Abstract
Brain-computer interfaces (BCIs) enable direct communication between the brain and information technologies, translating brain activity recorded intracranially into commands. Recent advances in BCIs have utilised multimodal approaches, such as electroencephalography (EEG)-based systems in combination with other biosignals, as well as deep learning to improve the efficiency and reliability of such technologies. Due to the inherent uncertainty of the data of electroencephalogram (EEG) patterns, traditional EEG diagnostic methods often face difficulties. Specifically, in multiple neurological disorders, the main motivation is to overcome the limitations of existing methods that are unable to cope with the complex and overlapping nature of EEG signals. In this paper, the use of Karhunen-Loève decomposition functions for the analysis of spatiotemporal EEG signals in a state of calm mental load in healthy persons and patients with nervous disorders is considered. Approaches in the time, frequency, and time-frequency domains are considered. The results in this study show the relationship between EEG modulation during a cognitive task involving healthy people of the control group and the pathological mental state of patients, according to the results of Karhunen-Loève decomposition in pre-selected EEG frequency ranges. The results given in this paper improve the quality and speed of recognising emotional states of patients with emotional expression disorders from the EEG signal, and also develop brain-computer interface (BCI) technologies, including for the application of artificial intelligence.