The study of neuronal activity with low frequency has shown an increasing interest for its greater stability and reliability recent years. One challenge in analyzing this kind of activity is to find similarities and differences between signals efficiently and effectively. The traditional analysis methods, such as short-time Fourier transform, are easily obscured by background noises and often involve a large number of parameters. Therefore, this paper introduces a novel time-frequency analysis method based on wavelet transformation and half-ellipsoid modeling to extract instantaneous frequency and instantaneous phase information. This method overcomes some shortcomings of conventional time-frequency analysis. In this method, wavelet transformation is used to provide high-level representations of raw signals, and parsimonious half-ellipsoid models are used to extract changes in time domain and frequency domain of neural recordings. The method was validated to local field potentials (LFPs) of olfactory bulb of anesthetized rats during three different odor stimuli. The results suggested that this method could detect odor-relevant features from olfactory signals with large variability. The Odors then were classified with support vector machine (SVM) algorithm and the classification accuracy reached 79.4%.
Algorithms ; Animals ; Evoked Potentials ; Fourier Analysis ; Odorants ; analysis ; Olfactory Bulb ; physiology ; Rats ; Reproducibility of Results ; Smell ; physiology ; Support Vector Machine ; Wavelet Analysis

气味是评价食品新鲜度最重要的参数之一。当气味以其自然浓度存在时，会在嗅觉系统中引发不同的神经活动模式。本研究提出了一种通过检测食物气味进行食物检测与评价的在体生物传感系统。我们通过将多通道微电极植入在清醒大鼠嗅球的僧帽/丛状细胞层上，进而对神经信号进行实时检测。结果表明，不同的气味可以引起不同的神经振荡活动，每个僧帽/丛状细胞会表现出特定气味的锋电位发放模式。单个大鼠的少量细胞携带足够的信息，可以根据锋电位发放频率变化率的极坐标图来区分不同储存天数的食物。此外，研究表明气味刺激后，β振荡比γ振荡表现出更特异的气味响应模式，这表明β振荡在气味识别中起着更重要的作用。综上，本研究提出的在体神经接口为评估食品新鲜度提供了一种可行性方法。
Olfactory Bulb ; Odorants ; Smell