Brain Decoding via Graph Kernels

Sandro Vega-Pons, P. Avesani
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引用次数: 14

Abstract

An emergent trend in data analysis of functional brain recordings is based on multivariate pattern recognition. Unlike univariate approaches, it is designed as a prediction task by decoding the brain state. fMRI brain decoding is a challenging classification problem due to the noisy, redundant and spatio-temporal correlated data, where there are generally much more features than samples. The use of a classifier requires that raw data is mapped into n-dimensional real vectors where the structural information of the data is not taken into account. Alternative methods propose a different data representation based on a graph encoding. While graphs provide a more powerful representation, machine learning algorithms for this type of encoding become computationally intensive. The contribution of this paper is the introduction of a graph kernel with a lower computational complexity that allows taking advantage from both the representative power of graphs and the discrimination power of kernel-based classifiers such as Support Vector Machines. We provide experimental results for a discrimination task between faces and houses on a fMRI dataset. We also investigate on synthetic data, how the brain decoding task differs according to the different encodings: vectorial and graph-based. A remarkable feature of the graph approach is its capability to handle data from different subjects, without the need of any intersubject alignment. An intersubject decoding experiment is also performed for the faces versus houses problem.
通过图核进行大脑解码
基于多元模式识别的脑功能记录数据分析是一个新兴趋势。与单变量方法不同,它被设计为通过解码大脑状态来预测任务。fMRI脑解码是一个具有挑战性的分类问题,由于数据的噪声、冗余和时空相关,其中通常存在比样本更多的特征。分类器的使用要求将原始数据映射为n维实向量,其中不考虑数据的结构信息。备选方法提出了基于图编码的不同数据表示。虽然图形提供了更强大的表示,但这种编码类型的机器学习算法变得计算密集型。本文的贡献在于引入了一种计算复杂度较低的图核,可以同时利用图的代表性和基于核的分类器(如支持向量机)的识别能力。我们提供了在fMRI数据集上人脸和房屋区分任务的实验结果。在合成数据上,我们还研究了基于向量和基于图的不同编码方式对大脑解码任务的影响。图方法的一个显著特征是它能够处理来自不同主题的数据,而不需要任何主题间的对齐。对面孔与房屋问题进行了被试间解码实验。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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