electroencephalography and application

I HC QUC GIA TP.HCM

TRNG I HC BCH KHOA

KHOA KHOA HC & K THUT MY TNH

THC TP TT NGHIP

PHN TCH SNG NO EEG S DNG TRONG IU KHIN N GIN

GVHD: ThS. V Thanh Hng

GVPB: TS. L Thnh Sch

SVTH 1: Phan Trn Ngc An (51000019) SVTH 2: V Kim Long (51001787)

TP. H CH MINH, THNG 12/2014

Thc tp tt nghip Phn tch sng no EEG

1

TM TT TI

Vi trnh pht trin khoa hc k thut hin nay, vic nghin cu tm hiu v nhng hot ng no b ca con ngi lun l vn thu ht s tm hiu ca rt nhiu nghin cu khoa hc. Nhng ng dng vi ti ny c th thy rng ri trong x hi hin nay nh: pht hin cc loi bnh l lin quan v thn kinh, iu khin t ng thng qua ngh, iu tra tm l ti phm

Qua , nhm sinh vin nghin cu v ti ny vi mong mun pht trin h thng phn loi da trn sng no, gp phn pht trin trong lnh vc nghin cu sng no.

Mc tiu ca ti l phn loi nh phn hnh vi ca con ngi thng qua d liu in no . Qu trnh thc hin ti thng qua ba bc tm hiu: Tm hiu v sng no EEG, Tm hiu v c trng sng no v cc phng php trch xut c trng, Tm hiu cc gii thut hc my dng cho phn loi sng no.

Tm hiu c trng sng no s a ra cc c trng c bn ca sng no, sau tp trung tm hiu vo cc bin i n gin v ph bin hin nay nh Fourier Transform v Wavelet Transform.

Phn tm hiu gii thut hc my s nghin cu cc thut ton hc my dnh cho phn loi hay c dng nh Nave Bayes, Support Vector Machine, K Nearest Neighbors

Sau khi tm hiu v nhng phng php bin i, thut ton, nhm sinh vin bt u hin thc h thng trn nn tng Java vi s h tr ca th vin Weka. H thng dng tp d liu u vo qua nhng phng php bin i c bn ri a vo nhng gii thut phn loi c trn Weka xut ra kt qu. Trong qu trnh trn, vic chn lc, trch xut d liu sao cho ph hp s c thc hin lin tc nhm ti u ho kt qu cui cng.

Ni dung bi bo co bao gm 7 chng:

Chng I: Gii thiu ti

Chng II: Tng quan v sng no EEG

Chng III: c trng sng no Trch xut c trng

Chng IV: Phn loi sng no bng hc my

Chng V: Thit k h thng

Chng VI: Kim th nh gi kt qu

Chng VII: Tng kt


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Mc Lc TM TT TI ....................................................................................................................................... 1

CHNG I: TNG QUAN V TI ..................................................................................................... 4

CHNG II: TNG QUAN V SNG NO EEG .................................................................................... 5

2.1. in no (Electroencephalography EEG) l g ? ........................................................................ 5

2.2. EEG nh th no ................................................................................................................................. 5

CHNG III: C TRNG SNG NO TRCH XUT C TRNG ............................... 8 3.1. c trng sng no ............................................................................................................................. 8

3.1.1. Delta ............................................................................................................................................ 8

3.1.2. Theta ............................................................................................................................................. 8

3.1.3. Alpha ........................................................................................................................................... 9

3.1.4. Beta ............................................................................................................................................. 9

3.1.5. Gamma ........................................................................................................................................ 9

3.2. Cch trch xut c trng ................................................................................................................. 10

3.3. Cc thut ton bin i ..................................................................................................................... 10

3.3.1. Bin i Fourier ......................................................................................................................... 10

3.3.2. Bin i Fourier thi gian ngn ................................................................................................. 11

3.3.3. Bin i Wavelet ....................................................................................................................... 12

CHNG IV: PHN LOI SNG NO BNG HC MY .................................................... 14 4.1. Machine Learning v phng php phn loi .................................................................................. 14

4.2. Mt s thut ton phn loi cho EEG ............................................................................................... 14

4.2.1. SVM .......................................................................................................................................... 14

4.2.2. NaiveBayes ................................................................................................................................ 16

4.2.3. K hng xm gn nht (K- Nearest Neighbors) ........................................................................... 17

CHNG V: THIT K H THNG .......................................................................................... 19 5.1. M t d liu (dataset) ...................................................................................................................... 19

5.2. Gii thiu Weka ............................................................................................................................... 19

5.3. S dng Weka trn cc tp d liu ................................................................................................... 20

5.3.1. Khi to Instances ...................................................................................................................... 21

5.3.2. S dng Filter trn tp d liu ................................................................................................... 21

5.3.3. Khi to Classifier ..................................................................................................................... 22

5.3.4. Khi to i tng qua Evaluation ............................................................................................ 22

5.3.5. S dng attribute selection ......................................................................................................... 22

5.4. Cu trc chng trnh ....................................................................................................................... 23

5.4.1. Giai on arff converter ............................................................................................................. 23


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5.4.2. Giai on filter thuc tnh .......................................................................................................... 23

5.4.3. Giai on Feature Selection ....................................................................................................... 23

5.4.4. Giai on Classification ............................................................................................................. 23

5.5. Giao din chng trnh ...................................................................................................................... 24

CHNG VI: KIM TH NH GI KT QU ..................................................................... 26 6.1. nh gi chnh xc v hiu sut i vi cc gii thut ............................................................... 26

6.2. nh gi chnh xc v hiu sut khi dng phng php Feature Selection ................................. 26

CHNG VII: TNG KT .......................................................................................................... 28 7.1. Kt qu t c .............................................................................................................................. 28

7.2. Phng hng pht trin .................................................................................................................. 28

Ti liu tham kho ......................................................................................................................... 29


4

CHNG I: TNG QUAN V TI

T xa n nay, vn nghin cu v no b con ngi lun l mt ti c sc hp dn rt ln v thu ht nhiu s ch ca cc nh khoa hc trn th gii. Vic nghin cu v cch thc hot ng ca no b s gip ch rt ln cho con ngi trong rt nhiu lnh vc: sinh hc, y hc, tin hc, an ninh, thng mi

Tuy nhin, vic nghin cu hot ng no b l phm tr v cng ln v cha c mt nghin cu khoa hc no c th hon ton tm hiu c ht nhng tim nng ca no b.

Mt trong nhng nghin cu v no b hin nay, Electroencephalography EEG, hay cn gi l in no l mt trong nhng cch tip cn vi no b thng qua vic o cc xung in th do no pht ra. Bng vic nghin cu ngha ca nhng dao ng in ny s lm pht trin nhng k thut cng ngh cao hin nay ca con ngi v y sinh hc nh pht hin cc cn bnh nguy him tim n,v tin hc nh cc cng ngh iu khin bng suy ngh, nhn dng cm xc, v kinh t nh tip th thng mi

Phng php nghin cu v in no l mt phng php cn mi cha nhiu thch thc v s c pht trin mnh trong tng lai gn. Vi s gip ch t ph to ln khi nghin cu thnh cng in no s a con ngi vo k nguyn mi vi nhng iu khin t ng thng qua ngh. Chnh v vy, nhm sinh vin quyt nh chn ti ny gp phn chung vi cng ng nghin cu tm hiu v EEG.

Mc tiu ca ti:

- Tm hiu cc thng tin c trng v sng no. - Tm hiu cc phng php rt trch c trng sng no. - Tm hiu cc gii thut quyt nh hnh vi thng qua d liu sng no t cc kho d liu

trc tuyn.

Phng hng hin thc:

ti c chia lm hai giai on chnh. giai on u, hng tip cn ch yu l v mt l thuyt. Hng nghin cu tp trung vo cc l thuyt ngha ca sng no; phng php, thut ton dng hin thc ti; so snh, chn lc cc phng php ph hp nht hin thc giai on tip theo.

giai on tip theo, nhm tp trung vo nghin cu su cc phng php x l d liu, rt trch c trng hng ti mc tiu ti u ho kt qu u ra, ng thi hin thc chng trnh hon chnh cho ti.


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CHNG II: TNG QUAN V SNG NO EEG

2.1. in no (Electroencephalography EEG) l g ?

Electroencephalography EEG l mt h thng chn on chc nng ghi li in th hot ng t v no pht ra. EEG c pht hin bi Berger nm 1924 bng mt dng c o in vi mt in cc b mt trn u con trai ng v ghi li c mt mu nhp nhng nhng dao ng in. Tn hiu ny l phn hi in sinh hc ca t bo no. in no l ng biu din s bin i tn hiu ny theo thi gian pht ra t no c pht hin da u.

Hnh 1 Tn hiu EEG[1]

2.2. o EEG nh th no ?

[1]

http://physionet.org/pn6/chbmit/


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Cc t bo no lin lc vi nhau nh to ra nhng sng in nh. Trong xt nghim in no , nhiu in cc c t vng da u ng vi nhiu vng khc nhau ca no nhm pht hin v ghi nhn cc kiu hot ng in cng nh tm kim nhng bt thng.

Xt nghim c thc hin bi cc k thut vin o in no trong mt phng thit k c bit ti bnh vin hoc phng khm. Ngi bnh c th nm trn bn hoc ngi trn gh da.

K thut vin s t 16 n 25 a kim loi dt (l cc in cc) ln vng da u nhng v tr khc nhau . Nhng a kim loi ny c c nh bi mt ming dnh. Cc in cc dng trong in no thng l nhng a kim loi, da u ch t in cc c bi kem dn in, sao cho in tr gia da u vi in cc khng vt qu mt ngng no , thng l khng qu 5 Kilo-Ohms. Nu lm sch da u tt, cng c th khng dng kem dn in trn in cc ghi, m dng ming xp tm dung dch mui. Ngi ta cng hay dng loi m cao su c gn sn in cc, v t trm ln u ngi bnh.

Cc in cc c ni vi mt my khuch i v mt my ghi. My ghi chuyn i cc tn hiu in thnh mt chui cc sng v v chng ln giy ghi chuyn ng lin tc. ng biu din ghi c thng di, c nhng sng dng v m ty theo sng nm trn hay di ng nm ngang.

T biu cc bc s quan st tn s, bin , hnh dng, tnh u n, v tr ca cc sng bit c no hot ng bnh thng hay c bnh l. Ngi bnh cn phi nhm mt, v nm yn v bt k mt c ng no cng c th lm thay i kt qu. Trong qu trnh lm xt nghim, ngi bnh c th phi thc hin mt s yu cu nh th su v nhanh trong vi pht hoc nhn vo nh n ang chp tt.

Trc khi ghi in no , cn thc hin vic o chun (calibration) m bo l my s cho ng ghi chnh xc. Sng ghi chun cung cp cho ta gi tr so snh bin cc sng in no. Ngi ta dng mt xung in hnh ch nht, hnh tam gic, hay hnh sin, c bin bit trc, a vo u vo ca b phng i ca my ghi in no . Nh vy tn hiu chun s i vo tt c cc ng ghi EEG, to ra mt sng chun trn bn ghi. Cn c vo sng chun ny, ngi ta nh gi cc sng in no v mt bin .[1]


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Hnh 2.1 Mt dng c o in no n gin[1]

[1]

www.mimas.com.vn/portfolio/giai-ma-thong-tin-trong-song-dien-nao-he-thong-nhan-dien-cam-xuc-con-nguoi/


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CHNG III: C TRNG SNG NO, TRCH XUT C

TRNG

Sng no cn x l c d liu u vo rt ln, vi mi hnh ng c trng ca con ngi nh nhm m mt, hot ng c u thu c mt lng tn hiu ln t vi trm n hng ngn tn hiu.

Do vy, vn tm ra c trng sng no l v cng cn thit d liu th u vo c trch xut thnh nhng d liu c trng c ngha, gim s chiu ca vector khi a vo b my phn loi thc thi.

Chng III s trnh by mt s c trng ca sng no cng cc phng php trch xut ph bin.

3.1. c trng sng no

EEG thng c chia thnh rhythmic v transient. Cc rhythmic s c chia thnh cc

di tn s khc nhau (khong t 1-30Hz, hu ht cc tn hiu no EEG u trong khong

ny). Di tn s ny thng c trch xut bng phng php quang ph. Mt s di tn

s lin tc s c t chung 1 nh danh, nh :

- Tn s 32: Gamma

3.1.1. Delta Delta l tn s dao ng ln n 4Hz. N c bin cao nht v l sng chm nht. N xut hin bnh thng ngi ln v tr s sinh. N c th pht hin ra cc tn thng di v no, bnh trn dch no. N thng xut hin pha trc u ngi ln ( FIRDA - Frontal Intermittent Rhythmic Delta) v vng sau u tr em ( OIRDA - Occipital Intermittent Rhythmic Delta).

Hnh 3.1 Sng Delta[1]

3.1.2. Theta

Theta l di tn s 4-7Hz. Theta c th thng thy tr nh. N c th thy trong nhng lc bun ng v lc hng phn tr em v ngi ln. Theta vt mc th hin cho s bt thng, ri lon tm thn.

[1]

http://www.germ-a.com/?p=1310


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Hnh 3.2 Sng Theta[1]

3.1.3. Alpha

Alpha l di tn s t 7-14Hz. y l sng vng sau, c th thy c cc vng sau c 2 v no tri v phi. N tng mnh vi s ng li ca mt hoc lc th gin, suy gim khi m mt hoc lc cng thng tinh thn.

Hnh 3.3 Sng Alpha[2]

3.1.4. Beta Beta l di tn s t 15Hz-30Hz. N c thy hai na bn no v th hin r nht pha chnh din u. Beta gn lin vi hnh vi vn ng, thng b suy gim khi thc hin hot ng di chuyn. Sng beta bin thp vi nhiu tn s khc nhau thng lin quan n cc hot ng bn rn, lo lng, suy ngh Beta vi cc tn s khc nhau thng lin quan n cc bnh l khc nhau.

Hnh 3.4 Sng Beta[3]

3.1.5. Gamma Gamma l di tn s khong 30-100Hz. Nhp Gamma c cho l biu hin s lin kt ca cc mng li neuron thn kinh vi nhau khi chng thc hin mt chc nng nhn thc hay chc nng c hc nht nh.

[1]

http://en.wikipedia.org/wiki/Electroencephalography [2]

http://www.germ-a.com/?p=1310 [3]



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Hnh 3.5 - Sng Gamma[1]

3.2. Cch trch xut c trng

Tn hiu EEG gc l dng tn hiu min thi gian ( time domain signal) v nng

lng tn hiu phn b tn x. Cc c trng (features) ca tn hiu b nhiu

(noise). Do vy, trch xut cc c trng th tn hiu EEG cn c phn tch v

m t bng 1 hm thi gian tn s.

Thm vo , s chiu ca tp d liu a vo l rt cao, nh hng rt ln n

thi gian hin thc. Do vy, cn trch xut c trng chn ra nhng gi tr

thng tin cn thit cho h thng phn loi, gim s chiu vector u vo.

C rt nhiu phng php bin i c s dng rng ri. y, ta nghin cu

hai bin i thng dng, d hiu v ph bin nht l : Fourier Transform v

Wavelet Transform.

3.3. Cc thut ton bin i

3.3.1. Bin i Fourier (Fourier Transform)

Bin i Fourier l k thut bin i tn hiu t min thi gian sang min tn s trn c s phn tch mt tn hiu thnh tng ca cc hm sin vi cc tn s khc nhau.

Bin i Fourier c xc nh bng biu thc:

( ) ( )

(3.1)

Biu thc bin i Fourier ngc:

( ) ( )

(3.2)

Trong , x(t) v X(f) c gi l mt cp bin i Fourier: ( ) ( )

[1]



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Hnh 7 Bin i Fourier

Mc d rt hiu qu nhng bin i Fourier vn c hn ch. Khi bin i sang min tn s, tn hiu thi gian b mt. Nu tn hiu khng thay i nhiu theo thi gian, iu ny khng nh hng nhiu. Nhng nu tn hiu c cha cc thng s ng (tri, nghing, bin i t ngt ), th Fourier Transform khng pht hin c.

3.3.2. Bin i Fourier thi gian ngn

Thc t, vi tn hiu EEG l tn hiu khng dng, bin i Fourier khng mang li hiu qu cao. Bin i Fourier khng phn tch c bin thin tn s trong tng vng theo thi gian tn hiu. Ni cch khc, n khng c tnh cc b v thi gian. V vy, cn cc b ho bin i Fourier phn tch cc tn hiu khng tnh.

khc phc iu trn, bin i Fourier Thi gian ngn ( Short Time Fourier Transform STFT) c xut. Bin i ny cn c gi l bin i Fourier ca s, ly tng s dng hm ca s xp x trung tm ni nh v. V vy, bin i STFT khai trin theo hai thng s tn s v dch thi gian.

Kch thc ca s trong STFT l c c nh trc. Tn hiu c phn thnh tng on bng cch nhn vi hm ca s ny, sau thc hin bin i Fourier

Hnh 8 Bin i Fourier thi gian ngn

Biu thc bin i:

( ) , ( ) ( )-

(3.3)

( ) ( ) ( )


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Nhc im: chnh xc ph thuc vo hm ca s Hm ca s c kch thc c th, bng nhau vi mi tn s, khng c s mm do trong mt s trng hp tn hiu.

3.3.3. Bin i Wavelet (Wavelet transform)

Bin i wavelet s dng 1 xung dao ng wavelet (sng nh) cho thay i kch thc v so snh vi tn hiu tng on ring bit. K thut ny bt u vi cc sng nh (wavelet) cha cc dao ng tn s kh thp, sng nh ny c so snh vi tn hiu phn tch c bc tranh ton cc ca tn hiu phn gii th. Sau sng nh c nn li nng cao dn dn tn s dao ng. Qu trnh ny gi l hm thay i t l (scale) phn tch. Khi thc hin tip bc so snh, tn hiu c nghin cu chi tit cc phn gii cao hn, gip pht hin cc thnh phn bin thin nhanh cn n bn trong tn hiu. l mc ch ca bin i Wavelet

Wavelet transform nh l p dng mt tp cc b lc thng cao v b lc thng thp. Cc b lc phi p ng : phng, trn v trc giao.

Cng thc bin i Wavelet Transform:

( ) ( )

| | .

/

(3.4)

Vi: a: thng s dch chuyn. b: thng s t l

Vi (t ) l 1 hm bnh phng kh tch bt k.

Mt s hm (t ) thng dng:

- Wavelet Haar : ( ) {

(3.5)

- Daubechies wavelet

S bin i Wavelet:

Bin i wavelet s p dng ng thi b lc thng cao v b lc thng thp vo tn hiu (Signal). Kt qu sau khi bin i wavelet s cho ra 2 thnh phn l Xp x (Approximation) v Chi tit (Detail).


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Hnh 9 Bin i Wavelet[1]

y, xp x l thnh phn t l cao tng ng vi thnh phn tn s thp, Chi tit tng ng thnh phn tn s thp, t l cao.

c im ni bt ca Wavelet transform:

- Nn tn hiu.

- Kh nhiu.

- STFT phn gii thi gian v tn s c lp vi tn s phn tch , cn

wavelet t l nghch vi .

- c tnh ca wavelet l phn gii thi gian tt tn s cao, phn

gii tn s tt tn s thp. Thch hp cho vic phn tch cc tn hiu

gm cc thnh phn tn s cao c thi gian tn li ngn v cc thnh

phn tn s thp c thi gian di.

[1]

http://www.quazoo.com/q/Discrete%20wavelet%20transform


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CHNG IV: PHN LOI SNG NO BNG HC MY

Hc my (Machine Learning) l k thut ph bin hin nay trong lnh vc tr tu nhn to. N c th dng phn loi, phn cm rt nhiu cc dng tn hiu s theo thi gian, tn hiu EEG l mt trong s .

Do ta s tm hiu v p dng mt s thut ton trong Hc my h tr vic phn loi sng no EEG trong ti.

4.1. Machine Learning v phng php phn loi

Hc my, c ti liu gi l My hc, (Machine Learning) l mt lnh vc ca tr tu nhn to lin quan n vic nghin cu v xy dng cc k thut cho php cc h thng "hc" t ng t d liu gii quyt nhng vn c th.[2]

Cc vn v hc my bao gm [3]: - M hnh ha cc hm mt xc sut iu kin: hi quy v phn loi

+ Mng n ron + Cy quyt nh + Support Vector Machine

+ K lng ging gn nht +

- M hnh ha cc hm mt xc sut qua cc m hnh pht sinh: + Thut ton cc i k vng + Cc m hnh ha gm mng Bayes v mng Markov + nh x topo pht sinh +

- Cc k thut suy lun xp x ng: + Chui Markov phng php Monte Carlo + Phng php bin thin

y, chng ta dng mt s trong phng php hc my k trn thc hin phn loi sng no EEG sau khi d liu c qua bc trch xut.

mc nghin cu n gin ca ti, phn loi y c thc hin l phn loi nh phn, ngha l vi cc s liu u vo, ch cho ra hai gi tr l 1 (yes) hoc 0 (no).

4.2. Mt s thut ton phn loi cho EEG

4.2.1. Support Vetor Machine SVM

My vect h tr (SVM - vit tt tn ting Anh: Support Vector Machine) l mt khi nim trong thng k v khoa hc my tnh cho mt tp hp cc phng php hc c gim st lin quan n nhau phn loi v phn tch hi quy. SVM dng chun nhn d liu vo v phn loi chng vo hai lp khc nhau. Do


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SVM l mt thut ton phn loi nh phn. Vi mt b cc v d luyn tp thuc hai th loi cho trc, thut ton luyn tp SVM xy dng mt m hnh SVM phn loi cc v d khc vo hai th loi . Mt m hnh SVM l mt cch biu din cc im trong khng gian v la chn ranh gii gia hai th loi sao cho khong cch t cc v d luyn tp ti ranh gii l xa nht c th. Cc v d mi cng c biu din trong cng mt khng gian v c thut ton d on thuc mt trong hai th loi ty vo v d nm pha no ca ranh gii.[4]

Mt my vect h tr xy dng mt siu phng hoc mt tp hp cc siu phng trong mt khng gian nhiu chiu hoc v hn chiu, c th c s dng cho phn loi, hi quy, hoc cc nhim v khc. Mt cch trc gic, phn loi tt nht th cc siu phng nm cng xa cc im d liu ca tt c cc lp (gi l hm l) cng tt, v ni chung l cng ln th sai s tng qut ha ca thut ton phn loi cng b.[5]

SVM bao gm cc dng tuyn tnh, bin cng, bin mm v phi tuyn

Gi s c mt tp hun luyn D sau:

{( )| * ++

(4.1)

Vi mang gi tr 1 hoc 1, xc nh lp ca im . Mi l mt vect thc p-chiu. Ta cn tm siu phng c l ln nht chia tch cc im c v cc im c . Mi siu phng u c th c vit di dng mt tp hp cc im tha mn:

(4.2)

Vi w: l mt vetor php tuyn ca siu phng cn chia

Chng ta cn chn w v b cc i ho l, tc l khong cch gia hai siu phng cn chia l ln nht.

Hnh 10 Phn loi SVM[1]

[1] http://en.wikipedia.org/wiki/Support_vector_machine


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Hai siu phng c xc nh bng:

(4.3)

v

(4.4)

Khong cch gia hai siu phng l

. V vy, phi cc tiu ho . m

bo khng c im d liu no trong l, ta thm cc iu kin sau:

hoc

Khi khng th phn chia tuyn tnh siu phng, ta cn kt hp SVM vi mt hm ht nhn (Kernel) chuyn khng gian ban u sang khng gian khc c s chiu ln hn.

Mt s hm Kernel :

Tuyn tnh: K(xi, xj)= xixj

a thc bc d: K(xi, xj)=(xixj+1)d

, vi ( )

Radial Basis Function: K(xi, xj)=exp(-(xi-xj)2), R+

4.2.2. NaiveBayes

Mng Bayes (ting Anh: Bayesian network hoc Bayesian belief network hoc Belief network) l mt m hnh xc sut dng th.

Mng Bayes l cch biu din th ca s ph thuc thng k trn mt tp hp cc bin ngu nhin, trong cc nt i din cho cc bin, cn cc cnh i din cho cc ph thuc c iu kin. Phn phi xc sut ng thi (joint probability distribution) ca cc bin c xc nh bi cu trc th ca mng. M t th ca mng Bayes dn ti cc m hnh d gii thch, v ti cc thut ton ton hc v suy lun hiu qu. [6]

Mt mng Bayes l mt th c hng phi chu trnh m trong :

cc nt biu din cc bin,


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cc cnh biu din cc quan h ph thuc thng k gia cc bin v phn phi xc sut a phng cho mi gi tr nu cho trc gi tr ca cc cha ca n.

Nu c mt cnh t nt A ti nt B, th bin B ph thuc trc tip vo bin A,

v A c gi l cha ca B. Nu vi mi bin , * + , tp hp cc bin cha c k hiu bi parents( ), th phn phi c iu kin ph thuc ca cc bin l tch ca cc phn phi a phng

( ) ( | ( )) (4.5)

Nu Xi khng c cha, ta ni rng phn phi xc sut a phng ca n l khng c iu kin, ngc li th gi l c iu kin. Nu bin c biu din bi mt nt c quan st, th ta ni rng nt l mt chng c (evidence node)

u im:

- D dng ci t - Thi gian thi hnh tng t nh cy quyt nh - t kt qu tt trong phn ln cc trng hp

Nhc im:

- Gi thit v tnh c lp iu kin ca cc thuc tnh lm gim chnh xc

4.2.3. K hng xm gn nht (K- Nearest Neighbors)

Thut ton K-Nearest Neighbors (K-NN) l thut ton phn loi c mc ch phn loi mt lp cho mu mi (Query Point) da trn cc thuc tnh v lp ca mu c sn (tp Training), cc mu ny c nm trong mt h gi l h khng gian mu. [7]

Mt i tng c phn lp da vo K lng ging ca n. K l s nguyn dng c xc nh trc khi thc hin thut ton. Ngi ta thng dng khong cch Euclidean tnh khong cch gia cc i tng.

Thut ton K-NN c m t nh sau:

1. Xc nh gi tr tham s K (s lng ging gn nht) 2. Tnh khong cch gia cc i tng cn phn lp (Query Point) vi tt c i

tng trong tp training data, thng s dng khong cch Euclidean sau:

Vi vector X=( ) v vector ( ) th:

D(X,Y)= ( ) (4.6)

3. Sp xp khong cch theo th t tng dn v xc nh K lng ging gn nht vi Query Point

4. Ly tt c cc lp ca K lng ging gn nht xc nh.


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5. Da vo phn ln lp ca lng ging gn nht xc nh lp cho Query Point.

Nhn xt:

- Thut ton K-NN l mt thut ton n gin, d hiu, d ci t. - Tuy nhin, kt qu bi ton da rt nhiu vo cch chn s K sao cho ph hp.

Vic quyt nh chn s K sao cho ph hp ti u cho tng bi ton l vn ct yu ca thut ton K-NN.


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CHNG V: THIT K H THNG

5.1. M t cc tp d liu (dataset)

Cc tp d liu s dng trong nghin cu ti c ly t cuc thi BCI Competition II [1] v ch s dng tp d liu Ia v Ib.

Tp d liu Ia thu c t mt i tng khe mnh, i tng ny s c hi v s di chuyn ca con tr (ln hoc xung) c hin th trn mn hnh, ng thi in th v no t 6 knh (channel) ca i tng ny s c ghi li. in th v no dng tng ng vi s di chuyn ln ca con tr trn mn hnh, in th m tng ng vi s di chuyn ln. Mi ln th (trial) c thc hin trong 6s. Hnh nh chuyn ng ca con tr s c hin th t giy 0.5 ti ht ln th v qu trnh ghi phn hi (feedback) t v no s c thc hin t giy 2.0 ti giy 5.5. tc l ch c 3.5s dng ghi li d liu vi tn s qut l 256Hz nh vy s ghi li c tt c 896 gi tr (value) mi knh mi ln th. Cc gi tr c n v l Volt.

Cu trc chung ca mi ln th:

Thi gian th: 6s

Thi gian hin th: t giy 0.5 ti giy 6.0

Thi gian phn hi: t giy 2.0 ti giy 5.5

Tp d liu Ib c thu t mt bnh nhn b chng x cng teo c mt bn. i tng ny c ghi li in th v no ca 7 knh trong mi ln th tng ng vi s di chuyn ca con tr (ln hoc xung) c hin hin th trn mn hnh hoc dng m thanh miu t chuyn ng. Mi ln th c thc hin trong 8s. Thi gian hin th l t giy 0.5 ti giy 7.5 v c ghi li in th v no t giy 2.0 ti giy 6.5. nh vy c 4.5s ghi d liu vi tn s qut l 256Hz s ghi li c 1152 gi tr mi knh mi ln th. Cc gi tr cng c n v l Volt.

Cu trc chung mi ln th:

Thi gian th: 8s

Thi gian hin th: t giy 0.5 ti giy 7.5

Thi gian phn hi: t giy 2.0 ti giy 6.5

5.2. Gii thiu v Weka

Weka (waikato enviroment for knowledge analysis) l phn mm khai ph d liu thuc d n nghin cu ca i hc waikato, new zealand. Weka bao gm tp hp cc gii thut hc my phc v cho cng cuc khai ph d liu. Weka cung cp giao din ha cng nh th vin c vit bng ngn ng Java cho php thc hin cc nhim v khai ph d liu thng gp nh tin s l d liu (pre-processing), phn lp (classification), khai thc lut kt hp (association), gom nhm (clustering).

s dng c th vin weka th cc tp d liu phi c a di nh dng tp tin *.arff. Arff (attribute-relation file format) l nh dng d liu chuyn bit ca weka, t chc d liu theo cu trc c quy nh trc.

[1]

http://bbci.de/competition/


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Cu trc tp tin arff bao gm hai phn: header v data.

Header cha cc khai bo quan h (relation) v danh sch cc thuc tnh. Cc thuc tnh c th biu din bng cc kiu d liu nh:

Numeric l kiu dng s gm real v integer.

Nominal l kiu d liu dng danh sch.

String l kiu d liu dng chui.

Date l kiu d liu dng thi gian.

Data cha cc cha cc i tng d liu (instance data) tng ng vi cc thuc tnh khai bo. Mi i tng d liu c ghi trn mt hng.

Mt s quy tc khai bo trong tp tin arff:

S dng k hiu % u dng ch thch.

S dng c php @ralation khai bo tn quan h.

S dng c php @attribute khai bo tn thuc tnh v kiu ca thuc tnh.

S dng c php @data bt u nhp cc i tng d liu.

5.3. S dng Weka trn cc tp d liu

Th vin Weka c t chc thnh nhiu gi (pakage) vi nhiu lp (class) khc nhau h tr cho vic khai ph d liu, v d nh:

Weka.core cha cc gi v cc lp c bn (nh lp weka.core.instance, lp weka.core.attribute, ...) cng nh nhiu lp tin ch khc.

Weka.filters cha cc gi v cc lp dng trong vic vic tin s l d liu, v d nh lc thuc tnh (weka.filters.unsupervised.attribute), lc d liu (weka.filters.unsupervised.instance), ...

Weka.classifiers cha cc gi v cc lp gii thut dng trong vic phn loi i tng (nh lp weka.classifiers.bayes, lp weka.classifiers.funtions.smo,...)

Weka.clusterers cha cc gi v cc lp gii thut dng cho vic gom nhm i tng (nh lp weka.clusterers.farthestfirst, lp weka.clusterers.simplekmeans,...)

Nhng thnh phn thng c dng trong mt chng trnh s dng th vin weka l:

Instances dng lu tr d liu hc (trainning) hoc kim th (testing. Filter dng lc thuc tnh hoc i tng d liu Classifier/Clusterer dng phn loi hoc gom cm d liu Evaluation dng tin hnh mt gii thut phn loi hoc gom cm trn mt tp d

liu. Attribute selection loi b nhng thuc tnh d tha, khng ph hp trong tp d liu.

5.3.1. Khi to Instances

C 2 cch to ra 1 tp d liu instances

S dng BufferedReader c t tp tin *.arff.


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import weka.core.Instances;

import java.io.BufferedReader;

import java.io.FileReader;

...

BufferedReader reader = new BufferedReader(

new FileReader("/some/where/data.arff"));

Instances data = new Instances(reader);

reader.close();

// setting class attribute

data.setClassIndex(data.numAttributes() - 1);

S dng i tng datasource chuyn i t tp tin *.arff vo i tng instances

import weka.core.converters.ConverterUtils.DataSource;

...

DataSource source = new DataSource("/some/where/data.arff");

Instances data = source.getDataSet();

// setting class attribute

if (data.classIndex() == -1)

data.setClassIndex(data.numAttributes() - 1);

Sau khi khi to i tng instances cn phi thit lp ch mc thuc tnh c dng phn loi. Mc nh trong tp tin *.arff s l thuc tnh cui cng.

5.3.2. S dng Filter trn tp d liu

Filter c s dng trong giai on tin s l d liu, loi i nhng thuc tnh cng nh cc i tng d liu trong tp d liu khng dng n hoc nhng i tng d liu b thiu thng tin.


import weka.filters.Filter;

import weka.filters.unsupervised.attribute.Remove;

...

String[] Options = {first, 2-5, 7, last}; // Set attribute be // filtered

Remove remove = new Remove(); // new instance of filter

remove.setAttributeIndices(options); // set options

remove.setInputFormat(data); // inform filter about dataset

Instances newData = Filter.useFilter(data, remove); // apply filter


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5.3.3. Khi to Classifier

Trong weka mt classifier c s dng hc mt tp d liu trainning. V d nh classifier s dng gii thut naives bayes trn tp d liu. Vic hc c thc hin qua phng thc buildClassifier(Instances).

import weka.classifiers.bayes.NaiveBayes;

...

NaiveBayes bayes = new NaiveBayes(); // new instance of tree

bayes.buildClassifier(data); // build classifier

5.3.4. Phn loi i tng qua Evaluation

Khi c mt tp d liu kim th, i tng Evaluation s c s dng sau khi khi to mt classifier v hc d liu trainning.


import weka.classifiers.Evaluation;

import weka.classifiers.bayes.NaiveBayes;

...

Instances train = ... // from somewhere

Instances test = ... // from somewhere

// train classifier

Classifier cls = new NaiveBayes ();

cls.buildClassifier(train);

// evaluate classifier in test dataset

Evaluation eval = new Evaluation(train);

eval.evaluateModel(cls, test);

5.3.5. S dng attribute selection

Weka c lp AttributeSelection gip cho vic la chn thuc tnh t tp d liu ban u nhm tinh gin d liu da trn mt phng php nh gi (Evaluator) v mt phng php tm kim (Search).

package weka.attributeSelection;

...

Instances data = ... // from somewhere

AttributeSelection selector = new AttributeSelection();

CfsSubsetEval eval = new CfsSubsetEval(); //evaluator method

GreedyStepwise search = new GreedyStepwise(); //search method

selector.setSearchBackwards(true); //use backward

selector.setEvaluator(eval); //set evaluation

selector.setSearch(search); //set search

selector.setInputFormat(data); //set datasets format // generate new data

Instances newData = selector.reduceDimensionaliy(data);


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5.4. Cu trc chng trnh

C th chia chng trnh thnh 4 giai on chnh:

5.4.1. Giai on arff converter

T cc tp d liu ban u c ghi di dng s trong tp tin *.txt s c chuyn i theo nh dng ca tp tin *.arff. Sau s c dng khi to cc i tng Instances trong chng trnh.

5.4.2. Giai on filter thuc tnh

Khi s dng feature selection trn thuc tnh ca tng knh trong tp d liu th tp d liu s c filter loi b cc thuc tnh ca cc knh khc. Sau s a vo feature selection.

5.4.3. Giai on feature selection

Cc thuc tnh c a vo feature selection s c s dng phng php correlation-base kt hp vi phng php tm kim best first search. Sau s c cc instances s c gim s chiu da trn nhng thuc tnh c la chn

5.4.4. Giai on classification.

Trong giai on ny cc Instances sau khi qua feature selection c gim s chiu s c s dng cc gii thut phn loi khc nhau ty vo la chn gii thut phn loi giao din chng trnh. Cc gii thut phn loi c s dng trong giai on ny gm c: naive bayes, support vector machine v k nearest neighbor (vi k=5)

Ty vo nhng ty chn phn loi cng nh s dng feature selection khc nhau m chng trnh s c nhng trnh t giai on khc nhau.

Phn loi d liu khng s dng Feature selection

Khi khng s dng feature selection cc tp d liu s c s dng trc tip trong giai on classification.

Hnh 5.1 S lung tc v khng s dng Feature selection

Phn loi d liu s dng feature selection trn ton b thuc tnh cc knh

Feature selection c s dng trn ton b cc thuc tnh ca tt c cc knh. Tp d liu sau khi qua feature selection s c gim s chiu ch gi li nhng thuc tnh c la chn. Tp test cng s c filter tng ng vi danh sch thuc tnh c chn.


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Hnh 5.2 S lung tc v s dng Feature selection trn ton b thuc tnh

Phn loi d liu s dng feature selection trn thuc tnh ca tng knh

Khi tp d liu ln c nhiu thuc tnh khi selection s mt nhiu thi gian v vy cc thuc tnh ca tng knh s c selection ring bit, sau s c hp cc instances c gim chiu ca tng knh li vi nhau trc khi a vo giai on classification. Tp test cng c filter da trn cc thuc tnh c chn.

Hnh 5.3 S lung tc v s dng Feature selection trn thuc tnh tng knh

5.5. Giao din chng trnh

Chng trnh h tr phn loi d liu s dng cc gii thut gm naive bayes, support vector machine v k nearest neighbor. Cho php s dng feature selection trn ton thuc tnh hoc trn thuc tnh ca tng knh hoc khng s dng feature selection. kt qu c lu tr vo result list cho php so snh cc ln chy.


25

Hnh 5.4 Giao din chng trnh Demo


26

CHNG VI: KIM TH NH GI KT QU

6.1. nh gi chnh xc v hiu sut i vi cc gii thut

Mi tp d liu c chy trn tng gii thut v ghi li chnh xc v thi gian chy ca gii thut.

Bng 6.1 Kt qu tp d liu Ia:

Gii thut S testcase S testcase ng chnh xc

(%)

Thi gian chy

(ms)

Naive Bayes 293 263 89.76 28702

SVM 293 248 84.64 11114

K Nearest neighbor 293 221 75.42 10207

Bng 6.2 Kt qu tp d liu Ib:

Gii thut S testcase S testcase ng chnh xc

(%)

Thi gian chy

(ms)

Naive Bayes 180 98 54.44 4752

SVM 180 90 50.00 5414

K Nearest neighbor 180 89 49.44 4269

6.2. nh gi chnh xc v hiu sut khi dng phng php feature selection

Mi tp d liu s c c nh gi khi s dng feature selection trn ton b thuc tnh, trn thuc tnh ca tng knh v khi khng s dng feature selection. tt c cc ln chy s dng cng 1 phng php phn loi l naive bayes.

Bng 6.3 Kt qu tp d liu Ia:

Feature selection S testcase S testcase ng

chnh xc

(%)

Thi gian chy

(ml)

Khng s dng 293 254 86.68 6010

Trn ton b thuc tnh 293 249 84.98 87784

Trn thuc tnh tng knh 293 263 89.76 28702


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Bng 6.4 Kt qu tp d liu Ib:

Feature selection S testcase S testcase ng

chnh xc

(%)

Thi gian chy

(ml)

Khng s dng 180 87 48.33 4368

Trn ton b thuc tnh

180 89 49.44 7727

Trn thuc tnh tng knh

180 98 54.44 4752


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CHNG VII: TNG KT

7.1. Kt qu t c

Vi ti s dng tn hiu sng no EEG trong iu khin n gin, nhm sinh vin hon thnh s b giai on u.

Nhm sinh vin tm hiu cc c trng c bn ca sng no. Tm hiu cc phng php rt trch c trng nh Fourier Transform, Wavelet Transform tm ra c trng d liu th. Cng , nhm sinh vin cng tm hiu v mt s phng php phn loi, u khuyt im ca tng gii thut a ra hng hin thc cho chng trnh.

Chng trnh Demo v ti c hin thc, c kh nng phn loi vi chnh xc tng i. Trong , chng trnh s dng 3 gii thut phn loi c bn ban u l Nave Bayes, Support Vector Machine, K nearest neighbor kt hp vi phng php Feature Selection nng cao hiu sut.

7.2. Phng hng pht trin

Vi h thng chng trnh c hin thc, nhm sinh vin tip tc hon thnh giai on 2 theo hng s dng phng php lc nhiu d liu, rt trch c trng ti u ho kt qu phn loi, nng cao kh nng d on ca chng trnh.


29

Ti liu tham kho:

[1] Priyanka Khatwani, Archana Tiwari - International Journal of Advanced Research in

Computer and Communication Engineering Vol. 2, Issue 2, February 2013, A survey on different

noise removal techniques of EEG signals.

[2] Jorge Baztarrica Ochoa - EEG Signal Classification for Brain Computer Interface

Applications

[3] Ales Prochazka and Jaromr Kukal - Institute of Chemical Technology in Prague Department of Computing and Control Engineering, Wavelet Transform Use for Feature

Extraction and EEG Signal Segments Classification.

[4] Brett D. Mensh*, Justin Werfel, and H. Sebastian Seung - BCI Competition 2003Data Set Ia: Combining Gamma-Band Power With Slow Cortical Potentials to Improve Single-Trial

Classification of Electroencephalographic Signals.

[5] Abdul-Bary Rauf Suleiman, Toka Abdul-hameed Fatehi, Computer and Information

Engineering Department College Of Electronics Engineering, University of Mosul Mosul,

Iraq, Features Extraction Techniques Of EEG Signal.

[6] Irena Koprinska - School of Information Technologies, University of Sydney, Sydney NSW

2006, Australia. Feature Selection for Brain-Computer Interfaces

electroencephalography and application

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