Object classification from randomized EEG trials
Object classification from randomized EEG trials
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DOI：
10.1109/cvpr46437.2021.00384
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发表时间：
2020-04
期刊：
影响因子：
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通讯作者：
Hamad Ahmed;R. Wilbur;Hari M. Bharadwaj;J. Siskind
中科院分区：
文献类型：
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作者：
Hamad Ahmed;R. Wilbur;Hari M. Bharadwaj;J. Siskind
新的研究结果表明，通过EEG测量的图像刺激诱发的人脑活动对对象分类的可行性有很大的限制。相当多的先前的工作受到混淆之间的刺激类和时间，因为实验的开始。先前尝试使用随机试验来避免这种混淆，但当数据集与原始实验大小相同时，无法以统计学显著的方式获得高于偶然性的结果。在这里，我们尝试使用一系列代表最新技术水平的方法从EEG进行对象分类，其中随机EEG试验的数据集要大得多（20×），40个类别中的每个类别有1,000个刺激呈现，所有这些都来自单个受试者。据我们所知，这是从单个受试者收集的最大的EEG数据，并且处于可行性范围内。我们获得的分类准确度略高于机会，并以统计学上显著的方式高于机会，并进一步评估准确度如何取决于所使用的分类器，所使用的训练数据量和类的数量。达到数据收集的极限，只有轻微的机会以上的性能表明，目前的文献大大夸大了从EEG对象分类的可行性。
New results suggest strong limits to the feasibility of object classification from human brain activity evoked by image stimuli, as measured through EEG. Considerable prior work suffers from a confound between the stimulus class and the time since the start of the experiment. A prior attempt to avoid this confound using randomized trials was unable to achieve results above chance in a statistically significant fashion when the data sets were of the same size as the original experiments. Here, we attempt object classification from EEG using an array of methods that are representative of the state-of-the-art, with a far larger (20×) dataset of randomized EEG trials, 1,000 stimulus presentations of each of forty classes, all from a single subject. To our knowledge, this is the largest such EEG data-collection effort from a single subject and is at the bounds of feasibility. We obtain classification accuracy that is marginally above chance and above chance in a statistically significant fashion, and further assess how accuracy depends on the classifier used, the amount of training data used, and the number of classes. Reaching the limits of data collection with only marginally above-chance performance suggests that the prevailing literature substantially exaggerates the feasibility of object classification from EEG.