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Classifying Multivariate ElectrocorticographicSignal Patterns from different sessions
KTH, School of Computer Science and Communication (CSC).
KTH, School of Computer Science and Communication (CSC).
2013 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

In the field of Brain-Computer Interfaces (BCI) there is a

problem called the inter-session problem, generally causing

a decrease in classification performance between sessions.

This study investigates the extent of this problem

in Electrocorticographic (ECoG) data, and how it may be

approached using classification and feature selection algorithms.

The focus regarding classification is whether linear

or nonlinear classification methods generalizes better, and

the focus of feature selection is whether filter or wrapper

methods improve generalization more. These questions are

answered by empirical experiments on two sets of ECoG

data collected over two different sessions.

The inter-session problem in ECoG data proved to be of

considerable size. Classification performance dropped from

78-91% on the training data set (using cross validation)

to 70-80% on the tests. Better normalizations and scaling

methods were deemed necessary to help reduce this drop.

The results were inconclusive as to linear or nonlinear

classifier generalization, since performance was nearly

identical. Due to their simplicity, linear methods would be

preferable in this case. As to feature selection, the risks

of overfitting became apparent using Simulated Annealing

(SA) wrapper methods. Simpler feature selection algorithms

that were less prone to overfitting, both filter and

wrapper methods, helped to improve generalization more.

Abstract [sv]

Inom området Brain-Computer Interfaces (BCI) finns ett

problem som kallas inter-sessionsproblemet, som vanligtvis

orsakar en försämring av klassifieringsprestandan mellan

sessioner. Denna studie undersöker problemets omfattning

med ECoG (

Electrocorticography) data, och hur det

kan hanteras med klassificerings och särdragsurvalsalgoritmer

(en.

feature selection). Fokus rörande klassifiering är

hurvida linjära eller icke-linjära klassifieringsmetoder generaliserar

bättre, och fokus rörande särdragsurval är hurvida

metoder av typen

filter eller wrapper förbättrar generalisering

mer. Dessa frågor besvaras genom empiriska experiment

av två datamängder ECoG data insamlad från två

olika sessioner.

Inter-sessionsproblemet med ECoG data visade sig vara

av betydande storlek. Klassificeringsprestandan försämrades

från 78-91% på träningsdatan (med korsvalidering) till

70-80% på testerna. Bättre normaliserings- och skalningsmetoder

anses nödvändiga för att reducera försämringen.

Hurvida linjära eller icke-linjära klassifierare generaliserade

bättre var resultaten inte entydiga, då prestandan

var nästan densamma. I detta fall föredras linjära klassifierare,

på grund av deras enkelhet. Gällande särdragsurval

uppenbarade sig riskerna för överinlärning under användningen

av

Simulated Annealing. Enklare särdragsurvalsmetoder

som var mindre benägna att överinlära, både filter

och wrappermetoder, förbättrade generaliseringen mer.

Place, publisher, year, edition, pages
2013.
Series
Kandidatexjobb CSC, K13061
National Category
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-136590OAI: oai:DiVA.org:kth-136590DiVA: diva2:676564
Educational program
Master of Science in Engineering - Computer Science and Technology
Supervisors
Examiners
Available from: 2013-12-13 Created: 2013-12-06 Last updated: 2013-12-13Bibliographically approved

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