BACKGROUND While decoding visual and auditory stimuli using recorded EEG signals has enjoyed significant attention in the past decades, decoding olfactory sensory input from EEG data remains a novelty. Recent interest in the brain's mechanisms of processing olfactory stimuli partly stems from the association of the olfactory system and its deficit with neurodegenerative diseases. NEW METHODS An olfactory stimulus decoder using features that represent nonlinear behavior content in the recorded EEG data has been introduced for classifying 4 olfactory stimuli in 5 healthy male subjects. RESULTS We show that by using nonlinear and chaotic features, a subject-specific classifier can be developed for identifying the odors that subjects perceive with an average accuracy of 96.71% and 88.79% in the eyes-open and eyes-closed conditions, respectively. We also employ our methodology in building cross-subject classifiers: once for identifying pleasant and unpleasant odors, and once for the classification of all four olfactory stimuli. The accuracy of our proposed methodology is 91.7% and 82.1% in the eyes-open and eyes-closed conditions, for the odor pleasantness classification. The accuracy of cross-subject classification of all odors is 64.3% and 54.8% for the eyes-open and eyes-closed conditions, respectively, which is well above chance level. COMPARISON WITH EXISTING METHODS Comparison with similar studies reveals that our proposed method outperforms other classification schemes in terms of accuracy. CONCLUSIONS The results can help researchers design more accurate classifiers for the detection of perceived odors using EEG signals. These results can contribute to gaining more insight into the brain's process of odor perception.

中文翻译：

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使用非线性特征解码脑电数据中的嗅觉刺激：一项初步研究。

背景技术尽管在过去的几十年中使用记录的EEG信号解码视觉和听觉刺激已经引起了广泛的关注，但是从EEG数据解码嗅觉感觉输入仍然是新颖的。对大脑处理嗅觉刺激机制的最新兴趣部分是由于嗅觉系统及其与神经退行性疾病的缺陷有关。新方法引入了一种嗅觉刺激解码器，该功能使用代表所记录的EEG数据中非线性行为内容的特征对5个健康男性受试者的4种嗅觉刺激进行分类。结果我们显示，通过使用非线性和混沌特征，可以开发特定于对象的分类器来识别对象在睁眼和闭眼条件下感知到的气味的平均准确度分别为96.71％和88.79％。我们还在构建跨学科分类器时采用了我们的方法：一次用于识别令人愉悦和难闻的气味，一次用于对所有四个嗅觉刺激进行分类。对于气味愉悦度分类，我们提出的方法在睁眼和闭眼条件下的准确度分别为91.7％和82.1％。睁眼和闭眼条件下所有气味的跨学科分类准确率分别为64.3％和54.8％，远高于偶然水平。与现有方法的比较与类似研究的比较表明，我们提出的方法在准确性方面优于其他分类方案。结论结果可帮助研究人员设计更准确的分类器，以使用EEG信号检测感知到的气味。