Decoding visual object categories from temporal correlations of ECoG signals
Decoding visual object categories from temporal correlations of ECoG signals
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DOI：
10.1016/j.neuroimage.2013.12.020
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发表时间：
2014-04-15
期刊：
影响因子：
5.7
通讯作者：
Kamitani, Yukiyasu
中科院分区：
文献类型：
--
作者：
Majima, Kei;Matsuo, Takeshi;Kamitani, Yukiyasu
视觉对象类别在大脑中的表征方式是神经科学中的关键问题之一。对低层次视觉特征的研究表明，多个大脑位置之间的神经活动的相对时间或阶段编码信息。然而，这种神经活动的时间模式是否用于视觉对象的表示是未知的。在这里，我们研究了是否以及如何视觉对象类别可以预测（或解码）从颞叶皮层的皮层电图（ECoG）信号的时间模式在5例癫痫患者。我们使用电极之间的时间相关性作为输入特征，并将解码性能与由各个电极的频谱功率和相位定义的特征进行比较。当单独使用功率或相位时，解码精度显著优于机会，单独相关或与功率相结合的相关优于其他特征。通过打乱每个电极中相同类别的试验的顺序来降低具有相关性的解码性能，这表明每个试验中电极之间的相对时间序列是至关重要的。使用滑动时间窗的分析表明，解码性能的相关性开始上升早于功率。这种早期的性能提高被一个使用相位差编码类别的模型复制。这些结果表明，多个神经元单位之间的相互作用产生的活动模式进行额外的信息对视觉对象的类别。(C)2013作者爱思唯尔公司出版All rights reserved.
How visual object categories are represented in the brain is one of the key questions in neuroscience. Studies on low-level visual features have shown that relative timings or phases of neural activity between multiple brain locations encode information. However, whether such temporal patterns of neural activity are used in the representation of visual objects is unknown. Here, we examined whether and how visual object categories could be predicted (or decoded) from temporal patterns of electrocorticographic (ECoG) signals from the temporal cortex in five patients with epilepsy. We used temporal correlations between electrodes as input features, and compared the decoding performance with features defined by spectral power and phase from individual electrodes. While using power or phase alone, the decoding accuracy was significantly better than chance, correlations alone or those combined with power outperformed other features. Decoding performance with correlations was degraded by shuffling the order of trials of the same category in each electrode, indicating that the relative time series between electrodes in each trial is critical. Analysis using a sliding time window revealed that decoding performance with correlations began to rise earlier than that with power. This earlier increase in performance was replicated by a model using phase differences to encode categories. These results suggest that activity patterns arising from interactions between multiple neuronal units carry additional information on visual object categories. (C) 2013 The Authors. Published by Elsevier Inc. All rights reserved.