Sh ya ma n t a
M.
Ha z a r i k a
,
Ad i t y
Sa i k i a
,
Si ma n t a
Bo r d o l o i
,
Uj j a l
Sh a r ma An d
Na ya n t a r a
Ko t o k y
De p a r t me n t Of Co mp u t e r Sc. & En g i n e e r i n g,
Te z p u r
Un i v e r s i t y
Te z p u r
, I n d i a
s h ya ma n t a @i e e e.o r g
Brain Computer Interface as Sensor
for Ambient Intelligent Living:
A Position Paper
Biomimetic and Cognitive Robotics @ TU
BCR@TU
conducts
research
in
the
area
of
Cognitive
Robotics
and
Knowledge
Representation
&
Reasoning
.
We
are
particularly
interested
in
Qualitative
Spatial
and
Temporal
Reasoning
.
This
translates
into
interest
in
Cognitive
Vision
and
Rehabilitation
Robotics
.
Our
research
within
Cognitive
Robotics
and
KR
&R
is
driven
by
biomimetics
i
.
e
.
,
examination
of
nature
particularly
human
intelligence
and
skills,
its
models,
systems,
processes,
and
elements
to
emulate
or
take
inspiration
from
these
designs
and
processes
.
For
development
of
prostheses
and
assistive
devices
within
Rehabilitation
Robotics
we
undertake
biomimetic
design
,
which
is
NOT
JUST
A
COPY
of
the
geometry!
For
us
biomimetic
design
is
biomimetic
geometry
together
with
functional
biomimesis
.
Brain Computer Interfaces
Brain computer interfaces
–
Use computers to sense human
thoughts and enable the users to
control external devices
–
Infer a user’s intentions using only
brain activity
–
Provide a non
-
muscular avenue for
communication
Applications
–
BCIs are aimed at assisting,
augmenting, or repairing human
cognitive or sensory
-
motor
functions.
e.g. locked
-
in syndrome
(cognitively unimpaired, but no motor
control)
•
Brain Computer Interfaces
•
Depending on application, BCI can be classified
as
•
Cognitive
•
Sensory
•
Motor
•
Motor BMI seeks to translate brain activity
from the central or peripheral nervous system
into useful commands to external devices.
•
Drive Prosthetics
•
Functional electrical stimulation
•
Motor BMI can be categorized as
•
Invasive
•
Partially Invasive
•
Non
-
Invasive
What is an EEG?
An
electroencephalogram
is
a
measure
of
the
brain's
voltage
fluctuations
as
detected
from
scalp
electrodes
.
It
is
an
approximation
of
the
cumulative
electrical
activity
of
neurons
.
•
Brain
–
set of interconnected modules
–
performs information processing
operations at various levels
•
sensory input analysis
•
memory storage and retrieval
•
reasoning
•
feelings
•
consciousness
•
Neurons
–
basic computational elements
–
heavily interconnected with other
neurons
Beta Rhythm
Alpha & Mu Rhythm
Grounding
Electrode Placement
Standard 10:20 System
Experiment Protocol
Bispectrum of EEG Signal
•
Bispectrum
is
the
expectation
of
three
frequencies
;
two
direct
frequency
components
and
the
third
the
conjugate
frequency
of
the
sum
of
those
two
frequencies
.
•
Knowing
the
Fourier
frequency
components
X(f)
the
bispectrum
B(f
1
,
f
2
)
can
be
estimated
using
the
Fourier
-
Stieltjes
representation
.
B(f
1
,
f
2
)
=
E(X(f
1
)X(f
2
)X*(f
1
+
f
2
))
Where
X*(f)
is
the
complex
conjugate
of
X(f)
and
E(
)
is
the
statistical
expectation
operator
Bispectrum Analysis
Bispectrum Analysis
•
Bispectrum
analysis
provide
a
way
to
evaluate
mental
representation
during
observation
and
imagination
of
hand
movement
•
Prior
visual
representation
of
motor
acts
make
difference
during
motor
imagination
.
Another experiment
•
Aim
is
to
classify
four
different
motor
imagery,
namely,
–
Both
Hands
Up
–
Tighten
Both
Fists
–
Left
Hand
Up
–
Right
Hand
Up
•
The
Protocol
Start Audio Cue
Action Audio Cue
Stop Audio Cue
Relax and keep
your eyes closed.
Imagine
the action
.
End the task
and relax.
The Architecture
EEG Unit
Noise &
Amplitude
Normalization
Feature
Extraction
Unit
K
-
fold cross
validation
SVM
Filtration &
Normalization Unit
Motor
Imagery
Types
Classification Unit
Hybrid Features of
Bispectrum
•
Here we do not make use of the bispectrum feature
directly rather we use following two hybrid features of
bispectrum in order to retain the temporal as well as
frequency information within the EEG data.
•
Sum of Logarithmic Amplitudes (SLA)
to characterizes temporal
bispectral information.
θ
gives the principal domain.
•
First Order Spectral Moment (FOSM)
to characterizes frequency
information of the bispectrum.
N is the number of diagonal elements of Bispectrum
Figure : Bispectrum Estimation of the EEG Signals.
Top
-
left: left hand motor imagery; top
-
right: right hand motor imagery;
bottom
-
left: both hands motor imagery & bottom
-
right: both fists motor imagery.
Bispectrum Analysis
Classification
•
We have used RBF kernel
SVM for classification of
the MIs.
•
The original SVM algorithm
was proposed by Vladimir
Vapnik in 1970.
•
The result is cross
-
validated
through 10
-
Fold Cross
Validation.
Confusion Matrix
BCI Based Maze Game
With an aim of developing a non
-
invasive BCI to be used as an
intelligent assistive system, we have designed and developed a simple
maze game, where a player plays the game in real time by using his
brain signals.
Mapping of Motor Imageries with
Game Moves
Motor Imagery
Game Move
Both Hands Up
Move Forward
Right Hand Up
Move Right
Left Hand Up
Move Left
Tight Both Fists
Move Backward
What we have done so far?
BCI Integrated Collaborative Control
•
The idea is to integrate a BCI with a cognitive architecture for
collaborative control of a smart wheelchair.
•
The cognitive architecture mediates based on the extent of
automatic vs. manual control to be achieved.
•
AIM…
–
To help people with mobility disability (with or without cognitive
impairment) to achieve a level of independence so that carryout their
daily activities.
BCI Integrated Collaborative Control
Architecture
Automatic control
Module
Adaptation
Module
Mediator
Manual control
Module
Sensing
Control
Sensor Role
Actor Role
Brain Computer Interface
Assistive Device
Intelligent/Smart Wheelchair
BCI Integrated Collaborative Control
Architecture
•
Three layered control architecture
–
BCI; Superior
Control and Local Control.
•
The BCI plays a dual role that of an
actor as well as a
sensor
.
•
It not only does provide control commands to drive
the wheelchair but also monitor the
cognitive state
of
the user
-
his
confidence, cognitive workload and
wellbeing
, depending on which BCI could provide
assistance range from partial control of navigation to
complete autonomous mode .
Final Comments
•
Over the years AI has drifted away from its main
aim. This work is an attempt to focus on integrated
systems rather than component algorithms.
•
The cognitive systems paradigm needs to have its
source of ideas in human cognition. This position
paper describes work done at the Biomimetic and
Cognitive Robotics Lab at Tezpur University for
development of a BCI Integrated Collaborative
Controller for an intelligent wheelchair.
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