Lerga Jonatan, Saulig Nicoletta, Mozetič Vladimir
University of Rijeka, Faculty of Engineering, Department of Computer Engineering, Vukovarska 58, HR-51000 Rijeka, Croatia.
University of Rijeka, Faculty of Engineering, Department of Automation and Electronics, Vukovarska 58, HR-51000 Rijeka, Croatia.
Comput Biol Med. 2017 Jan 1;80:1-13. doi: 10.1016/j.compbiomed.2016.11.002. Epub 2016 Nov 15.
Stochastic electroencephalogram (EEG) signals are known to be nonstationary and often multicomponential. Detecting and extracting their components may help clinicians to localize brain neurological dysfunctionalities for patients with motor control disorders due to the fact that movement-related cortical activities are reflected in spectral EEG changes. A new algorithm for EEG signal components detection from its time-frequency distribution (TFD) has been proposed in this paper. The algorithm utilizes the modification of the Rényi entropy-based technique for number of components estimation, called short-term Rényi entropy (STRE), and upgraded by an iterative algorithm which was shown to enhance existing approaches. Combined with instantaneous frequency (IF) estimation, the proposed method was applied to EEG signal analysis both in noise-free and noisy environments for limb movements EEG signals, and was shown to be an efficient technique providing spectral description of brain activities at each electrode location up to moderate additive noise levels. Furthermore, the obtained information concerning the number of EEG signal components and their IFs show potentials to enhance diagnostics and treatment of neurological disorders for patients with motor control illnesses.
已知随机脑电图（EEG）信号是非平稳的，且通常是多分量的。由于与运动相关的皮层活动反映在脑电图频谱变化中，检测和提取其分量可能有助于临床医生对患有运动控制障碍的患者进行脑神经系统功能障碍的定位。本文提出了一种从脑电图信号的时频分布（TFD）中检测其分量的新算法。该算法利用基于雷尼熵的技术改进来估计分量数量，称为短期雷尼熵（STRE），并通过一种迭代算法进行升级，该迭代算法被证明可以改进现有方法。结合瞬时频率（IF）估计，将该方法应用于无噪声和有噪声环境下的肢体运动脑电图信号的脑电图信号分析，结果表明该方法是一种有效的技术，能够在中等加性噪声水平下提供每个电极位置的脑活动频谱描述。此外，获得的有关脑电图信号分量数量及其瞬时频率的信息显示出增强对患有运动控制疾病的患者进行神经疾病诊断和治疗的潜力。
