Department of Biomedical Engineering, Inonu University, Malatya, Turkey.
Department of Biomedical Engineering, Erciyes University, Kayseri, Turkey.
Comput Methods Biomech Biomed Engin. 2022 Jun;25(8):840-851. doi: 10.1080/10255842.2021.1983803. Epub 2021 Oct 4.
This study, it was aimed to contribute to the literature on Amyotrophic lateral sclerosis (ALS) diagnosis and Brain-Computer Interface (BCI) technologies by analyzing the electroencephalography (EEG) signals obtained as a result of visual stimuli and attention from ALS patients and healthy controls. It was observed that the success rate significantly increased both in the occipital and central regions in all classifiers, especially in the entropy features. The most successful classification was obtained with the Naïve Bayes (NB) classifier using the Morphological Features (MF) + Variational Mode Decomposition (VMD) -Entropy features at 88.89% in the occipital region and 94.44% in the central region.
本研究旨在通过分析肌萎缩侧索硬化症 (ALS) 患者和健康对照者的视觉刺激和注意力引起的脑电图 (EEG) 信号，为肌萎缩侧索硬化症诊断和脑-机接口 (BCI) 技术的文献做出贡献。结果表明，在所有分类器中，尤其是在熵特征中，枕部和中央区域的成功率都显著提高。使用形态学特征 (MF) + 变分模态分解 (VMD) -熵特征，贝叶斯 (NB) 分类器在枕部区域获得了 88.89%的最高成功率，在中央区域获得了 94.44%的成功率。
