Journal article
Automated classification of fMRI data employing trial-based imagery tasks
Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA.1
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 T(*)(2)-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.
Language: | English |
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Year: | 2009 |
Pages: | 392-404 |
ISSN: | 13618423 and 13618415 |
Types: | Journal article |
DOI: | 10.1016/j.media.2009.01.001 |
Adult Algorithms Artificial Intelligence Brain Brain Mapping Brain-computer interface (BCI) Brain–computer interface (BCI) Evoked Potentials Female Functional MRI Humans Image Enhancement Image Interpretation, Computer-Assisted Imagination Magnetic Resonance Imaging Male Neuroimaging Pattern Recognition, Automated Pattern recognition Reproducibility of Results Sensitivity and Specificity Support vector machine (SVM) Task Performance and Analysis Young Adult functional MRI