In the past few years, due to their ability to extract multivariate correlations, machine learning tools have become more and more important for discovery of information in very complex data sets. This has had specific application to various data sets related to human brain tasks. However, this is far from a simple and direct methodology. Some of the issues involve dealing with the extreme signal to noise ratios, as well as variation between different individuals. Moreover, the huge amount of features relative to the number of data points is a challenge. As a result, in attacking these problems, we found it necessary to adapt a large variety of methodologies; chosen to overcome specific obstructions for specific problems. In this paper, we describe our experience working on several examples at the edge of capabilities of these systems and describe the various and variant methodologies we needed to overcome these sort of challenges. Hopefully these cases will serve as a guideline for other applications.

中文翻译：

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从基于神经生理学的复杂数据中分析认知过程：一些经验教训

在过去几年中，由于能够提取多元相关性，机器学习工具对于在非常复杂的数据集中发现信息变得越来越重要。这对与人类大脑任务相关的各种数据集有特定的应用。然而，这远不是一种简单直接的方法。其中一些问题涉及处理极端的信噪比，以及不同个体之间的差异。此外，与数据点数量相关的大量特征是一个挑战。因此，在解决这些问题时，我们发现有必要采用多种方法；选择克服特定问题的特定障碍。在本文中，我们描述了我们在这些系统的能力边缘处理几个示例的经验，并描述了我们克服这些挑战所需的各种不同的方法。希望这些案例可以作为其他应用程序的指南。