Automated identification of neural correlates of continuous variables.
Automated identification of neural correlates of continuous variables.
复制标题
自动识别连续变量的神经相关性。
DOI：
10.1016/j.jneumeth.2014.12.012
复制
发表时间：
2015
影响因子：
3
通讯作者：
Daly I
中科院分区：
文献类型：
--
作者：
Daly I
背景脑电（EEG）可以由大量不同的特征类型描述，为了可靠地识别与连续自变量相关的特征，需要自动特征选择方法。提出了一种基于特征集的谱分解的神经数据集特征自动识别方法。此外，该方法是能够识别的功能，涉及到连续的独立volumets.ResultsThe所提出的方法是第一次评估合成EEG数据集，并观察到可靠地识别正确的功能。然后将该方法应用于音乐聆听任务期间记录的脑电信号，并观察到自动识别音乐克里思变化的神经相关性，类似于之前研究中识别的神经相关性。最后，该方法被应用于识别音乐诱导的情感状态的神经相关。所确定的神经相关主要驻留在额叶皮层，并与广泛报道的神经相关的emotions.Comparison with existing methodsThe建议的方法相比，典型相关分析和常见的空间模式的国家的最先进的方法，为了识别区分合成事件的特征，不同幅度的相关电位，并观察到表现出更大的性能作为数据集中的独特群体的数量增加。结论所提出的方法是能够识别神经相关的连续变量的EEG数据集，并表现出优于典型相关分析和常见的空间模式。
BackgroundThe electroencephalogram (EEG) may be described by a large number of different feature types and automated feature selection methods are needed in order to reliably identify features which correlate with continuous independent variables.New methodA method is presented for the automated identification of features that differentiate two or more groups in neurological datasets based upon a spectral decomposition of the feature set. Furthermore, the method is able to identify features that relate to continuous independent variables.ResultsThe proposed method is first evaluated on synthetic EEG datasets and observed to reliably identify the correct features. The method is then applied to EEG recorded during a music listening task and is observed to automatically identify neural correlates of music tempo changes similar to neural correlates identified in a previous study. Finally, the method is applied to identify neural correlates of music-induced affective states. The identified neural correlates reside primarily over the frontal cortex and are consistent with widely reported neural correlates of emotions.Comparison with existing methodsThe proposed method is compared to the state-of-the-art methods of canonical correlation analysis and common spatial patterns, in order to identify features differentiating synthetic event-related potentials of different amplitudes and is observed to exhibit greater performance as the number of unique groups in the dataset increases.ConclusionsThe proposed method is able to identify neural correlates of continuous variables in EEG datasets and is shown to outperform canonical correlation analysis and common spatial patterns.