An important function of the construction of the Brain-Computer Interface (BCI) device is the development of a model that is able to recognize emotions from electroencephalogram (EEG) signals. Research in this area is very challenging because the EEG signal is non-stationary, non-linear, and contains a lot of noise due to artifacts caused by muscle activity and poor electrode contact. EEG signals are recorded with non-invasive wearable devices using a large number of electrodes, which increase the dimensionality and, thereby, also the computational complexity of EEG data. It also reduces the level of comfort of the subjects. This paper implements our holographic features, investigates electrode selection, and uses the most relevant channels to maximize model accuracy. The ReliefF and Neighborhood Component Analysis (NCA) methods were used to select the optimal electrodes. Verification was performed on four publicly available datasets. Our holographic feature maps were constructed using computer-generated holography (CGH) based on the values of signal characteristics displayed in space. The resulting 2D maps are the input to the Convolutional Neural Network (CNN), which serves as a feature extraction method. This methodology uses a reduced set of electrodes, which are different between men and women, and obtains state-of-the-art results in a three-dimensional emotional space. The experimental results show that the channel selection methods improve emotion recognition rates significantly with an accuracy of 90.76% for valence, 92.92% for arousal, and 92.97% for dominance.

Accurate source localization is an important problem in many research areas as well as practical applications in wireless communications and acoustic signal processing. This paper presents a passive three-dimensional sound source localization (SSL) method that employs a geometric configuration of three soundfield microphones. Two methods for estimating the angle of arrival (AOA) and time difference of arrival (TDOA) are proposed based on Ambisonics A and B format signals. The closed-form solution for sound source location estimation based on two TDOAs and three AOAs is derived. The proposed method is evaluated by simulations and physical experiments in our anechoic chamber. Simulations demonstrate that the estimation method can theoretically obtain Cramér-Rao lower bound for a small Gaussian noise present in AOA and TDOA observations. Investigation on the uncertainty of TDOA and AOA measurements depending on the length of measurement interval is also conducted. Experimental results in terms of RMSE indicate that the proposed solution can be used to accurately find a 3D position of the sound source in free-field environment. Performance evaluation regarding the number of estimation steps shows that higher accuracy can be achieved by longer observations of stationary sound source.