Identification of EEG signals is currently an open problem where performance analysis in terms of accuracy is relevant in several fields, such as biomedicine and brain computer interfaces. Nevertheless, performance depends on the feature extraction phase, where the aim is to find relevant patterns related to different mental activities. Thus, in this work, an approach to extract features from EEG signals is proposed based on spectrograms: Firstly, STFT is applied to EEG to obtain time-frequency representations, where parameters such as window length and type are experimented based on the EEG signal frequency. After that, spectral peaks are found to be used as reference in order to obtain descriptors per spectrogram. Three ways for extracting features from EEG are presented, the first based on frequency and surfaces, the second using K-means to extract features and the adaptation of local ternary pattern, and finally, a third using maximum peaks. The extracted descriptors are evaluated by means of a multilayer perceptron, support vector machines, and k-nearest neighbors. The proposed approach was evaluated using two epileptic seizure dataset from Bonn University, identifying a healthy person and an epileptic attack classes. According to the experimental results, the proposed method obtains acceptable accuracy (100%) in several cases by considering fewer features than those extracted by other related works.

脑电信号的识别目前是一个悬而未决的问题，其中在准确性方面的性能分析在多个领域（如生物医学和脑计算机接口）中至关重要。然而，性能取决于特征提取阶段，其目的是找到与不同心理活动有关的相关模式。因此，在这项工作中，提出了一种基于频谱图的从脑电信号中提取特征的方法：首先，将STFT应用于脑电图以获取时频表示，并根据脑电信号频率对诸如窗口长度和类型之类的参数进行实验。 。此后，发现频谱峰可用作参考，以便获得每个频谱图的描述符。提出了三种从EEG中提取特征的方法，第一种基于频率和表面，第二个使用K均值提取特征和局部三元模式的适应性，最后一个使用最大峰值。提取的描述符通过多层感知器，支持向量机和k近邻进行评估。使用波恩大学的两个癫痫发作数据集对提出的方法进行了评估，确定了一个健康的人和一个癫痫发作类别。根据实验结果，该方法通过考虑比其他相关工作提取的特征更少的特征，在某些情况下获得了可接受的准确性（100％）。使用波恩大学的两个癫痫发作数据集对提出的方法进行了评估，确定了一个健康的人和一个癫痫发作类别。根据实验结果，通过考虑比其他相关工作提取的特征更少的特征，该方法在某些情况下获得了可接受的准确性（100％）。使用波恩大学的两个癫痫发作数据集对提出的方法进行了评估，确定了一个健康的人和一个癫痫发作类别。根据实验结果，该方法通过考虑比其他相关工作提取的特征更少的特征，在某些情况下获得了可接受的准确性（100％）。