Automated classification of fMRI data employing trial-based imagery tasks

Jong Hwan Lee, Matthew Marzelli, Ferenc A. Jolesz, Seung Schik Yoo

Research output: Contribution to journalArticlepeer-review

22 Citations (Scopus)


Automated interpretation and classification of functional MRI (fMRI) data is an emerging research field that enables the characterization of underlying cognitive processes with minimal human intervention. In this work, we present a method for the automated classification of human thoughts reflected on a trial-based paradigm using fMRI with a significantly shortened data acquisition time (less than one minute). Based on our preliminary experience with various cognitive imagery tasks, six characteristic thoughts were chosen as target tasks for the present work: right-hand motor imagery, left-hand motor imagery, right foot motor imagery, mental calculation, internal speech/word generation, and visual imagery. These six tasks were performed by five healthy volunteers and functional images were obtained using a T2*-weighted echo planar imaging (EPI) sequence. Feature vectors from activation maps, necessary for the classification of neural activity, were automatically extracted from the regions that were consistently and exclusively activated for a given task during the training process. Extracted feature vectors were classified using the support vector machine (SVM) algorithm. Parameter optimization, using a k-fold cross validation scheme, allowed the successful recognition of the six different categories of administered thought tasks with an accuracy of 74.5% (mean) ± 14.3% (standard deviation) across all five subjects. Our proposed study for the automated classification of fMRI data may be utilized in further investigations to monitor/identify human thought processes and their potential link to hardware/computer control.

Original languageEnglish
Pages (from-to)392-404
Number of pages13
JournalMedical Image Analysis
Issue number3
Publication statusPublished - 2009 Jun
Externally publishedYes

Bibliographical note

Funding Information:
Authors appreciate the technical support of Ms. Heather O’Leary for the data acquisition, the logistic support of Mr. Dong-Woo Hahn, and the editorial support of Mr. Samuel Polio. This work was partially supported by grants from NIH (R01-NS048242 to Yoo, SS and NIH U41RR019703 to Jolesz FA), the Korean Ministry of Commerce, Industry, and Energy (Grant No. 2004-02012 to S.S. Yoo), and Gachon Neuroscience Research Institute Grant (to Yoo SS).


  • Brain-computer interface (BCI)
  • Functional MRI
  • Neuroimaging
  • Pattern recognition
  • Support vector machine (SVM)

ASJC Scopus subject areas

  • Radiological and Ultrasound Technology
  • Radiology Nuclear Medicine and imaging
  • Computer Vision and Pattern Recognition
  • Health Informatics
  • Computer Graphics and Computer-Aided Design


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