Senior project presentation on Study of Motion Artifact in Ambulatory EEG

1. IndependentUniversityBangladesh
Undergradseniorprojectpresentation
STUDYANDANALYSISOFMOTIONARTIFACTFORAMBULATORY
EEG.
Supervised By:
Md. Kafiul Islam
Presented By:
Asma Islam (1620262)
Eshrat Jahan Esha (1620033)

2. Outline
Electroencephalography (EEG)
 Basics of EEG
 Ambulatory EEG
Motivation
Literature review
Objective
Methodology
 Experiment Design
 Proposed algorithm
Result and Discussion
Conclusion
2

3. Electroencephalography
 Electroencephalography is a non-invasive brain recording
technique that records electrical activities in cortex
spontaneously .
 Bandwidth: 0.05-128Hz; amplitude: 0.02- 0.4mV
(a) (b)
Figure 1: (a)Layers of human brain; and (b) Brain signal recording techniques. 3

4. 4
 Delta: (< 4 Hz)
◦ adult slow-wave sleep
◦ in babies
◦ continuous-attention tasks
 Theta: (4 - 8 Hz)
◦ higher in young children
◦ drowsiness in adults and teens
◦ idling
 Alpha: (8 - 13 Hz)
◦ relaxed/reflecting
◦ closing the eyes
 Beta: (14 - 30 Hz)
◦ active thinking, focus, high alert,
anxious
 Gamma: (> 30 Hz)
◦ Displays during cross-modal sensory
processing
 Mu: (7.5 - 12.5 Hz)
◦ Shows rest-state motor neurons
Gamma

5. History of EEG
• First human EEG was recorded by Hans Berger in 1924.
• Signal was recorded in semi non-invasive way .
• He is considered to be the father of EEG.
Figure 2: First recorded human electroencephalogram
5

6. Applications of EEG
Application
Clinical
Diagnosis
Seizure
prediction
Generalized
brain
dysfunction
BCI
Prosthetics
Communication
devices
6

7. Advantages of EEG
• Non invasive
• Safe
• High temporal resolution
• Moderate spatial
resolution
• Cost effective
• Portability
Figure : Comparison of existing modalities
7

8. Ambulatory EEG
 Portable
 Light weight
 Safe in unrestricted
environment
 Easier to understand
 Continuous monitoring
 Affordable.
8

9. Recording setups
Conventional EEG Ambulatory EEG
9

10. Available devices
EMOTIV EPOC+ NeuroSky
B-Alert X10 EEG Apollo cadewell
10

11. Artifacts in AEEG
11

12. Challenge: Motion Artifacts
 Ambulatory EEG facilitates mobility making it prone to motion
Artifacts the most.
 During motion, voltage fluctuations occur due to the
movements of electrode from their standard position or cable
sway.
 Motion artifacts can occur from different sources like Ground
reaction forces, cyclic motion, head movements which are
related to daily activities.
 It has the same frequency spectra ranging up to 50 Hz as EEG
signal and can have higher amplitude in millivolts.
 The intensity and types of movements can vary person to
person making it most versatile artifacts. 12

13. Motivation
Motion artifacts intrude significant challenges on diagnosing
patients with critical neurological illness accurately.
The contaminated EEG signal can cause wrong detection,
prediction even miss the medical emergency.
Deterioration of BCI system performance during practical
applications
13

14. Literature review
14
Reference Dataset Activities Preprocessing
technique
Main
method
Performance
metrics
Novel Approaches for the
Removal of Motion Artifact
from EEG Recordings
Online By pulling
electrode
leads
N/A MTV(multiresoluti
on total
variation)and
MWTV(multiresol
ution weighted
total variation)
Average △SNR - 29.12dB(MTV) &
29.29db(MWTV)
Average η -
68.56% (MTV) & 67.51%(MWTV)
The Impact of Head
Movements on EEG and
Contact Impedance: An
Adaptive Filtering Solution for
Motion Artifact Reduction
Generated Head
movements
Stop band filter-
49-51Hz(3rd
order
butterworth
filter)
High pass filter-
0,194Hz(1st order
butterworth
filter)
Adaptive filtering MCAF result-high for D scores &
low for S scores (from the graph)
Motion artifact removal from
single channel
electroencephalogram signals
using singular spectrum
analysis
Online By pulling
electrode
leads
Bandpass
(0.5-30hz)
2nd order
butterworth
SSA-techniques Comparing with EEMD-CCA
computation complexity-six times
lesser
Percentage reduction-
11.388%
SNR-0.915dB
“The use of ensemble
empirical mode
decomposition with canonical
correlation analysis as a novel
artifact removal technique,”
Online By pulling
electrode
leads
NA ensemble
empirical mode
decomposition
with canonical
correlation
analysis
average ∆ SNR of 8.2 dB and
artifact was reduced by 52.3 %
yielding correlation of 0.63.

15. Literature review
Reference Dataset Activities Preprocessing
technique
Main method Performance metrics
EEG mobility artifact
removal for
ambulatory
eplieptic seizure
prediction
application
Generated Walking in
different speeds
bandpass(0.5-64hz)
Notch filter(50hz)
Infomax ICA Improvement in-
SNR(11dB)
RMSE(49mv)
Correlation-
77%
coherence-95%
For cleaned signal compared to
contaminated signal
Gaussian
Elimination-Based
Novel Canonical
Correlation Analysis
Method for EEG
Motion Artifact
Removal.
Online Seizure predicti
on and detection
from 23 pediatric
epilepsy patients
NA Cascaded method
EEMD-GECCA-SWT
Compared to CCA-
18% faster
accuracy-63.227%
Real-time Motion
Artifact Detection
and Removal for
Ambulatory BCI.
Generated Walking Kalman filtering
algorithm
ICA decomposition &
stride based template
regration
Accuracy - up to 93%
Removing Head
Movement Artifact
from EEG by ICA
Analysis and
Filtering
Generated Walking Bandpass(0.1-50hz)
4th order butterworth
filter
ICA with high pass
filter(3hz)
4th order butterworth
Higher theta and alpha power
than the ICA methods
Proposed Algorithm Generated Upper and lower
body
movements
PLI and offset
removal
Mother wavelet
transform “Haar” at
L=4
Motion artifact reduction 23.64%
15

16. Objective
To generate a reliable well defined dataset which confirms
the presence of motion artifact contaminated EEG signal
using EMOTIV along with a general algorithm to minimize
the effect of motion artifacts in EEG signals on MATLAB
platform.
16

17. Methodology
Study Approach:
17

18. Basic structure of designed experiment
18

19. Experiment design:
19

20. Activitiesperformedduring our experiment
20
Head tilting Head shaking Head nodding
Stand up & sit down Leg trembling Walking

21. Activitiesperformedduring our experiment
21
Benting Talking Walking stairs

22. Proposed block diagram
22
SWT
Coeff
Fs (Hz) D1 D2 D3 D4 A4
Freq Band
(Hz)
128 32-64 16-32 8-16 4-8 0-4
Gamma beta Alpha Theta Delta

23. Result & Discussion
Correlation coefficient analysis
23
Activities
Accelerometer data (for open)
Head
tilting
Head
shaking
Head
nodding
Stand up
and sit
down
walking Talking Leg
trembli
ng
Benting Walking
stairs
Head
tilting 3.96% 8.59% 4.08% 9.24% 2.80% 11.57% 11.54% 0.43% 23.38%
Head
shaking
11.46
% 4.39% 2.28% 1.36% 9.43% 3.85% 4.65% 12.04% 14.69%
Head
nodding 7.75% 9.61% 2.09% 3.29% 7.32% 5.59% 6.29% 6.91% 6.99%
Stand up
and sit
down 4.45% 16.30% 0.42% 11.43% 4.94% 10.88% 2.43% 4.38% 22.50%
Walking
0.16% 2.16% 0.51% 4.27% 4.18% 13.23% 0.92% 3.77% 2.52%
Talking 2.63% 4.50% 7.86% 1.65% 12.73% 1.25% 2.36% 23.64% 6.38%
Leg
trembling
4.19% 2.61% 5.37% 11.49% 6.39% 4.45% 5.58% 17.51% 1.40%
Benting
1.44% 0.39% 3.12% 12.58% 3.18% 3.58% 12.78%
4.08%
7.95%
Walking
on stairs
12.58
% 2.80% 7.61% 1.22% 3.64% 15.59% 24.34% 9.66% 10.00%

24. Coherence coefficient analysis
EEG vs ACC
With electrode
Accelerometer data(open)
Relax Head
tilting
Head
nodding
Head
shaking
Stand up
& sit down
walking Taking Leg trembling Benting Walking
stairs
relax_O 34% 30% 39% 34% 35% 32% 32% 34% 34% 34%
relax_C 28% 35% 38% 33% 39% 32% 37% 31% 31% 32%
headtilt_O 39% 35% 35% 37% 32% 31% 36% 33% 39% 40%
headtilt_C 36% 37% 42% 32% 36% 38% 34% 32% 34% 35%
headnodding_O 32% 33% 31% 36% 37% 34% 36% 33% 29% 37%
headnodding_C 35% 29% 34% 35% 32% 36% 37% 34% 37% 35%
headshake_O 34% 32% 36% 36% 39% 36% 35% 36% 35% 32%
headshake_C 31% 35% 39% 37% 38% 34% 35% 34% 32% 33%
susd_O 28% 33% 33% 31% 35% 32% 33% 31% 36% 42%
susd_C 33% 38% 35% 34% 34% 32% 35% 36% 34% 31%
walk_O 34% 36% 34% 35% 38% 40% 34% 35% 33% 38%
walk_C 32% 42% 40% 37% 39% 34% 33% 34% 35% 31%
talking_O 32% 35% 34% 29% 37% 34% 34% 32% 36% 37%
talking_C 32% 30% 35% 34% 35% 33% 36% 33% 36% 32%
legtrembling_O 35% 39% 39% 35% 29% 33% 35% 37% 34% 38%
legtrembling_C 34% 34% 33% 37% 39% 35% 33% 33% 38% 33%
benting_O 36% 36% 33% 34% 33% 33% 33% 35% 36% 35%
benting_C 33% 34% 30% 33% 31% 27% 36% 33% 35% 31%
walkingstairs_O 33% 35% 36% 35% 34% 30% 40% 35% 35% 38%
walkingstairs_C 32% 33% 37% 30% 36% 33% 36% 36% 35% 35%

25. Coherence analysis
EEG vs ACC
Without
electrode
Accelerometer data(close)
Relax Head
tilting
Head
nodding
Head
shaking
Stand up
& sit
down
walkin
g
Taking Leg
trembling
Benting Walking
stairs
relax_O 36% 35% 38% 36% 36% 37% 36% 29% 32% 38%
relax_C 38% 33% 42% 39% 34% 18% 18% 33% 36% 74%
headtilt_O 38% 33% 37% 35% 35% 28% 31% 34% 29% 52%
headtilt_C 38% 33% 34% 37% 36% 23% 22% 31% 28% 51%
headnodding_O 37% 32% 40% 35% 39% 32% 31% 33% 34% 53%
headnodding_C 33% 35% 36% 33% 33% 31% 30% 36% 34% 43%
headshake_O 32% 33% 35% 34% 34% 31% 34% 36% 41% 33%
headshake_C 38% 38% 35% 32% 29% 35% 34% 35% 28% 35%
susd_O 39% 37% 40% 33% 36% 23% 21% 31% 37% 67%
susd_C 34% 32% 34% 41% 36% 18% 16% 30% 33% 59%
walk_O 37% 33% 30% 45% 34% 28% 15% 34% 34% 18%
walk_C 32% 37% 40% 33% 37% 35% 35% 31% 35% 30%
talking_O 32% 35% 34% 32% 33% 41% 43% 35% 37% 29%
talking_C 37% 35% 31% 43% 34% 26% 16% 32% 32% 33%
legtrembling_O 34% 32% 32% 34% 32% 35% 32% 34% 30% 35%
legtrembling_C 33% 41% 29% 31% 37% 33% 43% 40% 32% 35%
benting_O 34% 28% 33% 41% 29% 32% 32% 33% 28% 36%
benting_C 35% 36% 39% 34% 35% 26% 29% 30% 29% 62%
walkingstairs_O 36% 35% 38% 36% 36% 37% 36% 29% 32% 38%
walkingstairs_C 38% 33% 42% 39% 34% 18% 18% 33% 36% 74%
25

26. EEG rhythm
Subjects Relax data
DELTA THETA ALPHA BETA GAMMA
1 O 89.3634 1.0441 0.2276 0.7119 8.8269
C 87.4802 0.8797 0.5501 1.1942 9.9465
2 O 90.4495 4.5526 1.9744 1.9147 1.6771
C 87.3703 2.0642 2.3602 4.1362 4.2501
3 O 91.8342 2.6635 2.1411 2.3912 1.1317
C 99.6429 0.1583 0.0906 0.0971 0.0312
4 O 89.8294 0.6937 0.2297 0.7819 8.4947
C
87.053 0.6943
0.2007
0.7819 11.5838
26

27. EEG rhythm
EEG Rhythm
Activities
Head
tilting
Head
shaki
ng
Head
noddin
g
Stand
up and
sit
down
walking Talkin
g
Leg
trembling
Benting Walking on
stairs
delta O 97.33 96.67 97.57 98.41 97.67 98.09 95.68 96.68 96.95
C 95.36 97.17 96.98 98.96 94.73 95.86 84.76 97.17 93.62
theta O 1.17 1.148 0.81 0.74 0.87 0.32 1.02 0.54 0.87
C 1.51 0.81 0.63 0.32 1.38 0.55 1.97 0.24 3.43
Alpha O 0.48 0.49 0.42 0.23 0.41 0.27 0.72 0.34 0.53
C 1.34 0.83 1.08 0.16 0.55 0.78 6.36 0.29 1.84
beta O 0.62 0.81 0.56 0.28 0.46 0.34 0.86 0.43 0.79
C 0.75 0.55 0.67 0.23 1.59 0.67 3.37 0.70 1.03
gamma O 0.59 0.67 1.09 0.43 0.69 1 1.83 2.59 0.63
C 1.14 0.72 0.71 0.37 1.9 2.2 3.51 1.56 0.45
27

28. Qualitative analysis
28
Clean EEG(blue) and contaminated EEG signal(red)

29. 29
motion artifact Contaminated EEG signal
cleaned signal
Qualitative analysis (Cont..)

30. Performance Evaluation
Subjects
Performance evaluation
(% of Artifact Reduction)
open close
1 23.6439 12.6318
2 17.5281 7.1051
3 12.7701 23.4084
4 13.9034 9.02 30

31. Motion Artifact Templates
31

32. 32
Motion Artifact Templates

33. Conclusion
 Clean signal is not totally free from contamination.
 To characterize the artifacts the duration of each artifact has
to be longer.
 To characterize the artifact extracting the motion artifact from
EEG signal, controlled environment is a must.
 Accelerometer does not record the same motion artifact
signal due to accelerometer placement.
 Can not be depended on single device.
 Different activities demands different approach to evaluate.
33

34. Future work
 Artifact templates can be created artificially by extracting and
taking the envelope from neural signal for the ambulatory
activities. Then the artifact templates can be created into
different amplitude to do further study.
 Using the datasets the same performance parameters can be
reevaluated for the existing paper related to Ambulatory EEG.
 The data recorded without electrodes can be compared with
accelerometer data and study on this approach for new
papers.
 Online extension tool can be created to check performance
parameter for any trial method by improving and including
number of trials for motion artifacts. 34

35. 35