Automated classification of fMRI data employing trial-based imagery tasks.
Automated classification of fMRI data employing trial-based imagery tasks.
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DOI：
10.1016/j.media.2009.01.001
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发表时间：
2009-06
影响因子：
10.9
通讯作者：
Yoo, Seung-Schik
中科院分区：
文献类型：
--
作者：
Lee, Jong-Hwan;Marzelli, Matthew;Jolesz, Ferenc A.;Yoo, Seung-Schik
关键词：
功能性磁共振成像（fMRI）数据的自动解释和分类是一个新兴的研究领域，它能够以最少的人为干预来表征潜在的认知过程。在这项工作中，我们提出了一种方法，用于自动分类的人的想法，反映在一个基于试验的范例，使用功能磁共振成像显着缩短数据采集时间（不到一分钟）。基于我们对各种认知表象任务的初步经验，本研究选择了六种特征思维作为目标任务：右手运动表象、左手运动表象、右脚运动表象、心算、内部言语/词语生成和视觉表象。这六项任务由五名健康志愿者完成，并使用T2* 加权平面回波成像（EPI）序列获得功能图像。从激活图的特征向量，神经活动的分类所必需的，自动提取的区域，在训练过程中，一致和专门激活一个给定的任务。提取的特征向量进行分类使用支持向量机（SVM）算法。使用k倍交叉验证方案的参数优化允许成功识别六种不同类别的管理思维任务，所有五名受试者的准确度为74.5%（平均值）± 14.3%（标准差）。我们提出的研究功能磁共振成像数据的自动分类可能会被用于进一步的调查，以监测/识别人类的思维过程和他们的潜在联系，硬件/计算机控制。
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.
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