Daniela De Venuto, V. F. Annese, G. E. Biccario
Dipartimento di Ingegneria Elettrica e dell’Informazione, Politecnico di Bari,
Italy.
M. de Tommaso, E. Vecchio
Basic Medical, Neuroscience and Sensory System Department (SMBNOS)
Aldo Moro University, Bari, Italy
A. L. Sangiovanni Vincentelli
Department of EECS, University of California at Berkeley (CA) USA.
Combining EEG and EMG
Signals in a Wireless System
for Preventing Fall in
Neurodegenerative Deseases
FORITAAL 2014 – Catania 2-5 September 2014
Agenda
Introduction and Motivations
Premotor potential and µ-Rythm
EEG and EMG Signal Analysis
Wireless System Architecture
Conclusions and Future Improvements
FORITAAL 2014 - Daniela De Venuto - Catania 2-5 September 2014
The Risk of Fall in
Neurodegenerative Deseases
Each year, one out of three 65 aged or older adults fall
• Main cause of the most
common fractures (spine,
hip, forearm, leg, ankle,
pelvis, upper arm, and hand)
• 2010: 2.3 million non-fatal
fall injuries among older
adults were treated in
emergency.
• Brain injuries (TBI)
• 2010: the medical costs
related to falls was $36.4
billion.
• Fear of falling that actually
increases fall probability
• The medical costs prevision
for 2020 is $ 61.6 billion.
FORITAAL 2014 - Daniela De Venuto - Catania 2-5 September 2014
Premotor Potential (BP) vs µ-Rythm
Premotor Potential (BP)
• BP is a slow EEG positive component
in a time analysis that increases
progressively appearing even 1-2s
before the voluntary movement onset.
• Frequency band : 2-5 Hz
• Maximum 100-200ms before the
movement beginning
• More visible in contralateral
hemisphere to limb involved in the
movement.
µ-Rythm
• µ-rhythm, are synchronized patterns
of electrical activity involving large
numbers of neurons, in the brain area
that controls voluntary movement,
during a “movement steady state”.
• Frequency band 7.5–12.5 Hz
(primarily 9–11Hz)
• 2 sec before the movement onset
FORITAAL 2014 - Daniela De Venuto - Catania 2-5 September 2014
State of the Art
No studies in literature report about a WEARABLE, WIRELESS, NON
INVASIVE system synchronizing EEG and EMG for fall PREVENTION
• Combination of accelerometers and gyroscopes for detecting movements and body
position, 300ms before a fall
• New inertial sensor can detect fall 700ms before impact.
• Brain Computer Interface (BCI) devices for motor imagery use electroencephalograph
(EEG) to recognize the beginning of movements
• EMG has been studied for artificial limb control in order to increase power strength
and, in particular, only after movement is started
FORITAAL 2014 - Daniela De Venuto - Catania 2-5 September 2014
EEG and EMG Signal Analysis:
SET UP
EMG
EEG
+
• 8 Channels
• Monitored muscles
(both legs):
Rectus femoralis,
Abductor Femoralis,
Gastrocnemius,
Tibialis
•
256 samples per
sec
• Gain: ≈ 10 kV ∙ V-1
• Band pass filtered:
1 – 200 Hz
• 19 channels ( +
reference, +
ground)
• According to the
10-20 electrodes
international
standard
• 256 samples per
sec
• Gain ≈ 10 kV ∙ V-1
• Band pass
filtered:
1 – 200 Hz
FORITAAL 2014 - Daniela De Venuto - Catania 2-5 September 2014
EEG and EMG Signal Analysis:
Time Domain Analysis
Time domain signals are strongly biased by noise and artifacts (eye movement, head
movement, wires movements, etc etc): premotor potential is hard to detect in a realtime approach.
F3
Cz
C3
Right Tibialis
Right
Gastrocnemius
FORITAAL 2014 - Daniela De Venuto - Catania 2-5 September 2014
EEG and EMG Signal Analysis:
Time Domain Analysis
Signals are averaged and/or subtracted to better reduce the common noise and to
underline the premotion potential
FORITAAL 2014 - Daniela De Venuto - Catania 2-5 September 2014
EEG and EMG Signal Analysis:
Frequency Analysis
FFT in discrete windows of 1 second, show the the different spectral components for
the same channel in different conditions: rest (no movements), premotion, motion.
The contribution in the 1-5 Hz band is very high one second before the movement:
the subject is processing the movement.
FORITAAL 2014 - Daniela De Venuto - Catania 2-5 September 2014
EEG and EMG Signal Analysis:
Time - Frequency Analysis
Time–Frequency analysis for C3-T7 and C3-O2 from subjects doing different tasks: A:
resting, B: lifting the right leg, C:balancing on a tilting platform, D: starting a short
walk. Presence or absence of mu-rhythm and 2-4 Hz waves are highlighted in orange
circle. Red and green signals are respectively EMG and signal considered in timedomain.
FORITAAL 2014 - Daniela De Venuto - Catania 2-5 September 2014
Wireless System Architecture: Overview
FORITAAL 2014 - Daniela De Venuto - Catania 2-5 September 2014
Wireless System Architecture: WBAN
EEG node
868 MHz
antenna
INA
50 V/V
EEGx+
EEGBP Filtering
0,5-100 Hz
100 V/V
M
u
x
12x1
ADC
μC
RF
module
24 Bit
80 ksps
EMG node
868 MHz
antenna
INA
50 V/V
EMGx+
EMGxBP Filtering
10-500 Hz
50 V/V
M
u
x
μC
RF
module
8x1
12 Bit
80 ksps
FORITAAL 2014 - Daniela De Venuto - Catania 2-5 September 2014
Wireless System Architecture: Receiver
868 MHz
antenna
Receiver node
uController
&
Processing
Unit
μC
RF
module
LPF
MSK
signal
LNA
0
90
UMTS
module
USB
module
Sampler
Th
LO
Th
M
u 010…
x
awg noise
N0,i = kT [W/Hz]
LPF
Sampler
Model of the receiver for BER evaluation
BER vs SNR in the receiver
FORITAAL 2014 - Daniela De Venuto - Catania 2-5 September 2014
Wireless System Architecture:
the Wearable Electrodes
Printed electrode in gold
on Polycaprolactone (PCL)
Printed Antenna
Gold area for skin contact
SMD components
implemented using
conductiove glue
Printing in gold on PCL
examples
FORITAAL 2014 - Daniela De Venuto - Catania 2-5 September 2014
Wireless System Architecture: Feedback
Local
Feedback
It is possible to
avoid the fall
through:
•
•
Electro –
stimulation
Alerting
signal
External
Feedback
Fall
Prediction
FORITAAL 2014 - Daniela De Venuto - Catania 2-5 September 2014
The nearest
hospital will be
informed of the
incident. In this
way it is possible
an early and
optimized
intervention
Conclusion and Challenges
Power
Managment
Optimize
Biofeedback
Wearability
Dry
Electrode
Decision
Algorithm
Optimize
Fall
Detection
FORITAAL 2014 - Daniela De Venuto - Catania 2-5 September 2014
Thanks for your Attention,
Questions ?
Daniela De Venuto,
Politecnico di Bari,
Dep. of Electronics and Informatic Engineering
Email: daniela.devenuto@poliba.it
FORITAAL 2014 - Catania 2-5 September 2014