal , Valence , Stance] :- A 3-tuple models an
“emotion”.
 Arousal:- Surprise at high arousal, fatigue at low
arousal
-the intensity with which the emotion is experienced
 Valence:- Content at high valence, Unhappiness at
low valence
-the discrimination between positive and negative
experiences
 Stance:- Stern at closed stance, accepting at open
stance
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10. ELECTROENCEPHLOGRAPHY (EEG)
 A medical imaging technique
 A measurement of the electrical activity of the brain
 The recording of the brain’s spontaneous electrical
activity over a short period of time, usually 20-40 mins,
as recoded from multiple electrodes placed on the scalp.
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11. PRINCIPLES OF EEG
 The brain’s electrical charge is maintained by billions of
neurons.
 Neurons pass signals via action potential created by
exchange between sodium & potassium ions in and out of
the cell- Volume conduction
 When the wave of ions reaches the electrodes on the
scalp, they can push or pull electrons on the metal on the
electrodes, the difference in push, or voltage, between
any two electrodes can be measured by a voltmeter which
over time gives us the EEG
 Scalp EEG activity shows oscillations at a variety of
frequencies. Several of these oscillations have
characteristic frequency ranges, spatial distributions and
are associated with different states of brain functioning.
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12. APPLICATIONS
 Monitor alertness, coma and brain death
 Locate areas of damage following head injury, stroke,
coma etc.
 Test afferent pathways (by evoked potentials)
 Monitor cognitive engagement (alpha rhythm)
 Control anaesthesia depth
 Investigate epilepsy and locate seizure origin
 Test epilepsy drug effects
 Monitor human and animal brain development
 Test drugs for conclusive effects
 Investigate sleep disorder and physiology
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13. MAJOR COMPONENTS
 Electrodes with conductive media
 Amplifiers with filters
 A/D converter
 Recording device
• electrodes read signals from head surface
• amplifiers bring microvolt signals to the range where
they can be digitalized accurately
• converter changes signals from analog to digital
• Personal computer stores and displays obtained data
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14. RECORDING ELECTRODES
Types of electrodes :
 Disposable (gel-less, and pre-gelled types)
 Reusable disc electrodes (gold, silver or tin)
 Headbands and electrode caps
 Saline-based electrodes
 Needle electrodes
• Electrode caps are preferred with certain number of
electrodes installed on its surface.
• Needle electrodes are used for long recordings and are
invasively inserted under the scalp.
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15.  Electrode locations and names are specified by the
international 10-20 system
 Label 10-20 designates proportional distance in percents
between ears and nose where points for electrodes are
chosen.
 Electrode placements are labelled according to adjacent
brain areas : F(frontal), C(central), T(temporal),
P(posterior), and O(occipital).
 The letters are accompanied by odd nos at the left side
of the head and with even nos on the right side.
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16. Electrode Cap
Labels for points
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17. LIMITATIONS OF EEG
 Poor spatial resolution
 Most sensitive to a particular set of post synaptic
potentials, those generated in superficial layers of the
cortex, in dendrites and deep structures or producing
currents that are tangential to the skull.
 It is mathematically impossible to construct a unique
Intracranial current source for a given eeg signal as some
currents produce potentials that cancel each other out –
inverse problem.
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18. AREAS OF AFFECTIVE COMPUTING
 AFFECTIVE WEARABLES
Sensors & tools can be used in recognizing affective patterns,
but these tools require a lot of attention/ maintenance.
Figure : Wearer’s Blood Volume
Pressure using
photoplethysmography
Figure : Sample & transmit
biometric data to larger
computer for analysis
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19. AREAS OF AFFECTIVE COMPUTING
 EXPRESSING EMOTION:
Evolution over
the years
Figure : MS Office Assistant Figure : Kismet Robot
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20. KISMET
 an expressive robot at MIT is
equipped with auditory and
proprioceptive (touch) sensory
inputs.
 can express emotion through
*vocalization
* facial expression and
adjustment of Gaze
*direction & head orientation.
 Recognise stimuli
 Realistic
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21. CONCLUSION
Affective Computing is a young field of research
•For interactive systems, something far better than the
current crop of “intelligent” systems are needed.
•Affective Computing has applications in improving the
quality of life in impaired people (successfully
demonstrated for Autism)
•Ethical compromises need to be done to inculcate affective
computers
•This field can really benefit from research into the human
brain/mind.
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22. REFERENCES
1. R.W. Picard (1995), "Affective Computing“,MIT
Media Lab
2. R.W. Picard (1998) , “Towards Agents that
recognize emotions”, Actes Proceedings,
IMAGINA
3. http://www.ai.mit.edu/projects/humanoid-robotics-group/
kismet/kismet.html
4. Automatic Facial Expression Recognition using
Linear and Non-Linear Holistic Spatial Analysis,
Ma and Wang (2005)
5. Emotion and Reinforcement : Affective Facial
Expressions facilitate Robot Learning, Joost
Brokens (2007)
6. Emotion Recognition Based on Brain-Computer
Interface Systems- Taciana Saad Rached and
Angelo Perkusich 22

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24. QUERIES ??
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