An Eigen Based Feature on Time-Frequency Representation of EMG

Direk Sueaseenak1,3, Theerasak Chanwimalueang2, Manas Sangworasil1, Chuchart Pintavirooj1

1Department of Electronics, Faculty of Engineering,King Mongkut’s Institute of Technology Ladkrabang, Bangkok, Thailand

2Biomedical Engineering Programme, Faculty of Engineering, Srinakharinwirot University, Nakhon-Nayok, Thailand

3Faculty of Medicine, Srinakharinwirot University, Nakhon-Nayok, Thailand

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Direk Sueaseenak, Theerasak Chanwimalueang, Manas Sangworasil, Chuchart Pintavirooj, “An Eigen Based Feature on Time-Frequency Representation of EMG ” IEEE-RIVF 2009, Danang University of Technology, VietNam, July 13-17, 2009

Publication & Present

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Introduction to our research1

Goal and objective of research2

SEMG Acquisition System3

Outline

SEMG BSS4

Feature Extraction5

Conclusion6

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Biomedical,Image,Signal and System ( Biosis LAB )

Assoc.Prof.Dr.Chuchart Pintavirooj Assoc.Prof.Dr.Manas Sangworasil

MemberM.Eng 10 คนPh.D 6 คน

I S

Mini CT

Image reconstruction

Face & fingerprintrecognition

UCT

EMG Analysis and Recognition

Infant Incubator

EEG and BCI

ECG monitor

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EMG Control Prosthesis Research TeamFaculty of Engineering (KMITL)

Faculty of Engineering(SWU)

Faculty of Medicine (SWU)

Direk Sueaseenak (SWU+KMITL)

Chuchart Pintavirooj

Manas Sangworasil

Niyom Laoopugsin

Theerasak Chanwimalueang

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Multi-channel EMG Pattern Classification(M.Eng Thesis)

4x4 EMG Sensor 16 channel EMG 16 ch Raw EMG 16 ch FFT EMG

∑ Area from 16 channel

Spline InterpolationEMG Pattern

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Hand Close

Wrist extension

Wrist flexion

Wrist pronation

Hand open

Wrist supination

Radial flexion Ulnar flexion

Hand Movement and EMG Pattern

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Muscular contraction % Accuracy

1.Wrist extension 100%

2.Wrist flexion 33%

3.Wrist pronation 53.3%

4.Hand closed 86.7%

5.Radial flexion 93.3%

6.Ulnar flexion 93.3%

7.Wrist supination 93.3%

8.Hand open 100%

Classification Result

Disadvantage System complexity

Impossible in real application

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Goal of Research (Ph.D research)

Portable EMG Signal Acquisition and Pre-

processing

EMG Feature Extraction

EMG Classification

Minimum :EMG Measurement Channel

Maximum :Accuracy Rate of EMG Classification

No complexity for Real Application

Mechanical Control

Feedback Control

EMG Surface Electrode EMG Acquisition System FAST ICA Separation

Time-Frequency Analysis Eigen based Feature Extraction

Feature 1 Feature 2 STFT ICA 1 STFT ICA 2

Object of Research

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Surface EMG Acquisition And Measurement

System

Direk Sueaseenak, Theerasak Chanwimalueang, Manas Sangworasil, Chuchart Pintavirooj, “PSOC-BASED MULTICHANNEL ELECTROMYOGRAM

ACQUISITION SYSTEM WITH APPLICATION IN MUSCULAR FATIGUE ASSESSMENT” Proceedings of ThaiBME2007, vol.1, pp. 110-114,2007.

Publication

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Surface EMG Acquisition System

SurfaceElectrode

Instrumentation Amplifier

PSOC MCU (PGA,ADC,UART)

EMG Recorder

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Channel 1 Flexor carpi radialis

Channel 2 Flexor carpi ulnaris

SWAROMED Al/AgCl Electrode

Surface EMG Placement

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SEMG Signal

Channel 1 Channel 2

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SEMG Blind Source Separation“Independent Component

Analysis”

Direk Sueaseenak, Theerasak Chanwimalueang, Manas Sangworasil, Chuchart Pintavirooj, “An Investigation of Robustness in Independent Component

Analysis EMG” Proceedings of ECTI-CON2009, vol.2, pp. 1102-1105,2009.

Publication

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Blind Source Separation : Cocktail Party Problem

The mathematical mode l of CPP :X1(t)=A11S1+A12S2

X2(t)=A21S1+A22S2

x = As

s = Wx (1)

(2)

(3)

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SEMG Blind Source Separation

ICACh1

Ch2

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The mathematical mode l of CPP :X1(t)=A11S1+A12S2

X2(t)=A21S1+A22S2

x = As

s = Wx (1)

(2)

(3)

“Nongaussian is Independent”: Central Limit Theorem

x = As s = Wx www.bmekmitl.org

X1(t)=A11S1+A12S2

X2(t)=A21S1+A22S2

Measures of Nongaussianity

By kurtosis

Subgaussian

Supergaussian

• Subgaussian kurtosis < 0

• Superguassian kurtosis > 0

• Gaussian kurtosis = 0

(4)

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Initialize W (Set the weight vector to random values) Newton 's method (until convergence)

Normalization

G(u)=u3(5)

Process of ICA

s = Wx (6)

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SEMG BSS Result Channel 1 Channel 2

ICA 1 ICA 2

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SEMG Time-Frequency Analysis

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Short-Time Fourier Transform

(7)

Source: http://www.clecom.co.uk www.bmekmitl.org

NnkjN

n

etnWnxktSTFT /21

0

)()(),(

STFT Result

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Eigen based Feature Extraction

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Concept of Moment

dxdyyxpyxnmM nm ),(),(

dxdyyxpyxnmU ny

mx ),()()(),(

(8)

(9)

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Concept of Moment(cont.)

J

j

K

k

nj

mk kjfyxnmM

1 1

),()()(),(

J

j

K

k

njj

mkk kjfyyxxnmU

1 1

),()()(),(

0010 /mmx 0001 /mmy

Where

(10)

(11)

(12)

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)2,0()1,1(

)1,1()0,2(

uu

uuU

Concept of Moment(cont.)

UEET

EMG Features =

21 /

(13)

(14)

(15)

2

1

0

0

,2221

1211

ee

eeE

(16)

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Eigen Feature Extraction Result

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ICA-applied EMG

without ICA-applied EMG

AVG SD AVG SD

Wrist flexion 2.2743 0.2379 1.9630 0.4652

Relaxation 1.5695 0.3214 1.530 0.4718

Quantitative measurement of robustness of ICA application

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Conclusions We used a multi-channel electromyogram acquisition

system from previous work to acquire two channel surface electrodes on forearm muscles. and performed a

blind signal separation by using an independent component analysis (ICA) technique.

We purposed the novel features extraction for the EMG contraction classification. Our features are based on Eigen-vector approach. The time-frequency analysis is applied on the time-frequency magnitude spectrum of

the Independent component analysis EMG. The ratio between the two Eigen values are the novel features.

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Simple EMG Robotic Control Experiment

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Acknowledgment

Office of the Higher Education Commission

Faculty of MedicineSWU

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