The classification of electroencephalogram (EEG) signals plays a key role in detecting brain activities. Fuzzy methods are widely applied in decision-making problems because they are effective tools for handling imprecise and vague data. This paper proposes a modified algorithm to calculate the center of gravity of generalized trapezoidal fuzzy numbers. Accordingly, we introduce a new similarity measure for generalized trapezoidal fuzzy numbers that we use in the classification of EEG signals. This measure combines the height, the center of gravity, the perimeter, the area, and the gyradius of generalized trapezoidal fuzzy numbers to quantify the similarity between generalized trapezoidal fuzzy numbers. We use 16 sets of generalized trapezoidal fuzzy numbers to compare the proposed similarity measure with existing ones. Comparison results indicate that the proposed similarity measure can overcome the drawbacks of existing similarity measures. Finally, an EEG experiment is carried out in laboratory. Experimental results demonstrate that the proposed similarity measure is more effective than other methods in terms of classification of EEG signals.

脑电图（EEG）信号的分类在检测大脑活动中起关键作用。由于模糊方法是处理不精确和模糊数据的有效工具，因此被广泛应用于决策问题。提出了一种改进的算法来计算广义梯形模糊数的重心。因此，我们为脑电信号分类中使用的广义梯形模糊数引入了一种新的相似性度量。该度量结合了高度，重心，周长，面积和广义梯形模糊数的回旋，以量化广义梯形模糊数之间的相似性。我们使用16组广义梯形模糊数将拟议的相似性度量与现有度量进行比较。比较结果表明，所提出的相似性度量可以克服现有相似性度量的弊端。最后，在实验室进行了脑电图实验。实验结果表明，所提出的相似性度量在脑电信号分类方面比其他方法更有效。