The brain dynamics in the electroencephalogram (EEG) data are often challenging to interpret, specially when the signal is a combination of desired brain dynamics and noise. Thus, in an EEG signal, anything other than the desired electrical activity, which is produced due to coordinated electrochemical process, can be considered as unwanted or noise. To make brain dynamics more analyzable, it is necessary to remove noise in the temporal location of interest, as well as to denoise data from a specific spatial location. In this paper, we propose a novel method for noisy EEG analysis with accompanying toolbox which includes adaptive, data-driven noise removal technique based on the improved intersection of confidence interval (ICI)-based algorithm. Next, a local entropy-based method for EEG data analysis was designed and included in the toolbox. As shown in the paper, the relative intersection of confidence interval (RICI) procedure retains the dominant dipole activity projected on electrodes, while the local (short-term) Rényi entropy-based analysis of the EEG representation in the time-frequency domain is efficient in detecting the presence of P300 event-related potential (ERP) at specific electrodes. Namely, the P300 are detected as sharp drop of entropy in the temporal domain that enabled accurate calculation of the index of the noise class for the EEG signals.

脑电图（EEG）数据中的大脑动力学通常很难解释，特别是当信号是所需的大脑动力学和噪声的组合时。因此，在EEG信号中，由于协调的电化学过程而产生的除了期望的电活动之外的任何其他东西都可以被认为是不想要的或噪声。为了使大脑动力学更具可分析性，有必要消除感兴趣的时间位置中的噪声，并对特定空间位置的数据进行去噪。在本文中，我们提出了一种带有工具箱的用于噪声EEG分析的新方法，该方法包括基于改进的基于置信区间（ICI）的算法的自适应数据驱动的噪声消除技术。接下来，设计了一种基于局部熵的EEG数据分析方法，并将其包含在工具箱中。如本文所示，相对置信区间（RICI）程序保留了投射在电极上的主导偶极子活动，而时频域中基于局部（短期）Rényi熵的EEG表示分析非常有效检测特定电极上是否存在P300事件相关电位（ERP）。即，将P300检测为在时域中的熵的急剧下降，这使得能够准确计算EEG信号的噪声类别的指数。时频域中基于局部（短期）Rényi熵的EEG表示分析可有效检测特定电极上P300事件相关电位（ERP）的存在。即，将P300检测为在时域中的熵的急剧下降，这使得能够准确计算EEG信号的噪声类别的指数。时频域中基于局部（短期）Rényi熵的EEG表示分析可有效检测特定电极上P300事件相关电位（ERP）的存在。即，将P300检测为在时域中的熵的急剧下降，这使得能够准确计算EEG信号的噪声类别的指数。