Emotions are human reactions to events and they affect the whole body. An important function of creating a Brain-Computer Interface (BCI) device is the development of a model that is able to recognize emotions from electroencephalogram (EEG) signals. It is a challenging task to develop an intelligent framework that can provide high accuracy of emotion recognition due to the nature of brain signals. The EEG is non-stationary, non-linear, and contains a significant amount of noise caused by, for example, muscle activity, blinking, breathing, heart rate, or poor electrode contact. Moreover, when recording EEG signals with non-invasive wearable devices, a large number of electrodes are often used, which increases the computational complexity, dimensionality of EEG data, and reduces the level of comfort of the subjects. This dissertation proposes a new model for emotion recognition based on feature maps created using the topographic and holographic representation of EEG signal characteristics. The signals recorded by the electrodes of the measuring device are divided into subbands using discrete wavelet transform, and at each subband, the characteristics of the signals are calculated and mapped to a standard international system that describes the positions of the electrodes on the head. The position of the point in the three-dimensional space is defined by displaying the value of the signal characteristic at the electrode location. Investigation of signal characteristics in three-dimensional space is carried out in two directions. For the first, computer-generated holography is used, which creates two-dimensional feature maps from the spatial characteristics of the signal, while in the other direction of research, the values of the signal characteristics are shown by a topographic map. The ReliefF and Neighborhood Component Analysis methods were used in electrode selection research to optimize and increase model accuracy. The deep learning approach using a convolutional neural network was used to extract features from feature maps, and the characteristics obtained from each individual neural network are combined into a matrix of features and then classified. Recognition of emotional states was performed on all respondents together, but also separately depending on the gender of the respondents. The newly proposed model has been verified on four publicly available datasets: DEAP, DREAMER, AMIGOS, and SEED. The effectiveness of the proposed approach has been demonstrated compared to the state-of-the-art studies in which the authors use EEG signals to classify human emotions into three-dimensional emotional space. Experimental results show that the proposed approach can improve the rate of emotion recognition.