Data stream synchronization for defining meaningful fMRI classification problems

This source preferred by Marcin Budka

Authors: Budka, M.

http://eprints.bournemouth.ac.uk/22862/

Journal: Applied Soft Computing

Volume: 24

Pages: 212-221

ISSN: 1568-4946

DOI: 10.1016/j.asoc.2014.07.011

Application of machine learning techniques to the functional Magnetic Resonance Imaging (fMRI) data is recently an active field of research. There is however one area which does not receive due attention in the literature – preparation of the fMRI data for subsequent modelling. In this study we focus on the issue of synchronization of the stream of fMRI snapshots with the mental states of the subject, which is a form of smart filtering of the in- put data, performed prior to building a predictive model. We demonstrate, investigate and thoroughly discuss the negative effects of lack of alignment between the two streams and propose an original data-driven approach to efficiently address this problem. Our solution involves casting the issue as a constrained optimization problem in combination with an alternative classification accuracy assessment scheme, applicable to both batch and on-line scenarios and able to capture information distributed across a number of input samples lifting the common simplifying i.i.d. assumption. The proposed method is tested using real fMRI data and experimentally compared to the state-of-the-art ensemble models reported in the literature, outperforming them by a wide margin.

This data was imported from DBLP:

Authors: Budka, M.

http://eprints.bournemouth.ac.uk/22862/

Journal: Appl. Soft Comput.

Volume: 24

Pages: 212-221

DOI: 10.1016/j.asoc.2014.07.011

This data was imported from Scopus:

Authors: Budka, M.

http://eprints.bournemouth.ac.uk/22862/

Journal: Applied Soft Computing Journal

Volume: 24

Pages: 212-221

ISSN: 1568-4946

DOI: 10.1016/j.asoc.2014.07.011

Application of machine learning techniques to the functional Magnetic Resonance Imaging (fMRI) data is recently an active field of research. There is however one area which does not receive due attention in the literature - preparation of the fMRI data for subsequent modelling. In this study we focus on the issue of synchronization of the stream of fMRI snapshots with the mental states of the subject, which is a form of smart filtering of the input data, performed prior to building a predictive model. We demonstrate, investigate and thoroughly discuss the negative effects of lack of alignment between the two streams and propose an original data-driven approach to efficiently address this problem. Our solution involves casting the issue as a constrained optimization problem in combination with an alternative classification accuracy assessment scheme, applicable to both batch and on-line scenarios and able to capture information distributed across a number of input samples lifting the common simplifying i.i.d. assumption. The proposed method is tested using real fMRI data and experimentally compared to the state-of-the-art ensemble models reported in the literature, outperforming them by a wide margin. © 2014 The Authors.

This data was imported from Web of Science (Lite):

Authors: Budka, M.

http://eprints.bournemouth.ac.uk/22862/

Journal: APPLIED SOFT COMPUTING

Volume: 24

Pages: 212-221

eISSN: 1872-9681

ISSN: 1568-4946

DOI: 10.1016/j.asoc.2014.07.011

The data on this page was last updated at 17:31 on November 21, 2017.