Uşakli Ali Bülent
Department of Technical Sciences, The NCO Academy, Balikesir, TR, Turkey.
J Comput Neurosci. 2010 Jun;28(3):595-603. doi: 10.1007/s10827-010-0242-7. Epub 2010 May 7.
In bio-signal applications, classification performance depends greatly on feature extraction, which is also the case for electroencephalogram (EEG) based applications. Feature extraction, and consequently classification of EEG signals is not an easy task due to their inherent low signal-to-noise ratios and artifacts. EEG signals can be treated as the output of a non-linear dynamical (chaotic) system in the human brain and therefore they can be modeled by their dimension values. In this study, the variance fractal dimension technique is suggested for the modeling of movement-related potentials (MRPs). Experimental data sets consist of EEG signals recorded during the movements of right foot up, lip pursing and a simultaneous execution of these two tasks. The experimental results and performance tests show that the proposed modeling method can efficiently be applied to MRPs especially in the binary approached brain computer interface applications aiming to assist severely disabled people such as amyotrophic lateral sclerosis patients in communication and/or controlling devices.
在生物信号应用中，分类性能在很大程度上取决于特征提取，基于脑电图（EEG）的应用也是如此。由于脑电图信号固有的低信噪比和伪迹，其特征提取以及随后的分类并非易事。脑电图信号可被视为人类大脑中非线性动态（混沌）系统的输出，因此可以通过其维度值进行建模。在本研究中，提出了方差分形维技术用于与运动相关电位（MRP）的建模。实验数据集由右脚向上运动、噘嘴以及这两项任务同时执行期间记录的脑电图信号组成。实验结果和性能测试表明，所提出的建模方法可以有效地应用于运动相关电位，特别是在旨在帮助严重残疾人士（如肌萎缩侧索硬化症患者）进行通信和/或控制设备的二元脑机接口应用中。
