SlideShare a Scribd company logo
Haptics
Symposium
2014
Haptics
Symposium
2014
Exoskeleton control by Motor Imagery
BCI for upper limb neurorehabilitation
Michele Barsotti, Antonio Frisoli
m.barsotti@sssup.it
a.frisoli@sssup.it
Brain Computer Interfaces
&
Haptics
February 23-26, 2014,
Houston Texas
Haptics Symposium
2014
Haptics
Symposium
2014
Haptics
Symposium
2014
• Brain activity acquisition methods
• General Brain Computer Interfaces
• Brain anatomy
• Movements EEG correlates
• Decoding movement intention by
analyzing EEG
• Feedbacks for motor-imagery-BCI
• Implementing a MI-BCI paradigm
OUTLINE
Haptics
Symposium
2014
Haptics
Symposium
2014
BRAIN ACTIVITY
ACQUISITION
Haptics
Symposium
2014
Haptics
Symposium
2014
ACQUIRING BRAIN ACTIVITY
Temporal Resolution [s]
SpatialResolution[cm]
BASED ON THE
BLOOD FLOW
VARIATION
BASED ON THE
MAGNETIC-
ELECTRICAL
ACTIVITY
Haptics
Symposium
2014
Haptics
Symposium
2014
ACQUIRING BRAIN ACTIVITY
Temporal Resolution [s]
SpatialResolution[cm]
ElectroCorticoGraphy
(ECoG)
Very good spatial and
temporal resolution (firing
of a single neuron)
INVASIVE
Surgical intervention
Haptics
Symposium
2014
Haptics
Symposium
2014
ACQUIRING BRAIN ACTIVITY
Temporal Resolution [s]
SpatialResolution[cm]
• Most widely used
strategy for BCI
applications
• Good Temporal
Resolution
• Several portable,
cheap systems exist
•Motion artifacts and
interferences can be greatly
reduced by employing active
electrodes
EEG is the record of
electrical activity of brain by
placing the electrodes on
the scalp.
Haptics
Symposium
2014
Haptics
Symposium
2014
EEG SIGNALs FEATURES
 AMPLITUDE RANGE:
 Wake EEG:: Vpp = 100µV
 Sleep EEG: Vpp = 300µV
 FREQUENCY RANGE:
 From 0.01 to 100 Hz
 COMMON EEG ARTIFACTs:
 Eye blinking (eye movement)
 Muscular activity (EMG)
 Ambient Noise + (50Hz-60Hz)
 Electrodes Movement
 Zero Mean
Haptics
Symposium
2014
Haptics
Symposium
2014
NATURAL EEG RHITMIC ACTIVITY
Band [Hz] Normaly Location
Gamma 32 + Displays during cross-modal sensory
processing and short-term memory
Somatosensory cortex
Beta 16 -
32
active thinking, focus, hi alert,
anxious
both sides, symmetrical
distribution, most evident
frontally; low-amplitude waves
Alpha 8 -
16
relaxed/reflecting
closing the eyes
inhibition control
posterior regions of head, both
sides, higher in amplitude on
non-dominant side.
Mu 8 -
12
Shows rest-state motor neurons Sensorimotor cortex
Theta 4 - 8 higher in young children
drowsiness in adults and teens
idling
Found in locations not related to
task at hand
Delta up to
4
adult slow-wave sleep
Has been found during some
continuous-attention tasks
frontally in adults, posteriorly in
children; high-amplitude waves
Haptics
Symposium
2014
Haptics
Symposium
2014
NATURAL EEG RHITMIC ACTIVITY
Band [Hz] Normaly Location
Gamma 32 + Displays during cross-modal sensory
processing and short-term memory
Somatosensory cortex
Beta 16 -
32
active thinking, focus, hi alert,
anxious
both sides, symmetrical
distribution, most evident
frontally; low-amplitude waves
Alpha 8 -
16
relaxed/reflecting
closing the eyes
inhibition control
posterior regions of head, both
sides, higher in amplitude on
non-dominant side.
Mu 8 -
12
Shows rest-state motor neurons Sensorimotor cortex
Theta 4 - 8 higher in young children
drowsiness in adults and teens
idling
Found in locations not related to
task at hand
Delta up to
4
adult slow-wave sleep
Has been found during some
continuous-attention tasks
frontally in adults, posteriorly in
children; high-amplitude waves
Haptics
Symposium
2014
Haptics
Symposium
2014
GENERAL BRAIN
COMPUTER
INTERFACES
Haptics
Symposium
2014
Haptics
Symposium
2014
GENERAL BCI FRAMEWORK
SIGNAL
PROCESSING
FEATURES
EXTRACTION
1 CSP
FC3 FCZ FC4
C5 C3 C1 CZ C2 C4 C6
CP3 CPZ CP4
-0.2 -0.1 0 -0.2 -0.1 0
CSP
13 CSP
FC3 FCZ FC4
C5 C3 C1 CZ C2 C4 C6
CP3 CPZ CP4VAR
MAX
VAR
MIN
SIGNAL
CLASSIFICATION
APPLICATION
OUTPUT
BIOFEEDBACK
USER
MENTAL
STRATEGY
BRAIN
SIGNALs
ACQUISITION
Haptics
Symposium
2014
Haptics
Symposium
2014
Feedback for
subject training
Machine
learning
 BCIs represent a set of
techniques to allow direct control
of a software or device via brain
activity – without the need of a
motor output
 The most common BCI approach
exploits voluntary modulation of
EEG activity, although more
invasive approaches have been
explored
 These techniques have
successfully been employed to
aid disabled patients
 Recently BCIs have also been
investigated as a rehabilitation
tool
GENERAL BCI FRAMEWORK
Haptics
Symposium
2014
Haptics
Symposium
2014
BCI CATEGORIES
INVASIVE NON-INVASIVE
Without penetrating the
skalp, mostly EEG, rarely
magnetoencephalogram
(MEG) or functional
magnetic resonance
imaging fMRI
- Several portable, cheap systems
exist
- Motion artifacts and interferences
can be greatly reduced by employing
active electrodes
DEPENDING ON THE ACQUISITION SYSTEM
Implanted sensors
(electrode array, needle
electrodes,
electrocorticogram ECoG)
-Control of 2-3 DoF, with good
accuracy.
-Implants have only been tested for
months after surgery
--Highly expensive
Haptics
Symposium
2014
Haptics
Symposium
2014
BCI
Invasive Non invasive
Single recording
site
Multiple recording
sites
ECoG
EEG
MEG
fMRI
Classification: signal acquisition
Haptics
Symposium
2014
Haptics
Symposium
2014
 Insertion of arrays of microelectrodes in cortical
tissue
 Control of 2-3 DoF, with good accuracy.
 Implants have only been tested for months
after surgery
 Highly expensive
Hochberg et al., Nature, 2006
Invasive vs. non-invasive BCI
 Invasive BCI
 Non-invasive BCI
 EEG systems range from low to high density (2
to 256 eletrodes)
 Several portable, cheap systems exist
 Motion artifacts and interferences can be
greatly reduced by employing active electrodes
Haptics
Symposium
2014
Haptics
Symposium
2014
BCI CATEGORIES
INDEPENDENT DEPENDENT
A Dependent BCI does not
use the brain’s normal
output pathways to carry
the message, but activity in
these pathway is needed to
generate the brain activity
that does carry it.
Independent from
peripheral nerves and
muscles, using only
central nervous system
(CNS) activity
DEPENDING ON THE MENTAL STRATEGY
Haptics
Symposium
2014
Haptics
Symposium
2014
BCI CATEGORIES
ENDOGENOUS EXOGENOUS
Evoked Potentials:
Users modulate brain
responses to external
stimuli
SSVEP
p300
Unstimulated Brain
Signals:
Users can voluntarily
produce the required
signals
(Motor Imagery,
Computational Task)
DEPENDING ON THE MENTAL STRATEGY
Haptics
Symposium
2014
Haptics
Symposium
2014
BCI CATEGORIES
ASYNCHRONOUS
Commands can only be
emitted synchronously with
external pace.
The system detects
when the user wants to
emit a command
DEPENDING ON THE COMMAND-TIMING
SYNCHRONOUS
The differences in EEG
response following
different stimuli are
used to discriminate
what subjects want
Subjects are asked to
perform visual
imagery tasks and
the local changes in
EEG power spectra
are recorded
Haptics
Symposium
2014
Haptics
Symposium
2014
 SSVEP
 VEP
 MOTOR IMAGERY  ERP (i.e.P300)
BCI CATEGORIES - SUMMARY
EXOGENOUSENDOGENOUSDEPENDENTINDEPENDENT
Haptics
Symposium
2014
Haptics
Symposium
2014
BRAIN ANATOMY
& EEG MOVEMENTS
CORRELATES
Haptics
Symposium
2014
Haptics
Symposium
2014
BRAIN ANATOMY
[Martini, 2006]
Haptics
Symposium
2014
Haptics
Symposium
2014
The Primary Somatic Sensory Cortex (Parietal Lobe) and the Primary Motor
Cortex (Temporal Lobe) are the most important regions for BCI research.
I
III
IV V
II
BRAIN ANATOMY: THE CEREBRAL CORTEX
Haptics
Symposium
2014
Haptics
Symposium
2014
The Primary Somatic Sensory Cortex (Parietal Lobe) and the Primary Motor
Cortex (Temporal Lobe) are the most important regions for BCI research.
I
III
IV V
II
BRAIN ANATOMY: THE CEREBRAL CORTEX
M1 S1
Haptics
Symposium
2014
Haptics
Symposium
2014
The Primary Somatic Sensory Cortex (Parietal Lobe) and the Primary Motor
Cortex (Temporal Lobe) are the most important regions for BCI research.
I
III
IV V
II
BRAIN ANATOMY: THE CEREBRAL CORTEX
M1 S1
Haptics
Symposium
2014
Haptics
Symposium
2014
TYPES OF MOVEMENT
Three types of movements may occur in respect of to ascending
and descending signals via different pathways and at different
levels:
• Reflexes movement : are performed
subconsciously and can occur at an exclusively
spinal level
• Rhythmic movement: stereotyped action
involving repetitions of the same movements
The control is at the spinal level without
involvement of higher cortical control
• Voluntary movement: usually goal directed
and therefore fully conscious. It arises in the
motor cortex and is executed by the spinal
cord.
Haptics
Symposium
2014
Haptics
Symposium
2014
TYPES OF MOVEMENT
Three types of movements may occur in respect of to ascending
and descending signals via different pathways and at different
levels:
• Reflexes movement : are performed
subconsciously and can occur at an exclusively
spinal level
• Rhythmic movement: stereotyped action
involving repetitions of the same movements
The control is at the spinal level without
involvement of higher cortical control
• Voluntary movement: usually goal directed
and therefore fully conscious. It arises in the
motor cortex and is executed by the spinal
cord.
When a voluntary movement is started, neurons in the
M1 send commands to upper and lower motor neurons.
The M1 needs to be stimulated by neurons from the
premotor cortex and the supplementary motor area
(SMA), which support and coordinate the M1, in order to
initiate a voluntary movement
Haptics
Symposium
2014
Haptics
Symposium
2014
Motor imagery is a mental process by which
an individual rehearses or simulates a given
action.
MOTOR IMAGERY
Performing motor imagery or attempting a
movement (i.e. for patients) influences the
brain activity as the voluntary movements do.
Haptics
Symposium
2014
Haptics
Symposium
2014
Why MOTOR IMAGERY is suitable for BCI?
• No need of external stimulus (it could be asynchronous)
• Not depend in any way on the brain’s normal output/input pathways
(independent)
• Possibility to provide different commands depending on which body
part is evolved in the simulated action
• Mental practice of motor actions via BCI training affect neuro-
rehabilitation in a positive way.
• the power in μ (8-12 Hz) and β (12-24Hz) EEG rhythms are affected
by motor imagery: Event Related Spectral Perturbation (ERSP)
• Users learn to perform motor imagery tasks
• Can be employed event if the motor areas are impaired
• Works mostly for digital control, has a fast response
Haptics
Symposium
2014
Haptics
Symposium
2014
DECODING
MOVEMENT
INTENTIONS BY
ANALIZING EEG
Haptics
Symposium
2014
Haptics
Symposium
2014
 Event Related Potential (ERP):
 - Repeatedly present discrete stimulus, average
raw EEG responses across presentations.
Characteristic feature (eg. P300)
 Event Related Spectral Perturbation (ERSP):
 Frequency band changes
- Average spectral features across presentation.
- Characteristic suppression/increase in power
(ERD/ERS: Event Related De-Synchronization).
EEG PHENOMENAL USABLE FOR BCI
 Event Related Spectral Perturbation (ERSP) and Event
Related Potential ERP are the measured brain response that
are the direct result of a specific sensory, cognitive, or motor
event.
Haptics
Symposium
2014
Haptics
Symposium
2014
time
Frequency[Hz]
epochtimechannel 
X epochfrequencytimechannel 
X
AVERAGING
time
Amplitude[µV]
ERP ERSP
• The ERPs and ERSP should be extracted from the background
noise mediating many recordings (Epochs or Trials)
Haptics
Symposium
2014
Haptics
Symposium
2014
Amplitude[µV]
EEG background
noise ~ 1/sqrt(N)
Costant Signal ERP
Repetition (N)
Post-Stimulus
EEG
Costant
Signal
Background
Noise
average
ERP
average
Signal
average
Noise
AVERAGING THEORY
S/N ratio increases as a
function of the square root of
the number of trials.
Haptics
Symposium
2014
Haptics
Symposium
2014
MOTOR IMAGERY CORRELATES IN EEG
Performing (or imagining) a motor action influences the EEG with two
main phenomena:
Haptics
Symposium
2014
Haptics
Symposium
2014
SLOW CORTICAL POTENTIALS [Kornhuber and Deecke (1965) ]
• Know as BereitschaftPotential
(readiness potential) or Movement
Related Cortical Potentials (MRCPs).
• Slow oscillations preceding the
movement
• Localized over the supplementary
motor area (SMA)
• Steps for MRCP detection
• Spatial filter,
• LP frequency filter
• Template extraction from the
training data
• matching with the ongoing eeg
MOTOR IMAGERY CORRELATES IN EEG
•Frequency close to the DC -> very
challenging to detect in single trial
Haptics
Symposium
2014
Haptics
Symposium
2014
SENSORIMOTOR RHYTHMS [Pfurtscheller and Lopes da Silva, (1999)]
• the power in μ (8-12 Hz) and β (12-24
Hz) EEG rhythms are affected by motor
imagery.
•Know also as Event Related
De/Synchronization (ERD,ERS)
•Steps for MRCP detection
• Spatial filter,
• Band Pass frequency filter
• Feature extraction
• LDA classifier
• High average classification
accuracy (>80%)
MOTOR IMAGERY CORRELATES IN EEG
Haptics
Symposium
2014
Haptics
Symposium
2014
ERD extraction: example with motor imagery
Collecting Trials
from a specific
electrode
Bandpass on the
specific frequency
Squaring Signals
Averaging over
Trials
Smoothing
[Pfurtscheller and Lopes da Silva, (1999)]
Haptics
Symposium
2014
Haptics
Symposium
2014
β ERD
13-30 Hz
µ ERD
8-12 Hz
Event Related
DeSynchronization
ERD Motor Imagery of
right hand
movement
EVENT RELATED SPECTRAL PERTURBATION
SENSORIMOTOR RHYTHMS
Haptics
Symposium
2014
Haptics
Symposium
2014
MOTOR IMAGERY: SIGLE TRIAL DETECTION
The important features of the motor imagery are:
 The frequency band.
 The spatial localization
A priori knowledgment:
 The frequency band are mu (8 -13Hz) and beta (15-30 Hz).
 The spatial localization is over the sensory motor
Very high intersubject
variability!
Need of optimized
spatial filters
Haptics
Symposium
2014
Haptics
Symposium
2014
The aim of spatial filtering is to improve the signal-to-noise ratio by
creating a virtual channel which is a (linear, in the following cases)
combination of the input channels of the filter.
A spatial filters can optimize the data extracted from an high number of
electrodes reducing the dimension of the features'space to only few
significant dimensions.
N-channel input (ex. 16 ch) 1-optimized channel output
1 CSP
FC3 FCZ FC4
C5 C3 C1 CZ C2 C4 C6
CP3 CPZ CP4
C
SPATIAL FILTERING
y(t) = a*ch1(t) + b*ch2(t) ....
Haptics
Symposium
2014
Haptics
Symposium
2014
Optimized Spatial filter: Common Spatial Pattern – CSP
VAR MAX VAR MIN
VAR MIN
RAW
CHANNELS
FIRST AND LAST
CSP FILTER
PROJECTED
DATA
REST MOVERESTMOVE
Trial i Trial i+1 Trial i Trial i+1
VAR MAX
[Pfurtscheller 1999]
Common Spatial Pattern (CSP) is a supervised spatial filtering
method for two-class discrimination problems, which finds
directions that maximize variance for one class and at the same
time minimize variance for the other class.
1 CSP
FC3 FCZ FC4
C5 C3 C1 CZ C2 C4 C6
CP3 CPZ CP4
-0.2 -0.1 0 -0.2 -0.1 0
CSP
13 CSP
FC3 FCZ FC4
C5 C3 C1 CZ C2 C4 C6
CP3 CPZ CP4
1 CSP
FC3 FCZ FC4
C5 C3 C1 CZ C2 C4 C6
CP3 CPZ CP4
-0.2 -0.1 0 -0.2 -0.1 0
CSP
13 CSP
FC3 FCZ FC4
C5 C3 C1 CZ C2 C4 C6
CP3 CPZ CP4
Haptics
Symposium
2014
Haptics
Symposium
2014
WHITENING
MATRIX
TRIALS
CLASS A
TRIALS
CLASS B
COVARIANCE
CLASS A
COVARIANCE
CLASS B
i
Ti
A
i
A
Ti
A
i
A
A
XXtrace
XX
R
)(

i
Ti
B
i
B
Ti
B
i
B
B
XXtrace
XX
R
)(

BAc RRR 
T
CCCC UUR 
T
CC UW
1

T
AA WWRS 
T
BB WWRS 
T
AA UUS 
T
BB UUS 
IBA 
WUP T

PXZ 
COMPOSITE
COVARIANCE
Transformed
Covariance A
Transformed
Covariance B
EIGENVECTOR
PROJECTION
MATRIX
EIGENVALUES
Common Spatial Pattern – Algorithms
Haptics
Symposium
2014
Haptics
Symposium
2014
• The scalp-plot of the Common Spatial Pattern can be
also used to give a physiological interpretation of the
data
Common Spatial Pattern: advantages
• Since variance of band-pass filtered
signals is equal to band-power, CSP filters
are well suited to discriminate mental
states characterized by spectral
perturbations (ERD and motor imagery
based BCIs).
Haptics
Symposium
2014
Haptics
Symposium
2014
The log-scaled band-power values in the mu and beta band of
the resulting two projected channels, can be used as a two-
dimensional feature of the brain activity. Classification is
performed using a linear discriminant classifier (LDA) or a
support vector machine (SVM)
CLASSIFICATION
Haptics
Symposium
2014
Haptics
Symposium
2014
CSP VARIANTS
 CSP – Pfurtscheller 1998
 FWM – Liu 2010
 CSSSP – Blankertz 2006
 CSSP – Lemm 2005
 SPEC-CSP – Tomioka 2006
 SB-CSP – Novi 2008
 FB-CSP – Ang 2008
 dCSP – Wang 2010
 SSCSP – Arvaneh 2011
 I-CSP – Blankertz 2008
With
Frequency
Optimization
Furhter Spatial
Optimization
Haptics
Symposium
2014
Haptics
Symposium
2014
FEEDBACKs for
motor imagery -
BCI
Haptics
Symposium
2014
Haptics
Symposium
2014
FEEDBACK FOR MOTOR IMAGERY
The biofeedback provided as a response to the mental activity can
improves the usability of motor imagery BCI.
The congruency of the provided feedback with the mental task is
expected to ease the performance of motor imagery.
Game
Illusion
Virtual reality
Exoskeleton
VISUAL
PROPRIOCEPTIVE
Haptics
Symposium
2014
Haptics
Symposium
2014
motor imagery –
BCI in
neurological
rehabilitation
Haptics
Symposium
2014
Haptics
Symposium
2014
BCI in neurological rehabilitation
Daly & Wolpaw,
Lancet, 2008
Haptics
Symposium
2014
Haptics
Symposium
2014
Daly & Wolpaw,
Lancet, 2008Goal: The subject should be able to control
muscle activity through brain activity
BCI in neurological rehabilitation
Haptics
Symposium
2014
Haptics
Symposium
2014
Daly & Wolpaw,
Lancet, 2008
Strategy 1: Train subjects to modulate
brain activity via visualization and
voluntary control of relevant features
BCI in neurological rehabilitation
Haptics
Symposium
2014
Haptics
Symposium
2014
Daly & Wolpaw,
Lancet, 2008
Strategy 2: Train subjects by using brain
activity to aid motion with assistive devices
BCI in neurological rehabilitation
Haptics
Symposium
2014
Haptics
Symposium
2014
A new multimodal architecture for gaze-independent brain–computer
interface (BCI)-driven control of a robotic upper limb exoskeleton for
stroke rehabilitation to provide active assistance in the execution of
reaching tasks in a real setting scenario.
Object
1
Object
2
Work
plane
Kinect
Eye tracker
BC
I
Haptics
Symposium
2014
Haptics
Symposium
2014
VAR
MIN
OPTIMAL CHANNELS
MOVE
VAR
MAX
REST MOVE REST
VAR
MAX
VAR
MIN
ORIGINAL CHANNELS
CSP
FILTERS
SVM
CLASSIFIER
TRAINING PHASE
VISUAL CONDITION ROBOT CONDITION
 Involving the BCI module only and
the visual feedback of a virtual arm
controlled through motor
 The subject performed a test session
with the complete system:
Kinect – EyeTracker – BCI – ArmExos
Haptics
Symposium
2014
Haptics
Symposium
2014
BCI-REHABILITATION PROTOCOL
BCI
EEG acquisition
& processing
L-EXOS
proprioceptive
feedback
MONITOR
visual feedback
• TRAINING PHASE: visual and proprioceptive feedback are provided accordingly to the task
• EXERCISE PHASE: the real-time classification output of the BCI was used for driving the
proprioceptive and visual feedback
ALL PATIENTS WERE
ABLE TO CONTROL THE
BCI SYSTEM AFTER
THE FIRST TWO
SESSION
5 right hemiparetic stroke
patients enrolled
SESSION STRUCTURE:
• MOVEMENT: the patient have to perform motor imagery of his impaired arm
• REST: the patient have to hold a resting mental state
TASKs REQUIRED:
Haptics
Symposium
2014
Haptics
Symposium
2014
BCI paradigm
based on Motor
Imagery
Haptics
Symposium
2014
Haptics
Symposium
2014
EEG
acquisition
Signal filtering
and
conditioning
Features
extraction
Features
classification
Online operations:
User
Offline BCI training
Frequency
bands and
artifact
removal
parameters
Spatial
Filter
parameters
Classifier
weights
Real-time feedback
MOTOR IMAGERY BCI: WORKFLOW
Haptics
Symposium
2014
Haptics
Symposium
2014
EEG CONFIGURATION
 EEG channels: minimal configuration
Frontal ground electrode
Reference ear lobe electrode
Electrodes covering the
motor cortex
Electrode for eye-blink
detection and removal
 Feature extraction
The power in the mu (8-12 Hz) and beta (16-24 Hz)
bands is computed over 500 ms windows.
Haptics
Symposium
2014
Haptics
Symposium
2014
TRAINING PHASE
Training paradigm
Subjects are asked to perform several motor imagery trials.
1. Feature classification
Acquired data is classified into two or more classes via machine
learning techniques, to optimize feature classification
2. Subject training
The subject is trained again with the output of the feature classifier as a
feedback signal, in order to optimize its motion imagery
TRIAL STRUCTURE
Haptics
Symposium
2014
Haptics
Symposium
2014
DATA PROCESSING
TRAINING
• Import data with the channel location
• Subdivide data into epochs for the two classes
• Remove artifactuated epochs
• Train the Common Spatial filter
• Extract Features
• Train the classifier
it is possible to predict the
BCI performance by a
visual inspection of both
the time-frequency plot of
the CSP-projected
channels and the
features plot
Haptics
Symposium
2014
Haptics
Symposium
2014
DATA PROCESSING: Visual Inspection
Haptics
Symposium
2014
Haptics
Symposium
2014
Time Frequency plot raw channels
Time [ms]
Frequency[Hz]
C3
-2000 0 2000 4000
10
20
30
-2
0
2
Time [ms]
Frequency[Hz]
CZ
-2000 0 2000 4000
10
20
30
-2
0
2
-2
0
2
CHANNELS ERD MAPS - MOVE
Time [ms]
Frequency[Hz]
C4
-2000 0 2000 4000
10
20
30
Time [ms]
Frequency[Hz]
C3
-2000 0 2000 4000
10
20
30
-2
-1
0
1
2
Time [ms]
Frequency[Hz]
CZ
-2000 0 2000 4000
10
20
30
-2
-1
0
1
2
-2
-1
0
1
2
CHANNELS ERD MAPS - REST
Time [ms]
Frequency[Hz]
C4
-2000 0 2000 4000
10
20
30
Click on electrodes to toggle name/number
Click on electrodes to toggle name/number
Click on electrodes to toggle name/number
Haptics
Symposium
2014
Haptics
Symposium
2014
Time Frequency plot CSP projected channels
Time [ms]
Frequency[Hz]
MOVE - First CSP
-2000 -1000 0 1000 2000 3000 4000
10
20
30
-5
0
5
Time [ms]
Frequency[Hz]
MOVE - Last CSP
-2000 -1000 0 1000 2000 3000 4000
10
20
30
-2
0
2
Time [ms]
Frequency[Hz]
REST - First CSP
-2000 -1000 0 1000 2000 3000 4000
10
20
30
-2
0
2
-2
0
2
Time [ms]
Frequency[Hz]
REST - Last CSP
-2000 -1000 0 1000 2000 3000 4000
10
20
30
FC3 FCZ FC4
C5 C3 C1 CZ C2 C4 C6
CP3 CPZ CP4
FC3 FCZ FC4
C5 C3 C1 CZ C2 C4 C6
CP3 CPZ CP4
First CSP
First CSP
Last CSP
Last CSP
REST trials
MOVE trials
Haptics
Symposium
2014
Haptics
Symposium
2014
0 1000 2000 3000
0
20
40
60
80
100
Time [ms]
CorrectRate[%]
CLASSIFIER PERFORMANCE
'Rest' ->89.65%
'Move'->99.95%
'Total' ->95.10%
1.8 2 2.2 2.4 2.6
2
2.5
3
3.5
1st CSP - Log Features
2ndCSP-LogFeatures
0
1
Support Vectors
PREDICTING RESULTS
Analysis of the BCI
output calculated with
parameters extracted
from the same dataset
Plot of each trial in
the features
space
Haptics
Symposium
2014
Haptics
Symposium
2014
MODEL
EEG amp
CSP and LDA
weights
Spatial Filtering
Features Extraction
Classifier
User Interface
Haptics
Symposium
2014
Haptics
Symposium
2014
SHOWING RESULTs
[Frisoli et al. 2012]
Haptics
Symposium
2014
Haptics
Symposium
2014
email: a.frisoli@sssup.it
m.barsotti@sssup.it
thank you!

More Related Content

What's hot

Brain Computer Interfaces(BCI)
Brain Computer Interfaces(BCI)Brain Computer Interfaces(BCI)
Brain Computer Interfaces(BCI)
Dr. Uday Saikia
 
Brain Computer Interface
Brain Computer InterfaceBrain Computer Interface
Brain Computer Interface
guest9fd1acd
 
Brain computer interface based
Brain computer interface basedBrain computer interface based
Brain computer interface based
Mohammed Saeed
 
Brain computer interface
Brain computer interfaceBrain computer interface
Brain computer interface
Sharat045
 
Brain Computer Interface (BCI)
Brain Computer Interface (BCI)Brain Computer Interface (BCI)
Brain Computer Interface (BCI)
Debabrata Chakraborty
 
Brain computer interface -smart living enviroment
Brain computer interface -smart living enviroment Brain computer interface -smart living enviroment
Brain computer interface -smart living enviroment
Anu N Raj
 
Brain computer interface
Brain computer interfaceBrain computer interface
Brain computer interface
NeurologyKota
 
Brain Computer Interface (BCI) - seminar PPT
Brain Computer Interface (BCI) -  seminar PPTBrain Computer Interface (BCI) -  seminar PPT
Brain Computer Interface (BCI) - seminar PPT
SHAMJITH KM
 
how brain computer interface works
how brain computer interface workshow brain computer interface works
how brain computer interface works
guest0cc23a8
 
BRAIN COMPUTER INTERFACE Documentation
BRAIN COMPUTER INTERFACE DocumentationBRAIN COMPUTER INTERFACE Documentation
BRAIN COMPUTER INTERFACE Documentation
Bhadra Gowdra
 
Brain computer interface
Brain computer interfaceBrain computer interface
Brain computer interface
Suman Chandra
 
Brain computer interface
Brain computer interfaceBrain computer interface
Brain computer interface
anilkumarkandrika
 
Brain computer interface
Brain computer interfaceBrain computer interface
Brain computer interface
Jaykrishna Thakkar
 
brain computer-interfaces PPT
 brain computer-interfaces PPT brain computer-interfaces PPT
brain computer-interfaces PPT
Vijay Mehta
 
Brain computer interfaces
Brain computer interfacesBrain computer interfaces
Brain computer interfaces
pavanireddy86
 
Exploring the Brain Computer Interface
Exploring the Brain Computer InterfaceExploring the Brain Computer Interface
Exploring the Brain Computer Interface
Jim McKeeth
 
EEG based Motor Imagery Classification using SVM and MLP
EEG based Motor Imagery Classification using SVM and MLPEEG based Motor Imagery Classification using SVM and MLP
EEG based Motor Imagery Classification using SVM and MLP
Dr. Rajdeep Chatterjee
 
BRAINWAVE TECHNOLOGY
BRAINWAVE TECHNOLOGYBRAINWAVE TECHNOLOGY
BRAINWAVE TECHNOLOGY
girisandeepreddy
 
Brain computer interface
Brain computer interfaceBrain computer interface
Brain computer interface
Koushik Veldanda
 
Brain computer interface
Brain computer interfaceBrain computer interface
Brain computer interface
Komal Maloo
 

What's hot (20)

Brain Computer Interfaces(BCI)
Brain Computer Interfaces(BCI)Brain Computer Interfaces(BCI)
Brain Computer Interfaces(BCI)
 
Brain Computer Interface
Brain Computer InterfaceBrain Computer Interface
Brain Computer Interface
 
Brain computer interface based
Brain computer interface basedBrain computer interface based
Brain computer interface based
 
Brain computer interface
Brain computer interfaceBrain computer interface
Brain computer interface
 
Brain Computer Interface (BCI)
Brain Computer Interface (BCI)Brain Computer Interface (BCI)
Brain Computer Interface (BCI)
 
Brain computer interface -smart living enviroment
Brain computer interface -smart living enviroment Brain computer interface -smart living enviroment
Brain computer interface -smart living enviroment
 
Brain computer interface
Brain computer interfaceBrain computer interface
Brain computer interface
 
Brain Computer Interface (BCI) - seminar PPT
Brain Computer Interface (BCI) -  seminar PPTBrain Computer Interface (BCI) -  seminar PPT
Brain Computer Interface (BCI) - seminar PPT
 
how brain computer interface works
how brain computer interface workshow brain computer interface works
how brain computer interface works
 
BRAIN COMPUTER INTERFACE Documentation
BRAIN COMPUTER INTERFACE DocumentationBRAIN COMPUTER INTERFACE Documentation
BRAIN COMPUTER INTERFACE Documentation
 
Brain computer interface
Brain computer interfaceBrain computer interface
Brain computer interface
 
Brain computer interface
Brain computer interfaceBrain computer interface
Brain computer interface
 
Brain computer interface
Brain computer interfaceBrain computer interface
Brain computer interface
 
brain computer-interfaces PPT
 brain computer-interfaces PPT brain computer-interfaces PPT
brain computer-interfaces PPT
 
Brain computer interfaces
Brain computer interfacesBrain computer interfaces
Brain computer interfaces
 
Exploring the Brain Computer Interface
Exploring the Brain Computer InterfaceExploring the Brain Computer Interface
Exploring the Brain Computer Interface
 
EEG based Motor Imagery Classification using SVM and MLP
EEG based Motor Imagery Classification using SVM and MLPEEG based Motor Imagery Classification using SVM and MLP
EEG based Motor Imagery Classification using SVM and MLP
 
BRAINWAVE TECHNOLOGY
BRAINWAVE TECHNOLOGYBRAINWAVE TECHNOLOGY
BRAINWAVE TECHNOLOGY
 
Brain computer interface
Brain computer interfaceBrain computer interface
Brain computer interface
 
Brain computer interface
Brain computer interfaceBrain computer interface
Brain computer interface
 

Viewers also liked

Introduction to Common Spatial Pattern Filters for EEG Motor Imagery Classifi...
Introduction to Common Spatial Pattern Filters for EEG Motor Imagery Classifi...Introduction to Common Spatial Pattern Filters for EEG Motor Imagery Classifi...
Introduction to Common Spatial Pattern Filters for EEG Motor Imagery Classifi...
Tatsuya Yokota
 
Conferencia del Dr. Niels Birbaumer en #Perspectives2015
Conferencia del Dr. Niels Birbaumer en #Perspectives2015Conferencia del Dr. Niels Birbaumer en #Perspectives2015
Conferencia del Dr. Niels Birbaumer en #Perspectives2015
TECNALIA Research & Innovation
 
Dataset ii ia
Dataset ii iaDataset ii ia
Dataset ii ia
Ashwani Jha
 
When Classifier Selection meets Information Theory: A Unifying View
When Classifier Selection meets Information Theory: A Unifying ViewWhen Classifier Selection meets Information Theory: A Unifying View
When Classifier Selection meets Information Theory: A Unifying View
Mohamed Farouk
 
IEEE 2014 MATLAB IMAGE PROCESSING PROJECTS Independent component ensemble of ...
IEEE 2014 MATLAB IMAGE PROCESSING PROJECTS Independent component ensemble of ...IEEE 2014 MATLAB IMAGE PROCESSING PROJECTS Independent component ensemble of ...
IEEE 2014 MATLAB IMAGE PROCESSING PROJECTS Independent component ensemble of ...
IEEEBEBTECHSTUDENTPROJECTS
 
EDX: Evoked potentilas
EDX: Evoked potentilasEDX: Evoked potentilas
EDX: Evoked potentilas
Shahram Sadeqi
 
"Kate, a Platform for Machine Intelligence" by Wayne Imaino, IBM Research
"Kate, a Platform for Machine Intelligence" by Wayne Imaino, IBM Research"Kate, a Platform for Machine Intelligence" by Wayne Imaino, IBM Research
"Kate, a Platform for Machine Intelligence" by Wayne Imaino, IBM Research
diannepatricia
 
Inroduction to BCI
Inroduction to BCIInroduction to BCI
Inroduction to BCI
Mohamed Abdelaal
 
V isual evoked potentials
V isual evoked potentialsV isual evoked potentials
V isual evoked potentials
Neurology resident slides
 
What the Brain says about Machine Intelligence
What the Brain says about Machine Intelligence What the Brain says about Machine Intelligence
What the Brain says about Machine Intelligence
Numenta
 
Real-Time Streaming Data Analysis with HTM
Real-Time Streaming Data Analysis with HTMReal-Time Streaming Data Analysis with HTM
Real-Time Streaming Data Analysis with HTM
Numenta
 
EEG Epilepsy
EEG EpilepsyEEG Epilepsy
EEG Epilepsy
ZY The Ripper
 
Support Vector Machines
Support Vector MachinesSupport Vector Machines
Support Vector Machines
adil raja
 
Eeg wave pattern
Eeg wave patternEeg wave pattern
Eeg wave pattern
Roopchand Ps
 
EEG - Montages, Equipment and Basic Physics
EEG - Montages, Equipment and Basic PhysicsEEG - Montages, Equipment and Basic Physics
EEG - Montages, Equipment and Basic Physics
Rahul Kumar
 
Passo a passo para baixar slides
Passo a passo para baixar slidesPasso a passo para baixar slides
Passo a passo para baixar slides
Dênia Cavalcante
 
TEDx Manchester: AI & The Future of Work
TEDx Manchester: AI & The Future of WorkTEDx Manchester: AI & The Future of Work
TEDx Manchester: AI & The Future of Work
Volker Hirsch
 

Viewers also liked (17)

Introduction to Common Spatial Pattern Filters for EEG Motor Imagery Classifi...
Introduction to Common Spatial Pattern Filters for EEG Motor Imagery Classifi...Introduction to Common Spatial Pattern Filters for EEG Motor Imagery Classifi...
Introduction to Common Spatial Pattern Filters for EEG Motor Imagery Classifi...
 
Conferencia del Dr. Niels Birbaumer en #Perspectives2015
Conferencia del Dr. Niels Birbaumer en #Perspectives2015Conferencia del Dr. Niels Birbaumer en #Perspectives2015
Conferencia del Dr. Niels Birbaumer en #Perspectives2015
 
Dataset ii ia
Dataset ii iaDataset ii ia
Dataset ii ia
 
When Classifier Selection meets Information Theory: A Unifying View
When Classifier Selection meets Information Theory: A Unifying ViewWhen Classifier Selection meets Information Theory: A Unifying View
When Classifier Selection meets Information Theory: A Unifying View
 
IEEE 2014 MATLAB IMAGE PROCESSING PROJECTS Independent component ensemble of ...
IEEE 2014 MATLAB IMAGE PROCESSING PROJECTS Independent component ensemble of ...IEEE 2014 MATLAB IMAGE PROCESSING PROJECTS Independent component ensemble of ...
IEEE 2014 MATLAB IMAGE PROCESSING PROJECTS Independent component ensemble of ...
 
EDX: Evoked potentilas
EDX: Evoked potentilasEDX: Evoked potentilas
EDX: Evoked potentilas
 
"Kate, a Platform for Machine Intelligence" by Wayne Imaino, IBM Research
"Kate, a Platform for Machine Intelligence" by Wayne Imaino, IBM Research"Kate, a Platform for Machine Intelligence" by Wayne Imaino, IBM Research
"Kate, a Platform for Machine Intelligence" by Wayne Imaino, IBM Research
 
Inroduction to BCI
Inroduction to BCIInroduction to BCI
Inroduction to BCI
 
V isual evoked potentials
V isual evoked potentialsV isual evoked potentials
V isual evoked potentials
 
What the Brain says about Machine Intelligence
What the Brain says about Machine Intelligence What the Brain says about Machine Intelligence
What the Brain says about Machine Intelligence
 
Real-Time Streaming Data Analysis with HTM
Real-Time Streaming Data Analysis with HTMReal-Time Streaming Data Analysis with HTM
Real-Time Streaming Data Analysis with HTM
 
EEG Epilepsy
EEG EpilepsyEEG Epilepsy
EEG Epilepsy
 
Support Vector Machines
Support Vector MachinesSupport Vector Machines
Support Vector Machines
 
Eeg wave pattern
Eeg wave patternEeg wave pattern
Eeg wave pattern
 
EEG - Montages, Equipment and Basic Physics
EEG - Montages, Equipment and Basic PhysicsEEG - Montages, Equipment and Basic Physics
EEG - Montages, Equipment and Basic Physics
 
Passo a passo para baixar slides
Passo a passo para baixar slidesPasso a passo para baixar slides
Passo a passo para baixar slides
 
TEDx Manchester: AI & The Future of Work
TEDx Manchester: AI & The Future of WorkTEDx Manchester: AI & The Future of Work
TEDx Manchester: AI & The Future of Work
 

Similar to Hs2014 bci mi

Cognitive and Emotional Neuro-Prostheses
Cognitive and Emotional Neuro-ProsthesesCognitive and Emotional Neuro-Prostheses
Cognitive and Emotional Neuro-Prostheses
Omer Ali
 
brain computer interface to control windows os presentation
brain computer interface to control windows os presentationbrain computer interface to control windows os presentation
brain computer interface to control windows os presentation
digil vinoy
 
Braingate
BraingateBraingate
Braingate
sayalipatil528
 
F1102024349
F1102024349F1102024349
F1102024349
IOSR Journals
 
Our Best Ideas in Our Hands with Adaptive Virtual Reality - MCAA - ESOF 2016
Our Best Ideas in Our Hands with Adaptive Virtual Reality - MCAA - ESOF 2016Our Best Ideas in Our Hands with Adaptive Virtual Reality - MCAA - ESOF 2016
Our Best Ideas in Our Hands with Adaptive Virtual Reality - MCAA - ESOF 2016
Niki Lambropoulos PhD
 
Reflex and Voluntary Control of Movement
Reflex and Voluntary Control of MovementReflex and Voluntary Control of Movement
Reflex and Voluntary Control of Movement
Csilla Egri
 
brain-computer-interfaces (1).pdf
brain-computer-interfaces (1).pdfbrain-computer-interfaces (1).pdf
brain-computer-interfaces (1).pdf
anooptiwari82
 
Brain-Computer Interface: Rebuilding radically new output of the Human Brain
Brain-Computer Interface:  Rebuilding radically new output of the Human BrainBrain-Computer Interface:  Rebuilding radically new output of the Human Brain
Brain-Computer Interface: Rebuilding radically new output of the Human Brain
Arina Kochetova
 
Brainwave Controlled Robotic Arm
Brainwave Controlled Robotic ArmBrainwave Controlled Robotic Arm
Brainwave Controlled Robotic Arm
IRJET Journal
 
Brain gate system
Brain gate systemBrain gate system
Brain gate system
Abhishek Srivastava
 
Password system that are mind control
Password system that are mind controlPassword system that are mind control
Password system that are mind control
vivatechijri
 
Neurobionics and robotic neurorehabilitations
Neurobionics and robotic neurorehabilitationsNeurobionics and robotic neurorehabilitations
Neurobionics and robotic neurorehabilitations
NeurologyKota
 
Biomarker and human cognition.pdf
Biomarker and human cognition.pdfBiomarker and human cognition.pdf
Biomarker and human cognition.pdf
ArundhutiNaskar
 
General Physiology - The nervous system, basic functions of synapses
General Physiology - The nervous system, basic functions of synapsesGeneral Physiology - The nervous system, basic functions of synapses
General Physiology - The nervous system, basic functions of synapses
Hamzeh AlBattikhi
 
neuro prosthesis
neuro prosthesisneuro prosthesis
neuro prosthesis
Sanket Rout
 
BrainWave Technology
BrainWave TechnologyBrainWave Technology
BrainWave Technology
Aditya SHAH
 
0rganization of nervous system & mechanism of synaptic transmission.pptx
0rganization of nervous system & mechanism of synaptic transmission.pptx0rganization of nervous system & mechanism of synaptic transmission.pptx
0rganization of nervous system & mechanism of synaptic transmission.pptx
bobehe4189
 
Neural interfacing
Neural interfacingNeural interfacing
Neural interfacing
Brujath Bru
 
EEG & Evoked potentials
EEG & Evoked potentialsEEG & Evoked potentials
EEG signal processing and application to Neurofeedback : Operant Conditioning...
EEG signal processing and application to Neurofeedback : Operant Conditioning...EEG signal processing and application to Neurofeedback : Operant Conditioning...
EEG signal processing and application to Neurofeedback : Operant Conditioning...
MOK wahedi
 

Similar to Hs2014 bci mi (20)

Cognitive and Emotional Neuro-Prostheses
Cognitive and Emotional Neuro-ProsthesesCognitive and Emotional Neuro-Prostheses
Cognitive and Emotional Neuro-Prostheses
 
brain computer interface to control windows os presentation
brain computer interface to control windows os presentationbrain computer interface to control windows os presentation
brain computer interface to control windows os presentation
 
Braingate
BraingateBraingate
Braingate
 
F1102024349
F1102024349F1102024349
F1102024349
 
Our Best Ideas in Our Hands with Adaptive Virtual Reality - MCAA - ESOF 2016
Our Best Ideas in Our Hands with Adaptive Virtual Reality - MCAA - ESOF 2016Our Best Ideas in Our Hands with Adaptive Virtual Reality - MCAA - ESOF 2016
Our Best Ideas in Our Hands with Adaptive Virtual Reality - MCAA - ESOF 2016
 
Reflex and Voluntary Control of Movement
Reflex and Voluntary Control of MovementReflex and Voluntary Control of Movement
Reflex and Voluntary Control of Movement
 
brain-computer-interfaces (1).pdf
brain-computer-interfaces (1).pdfbrain-computer-interfaces (1).pdf
brain-computer-interfaces (1).pdf
 
Brain-Computer Interface: Rebuilding radically new output of the Human Brain
Brain-Computer Interface:  Rebuilding radically new output of the Human BrainBrain-Computer Interface:  Rebuilding radically new output of the Human Brain
Brain-Computer Interface: Rebuilding radically new output of the Human Brain
 
Brainwave Controlled Robotic Arm
Brainwave Controlled Robotic ArmBrainwave Controlled Robotic Arm
Brainwave Controlled Robotic Arm
 
Brain gate system
Brain gate systemBrain gate system
Brain gate system
 
Password system that are mind control
Password system that are mind controlPassword system that are mind control
Password system that are mind control
 
Neurobionics and robotic neurorehabilitations
Neurobionics and robotic neurorehabilitationsNeurobionics and robotic neurorehabilitations
Neurobionics and robotic neurorehabilitations
 
Biomarker and human cognition.pdf
Biomarker and human cognition.pdfBiomarker and human cognition.pdf
Biomarker and human cognition.pdf
 
General Physiology - The nervous system, basic functions of synapses
General Physiology - The nervous system, basic functions of synapsesGeneral Physiology - The nervous system, basic functions of synapses
General Physiology - The nervous system, basic functions of synapses
 
neuro prosthesis
neuro prosthesisneuro prosthesis
neuro prosthesis
 
BrainWave Technology
BrainWave TechnologyBrainWave Technology
BrainWave Technology
 
0rganization of nervous system & mechanism of synaptic transmission.pptx
0rganization of nervous system & mechanism of synaptic transmission.pptx0rganization of nervous system & mechanism of synaptic transmission.pptx
0rganization of nervous system & mechanism of synaptic transmission.pptx
 
Neural interfacing
Neural interfacingNeural interfacing
Neural interfacing
 
EEG & Evoked potentials
EEG & Evoked potentialsEEG & Evoked potentials
EEG & Evoked potentials
 
EEG signal processing and application to Neurofeedback : Operant Conditioning...
EEG signal processing and application to Neurofeedback : Operant Conditioning...EEG signal processing and application to Neurofeedback : Operant Conditioning...
EEG signal processing and application to Neurofeedback : Operant Conditioning...
 

Recently uploaded

一比一原版(CalArts毕业证)加利福尼亚艺术学院毕业证如何办理
一比一原版(CalArts毕业证)加利福尼亚艺术学院毕业证如何办理一比一原版(CalArts毕业证)加利福尼亚艺术学院毕业证如何办理
一比一原版(CalArts毕业证)加利福尼亚艺术学院毕业证如何办理
ecqow
 
Seminar on Distillation study-mafia.pptx
Seminar on Distillation study-mafia.pptxSeminar on Distillation study-mafia.pptx
Seminar on Distillation study-mafia.pptx
Madan Karki
 
AI assisted telemedicine KIOSK for Rural India.pptx
AI assisted telemedicine KIOSK for Rural India.pptxAI assisted telemedicine KIOSK for Rural India.pptx
AI assisted telemedicine KIOSK for Rural India.pptx
architagupta876
 
2008 BUILDING CONSTRUCTION Illustrated - Ching Chapter 02 The Building.pdf
2008 BUILDING CONSTRUCTION Illustrated - Ching Chapter 02 The Building.pdf2008 BUILDING CONSTRUCTION Illustrated - Ching Chapter 02 The Building.pdf
2008 BUILDING CONSTRUCTION Illustrated - Ching Chapter 02 The Building.pdf
Yasser Mahgoub
 
Rainfall intensity duration frequency curve statistical analysis and modeling...
Rainfall intensity duration frequency curve statistical analysis and modeling...Rainfall intensity duration frequency curve statistical analysis and modeling...
Rainfall intensity duration frequency curve statistical analysis and modeling...
bijceesjournal
 
Design and optimization of ion propulsion drone
Design and optimization of ion propulsion droneDesign and optimization of ion propulsion drone
Design and optimization of ion propulsion drone
bjmsejournal
 
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...
IJECEIAES
 
4. Mosca vol I -Fisica-Tipler-5ta-Edicion-Vol-1.pdf
4. Mosca vol I -Fisica-Tipler-5ta-Edicion-Vol-1.pdf4. Mosca vol I -Fisica-Tipler-5ta-Edicion-Vol-1.pdf
4. Mosca vol I -Fisica-Tipler-5ta-Edicion-Vol-1.pdf
Gino153088
 
Welding Metallurgy Ferrous Materials.pdf
Welding Metallurgy Ferrous Materials.pdfWelding Metallurgy Ferrous Materials.pdf
Welding Metallurgy Ferrous Materials.pdf
AjmalKhan50578
 
integral complex analysis chapter 06 .pdf
integral complex analysis chapter 06 .pdfintegral complex analysis chapter 06 .pdf
integral complex analysis chapter 06 .pdf
gaafergoudaay7aga
 
官方认证美国密歇根州立大学毕业证学位证书原版一模一样
官方认证美国密歇根州立大学毕业证学位证书原版一模一样官方认证美国密歇根州立大学毕业证学位证书原版一模一样
官方认证美国密歇根州立大学毕业证学位证书原版一模一样
171ticu
 
artificial intelligence and data science contents.pptx
artificial intelligence and data science contents.pptxartificial intelligence and data science contents.pptx
artificial intelligence and data science contents.pptx
GauravCar
 
Null Bangalore | Pentesters Approach to AWS IAM
Null Bangalore | Pentesters Approach to AWS IAMNull Bangalore | Pentesters Approach to AWS IAM
Null Bangalore | Pentesters Approach to AWS IAM
Divyanshu
 
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...Electric vehicle and photovoltaic advanced roles in enhancing the financial p...
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...
IJECEIAES
 
Applications of artificial Intelligence in Mechanical Engineering.pdf
Applications of artificial Intelligence in Mechanical Engineering.pdfApplications of artificial Intelligence in Mechanical Engineering.pdf
Applications of artificial Intelligence in Mechanical Engineering.pdf
Atif Razi
 
cnn.pptx Convolutional neural network used for image classication
cnn.pptx Convolutional neural network used for image classicationcnn.pptx Convolutional neural network used for image classication
cnn.pptx Convolutional neural network used for image classication
SakkaravarthiShanmug
 
Properties Railway Sleepers and Test.pptx
Properties Railway Sleepers and Test.pptxProperties Railway Sleepers and Test.pptx
Properties Railway Sleepers and Test.pptx
MDSABBIROJJAMANPAYEL
 
CEC 352 - SATELLITE COMMUNICATION UNIT 1
CEC 352 - SATELLITE COMMUNICATION UNIT 1CEC 352 - SATELLITE COMMUNICATION UNIT 1
CEC 352 - SATELLITE COMMUNICATION UNIT 1
PKavitha10
 
spirit beverages ppt without graphics.pptx
spirit beverages ppt without graphics.pptxspirit beverages ppt without graphics.pptx
spirit beverages ppt without graphics.pptx
Madan Karki
 
哪里办理(csu毕业证书)查尔斯特大学毕业证硕士学历原版一模一样
哪里办理(csu毕业证书)查尔斯特大学毕业证硕士学历原版一模一样哪里办理(csu毕业证书)查尔斯特大学毕业证硕士学历原版一模一样
哪里办理(csu毕业证书)查尔斯特大学毕业证硕士学历原版一模一样
insn4465
 

Recently uploaded (20)

一比一原版(CalArts毕业证)加利福尼亚艺术学院毕业证如何办理
一比一原版(CalArts毕业证)加利福尼亚艺术学院毕业证如何办理一比一原版(CalArts毕业证)加利福尼亚艺术学院毕业证如何办理
一比一原版(CalArts毕业证)加利福尼亚艺术学院毕业证如何办理
 
Seminar on Distillation study-mafia.pptx
Seminar on Distillation study-mafia.pptxSeminar on Distillation study-mafia.pptx
Seminar on Distillation study-mafia.pptx
 
AI assisted telemedicine KIOSK for Rural India.pptx
AI assisted telemedicine KIOSK for Rural India.pptxAI assisted telemedicine KIOSK for Rural India.pptx
AI assisted telemedicine KIOSK for Rural India.pptx
 
2008 BUILDING CONSTRUCTION Illustrated - Ching Chapter 02 The Building.pdf
2008 BUILDING CONSTRUCTION Illustrated - Ching Chapter 02 The Building.pdf2008 BUILDING CONSTRUCTION Illustrated - Ching Chapter 02 The Building.pdf
2008 BUILDING CONSTRUCTION Illustrated - Ching Chapter 02 The Building.pdf
 
Rainfall intensity duration frequency curve statistical analysis and modeling...
Rainfall intensity duration frequency curve statistical analysis and modeling...Rainfall intensity duration frequency curve statistical analysis and modeling...
Rainfall intensity duration frequency curve statistical analysis and modeling...
 
Design and optimization of ion propulsion drone
Design and optimization of ion propulsion droneDesign and optimization of ion propulsion drone
Design and optimization of ion propulsion drone
 
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...
 
4. Mosca vol I -Fisica-Tipler-5ta-Edicion-Vol-1.pdf
4. Mosca vol I -Fisica-Tipler-5ta-Edicion-Vol-1.pdf4. Mosca vol I -Fisica-Tipler-5ta-Edicion-Vol-1.pdf
4. Mosca vol I -Fisica-Tipler-5ta-Edicion-Vol-1.pdf
 
Welding Metallurgy Ferrous Materials.pdf
Welding Metallurgy Ferrous Materials.pdfWelding Metallurgy Ferrous Materials.pdf
Welding Metallurgy Ferrous Materials.pdf
 
integral complex analysis chapter 06 .pdf
integral complex analysis chapter 06 .pdfintegral complex analysis chapter 06 .pdf
integral complex analysis chapter 06 .pdf
 
官方认证美国密歇根州立大学毕业证学位证书原版一模一样
官方认证美国密歇根州立大学毕业证学位证书原版一模一样官方认证美国密歇根州立大学毕业证学位证书原版一模一样
官方认证美国密歇根州立大学毕业证学位证书原版一模一样
 
artificial intelligence and data science contents.pptx
artificial intelligence and data science contents.pptxartificial intelligence and data science contents.pptx
artificial intelligence and data science contents.pptx
 
Null Bangalore | Pentesters Approach to AWS IAM
Null Bangalore | Pentesters Approach to AWS IAMNull Bangalore | Pentesters Approach to AWS IAM
Null Bangalore | Pentesters Approach to AWS IAM
 
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...Electric vehicle and photovoltaic advanced roles in enhancing the financial p...
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...
 
Applications of artificial Intelligence in Mechanical Engineering.pdf
Applications of artificial Intelligence in Mechanical Engineering.pdfApplications of artificial Intelligence in Mechanical Engineering.pdf
Applications of artificial Intelligence in Mechanical Engineering.pdf
 
cnn.pptx Convolutional neural network used for image classication
cnn.pptx Convolutional neural network used for image classicationcnn.pptx Convolutional neural network used for image classication
cnn.pptx Convolutional neural network used for image classication
 
Properties Railway Sleepers and Test.pptx
Properties Railway Sleepers and Test.pptxProperties Railway Sleepers and Test.pptx
Properties Railway Sleepers and Test.pptx
 
CEC 352 - SATELLITE COMMUNICATION UNIT 1
CEC 352 - SATELLITE COMMUNICATION UNIT 1CEC 352 - SATELLITE COMMUNICATION UNIT 1
CEC 352 - SATELLITE COMMUNICATION UNIT 1
 
spirit beverages ppt without graphics.pptx
spirit beverages ppt without graphics.pptxspirit beverages ppt without graphics.pptx
spirit beverages ppt without graphics.pptx
 
哪里办理(csu毕业证书)查尔斯特大学毕业证硕士学历原版一模一样
哪里办理(csu毕业证书)查尔斯特大学毕业证硕士学历原版一模一样哪里办理(csu毕业证书)查尔斯特大学毕业证硕士学历原版一模一样
哪里办理(csu毕业证书)查尔斯特大学毕业证硕士学历原版一模一样
 

Hs2014 bci mi