This paper investigates the extraction of time-frequency (TF) features for classification of electroencephalography (EEG) signals and episodic memory. We propose a model based on the definition of locally stationary processes (LSPs), estimate the model parameters, and derive a mean square error (MSE) optimal Wigner-Ville spectrum (WVS) estimator for the signals. The estimator is compared with state-of-the-art TF representations: the spectrogram, the Welch method, the classically estimated WVS, and the Morlet wavelet scalogram. First, we evaluate the MSE of each spectrum estimate with respect to the true WVS for simulated data, where it is shown that the LSP-inference MSE optimal estimator clearly outperforms other methods. Then, we use the different TF representations to extract the features which feed a neural network classifier and compare the classification accuracies for simulated datasets. Finally, we provide an example of real data application on EEG signals measured during a visual memory encoding task, where the classification accuracy is evaluated as in the simulation study. The results show consistent improvement in classification accuracy by using the features extracted from the proposed LSP-inference MSE optimal estimator, compared to the use of state-of-the-art methods, both for simulated datasets and for the real data example.

本文研究了时频（TF）特征的提取，以对脑电图（EEG）信号和情景记忆进行分类。我们基于局部平稳过程（LSP）的定义提出一个模型，估计模型参数，并得出信号的均方误差（MSE）最佳Wigner-Ville谱（WVS）估计器。将估算器与最新的TF表示形式进行比较：频谱图，Welch方法，经典估算的WVS和Morlet小波比例尺。首先，我们针对仿真数据的真实WVS评估每个频谱估计的MSE，这表明LSP推断MSE最佳估计器明显优于其他方法。然后，我们使用不同的TF表示来提取为神经网络分类器提供信息的特征，并比较模拟数据集的分类精度。最后，我们提供了一个在视觉记忆编码任务期间测得的EEG信号实际数据应用示例，其中分类精度的评估与仿真研究相同。结果表明，与使用最新方法相比，通过使用从建议的LSP推理MSE最佳估计器中提取的特征，分类精度得到了持续改善，无论是针对模拟数据集还是针对真实数据示例。如模拟研究那样评估分类精度。结果表明，与使用最新方法相比，通过使用从建议的LSP推理MSE最佳估计器中提取的特征，分类精度得到了持续改善，无论是针对模拟数据集还是针对真实数据示例。如模拟研究那样评估分类精度。结果表明，与使用最新方法相比，通过使用从建议的LSP推理MSE最佳估计器中提取的特征，分类精度得到了持续改善，无论是针对模拟数据集还是针对真实数据示例。