Brain-computer interfaces: neural signal processing and algorithmic approaches

The object of research: The research object is brain-computer interfaces (BCI) and issues related to the acquisition, processing, and analysis of neural signals. Investigated problem: The research focuses on the challenges related to the acquisition and processing of neural signals in brain-computer interface (BCI) systems and the solutions required to improve the system's efficiency. Specifically, issues such as signal weakness, data loss, artifacts, difficulties in real-time operation, and individual adaptation requirements are emphasized. Additionally, the comparison of invasive and non-invasive BCIs, the advantages and limitations of both approaches, and cybersecurity risks are central topics of this study. The goal is to overcome these challenges and develop new signal processing techniques and artificial intelligence algorithms to make BCI technologies more accurate, faster, and reliable. The main scientific results: The factors determining the effectiveness of BCI systems: actors such as the acquisition and processing of neural signals, the algorithms used for signal analysis, hardware, and user feedback are identified as key elements affecting the performance of BCI systems. Comparison of invasive and non-invasive BCIs: Both approaches' advantages and limitations have been reviewed. Invasive BCIs allow for more accurate signal acquisition but require surgical intervention. Non-invasive BCIs, on the other hand, are more comfortable and safer but are prone to artifacts and data loss. Advancements in signal processing methods: The application of new signal processing techniques and artificial intelligence algorithms is emphasized as crucial to improving the efficiency of BCI systems. Individual adaptation and real-time operation challenges: BCI systems' need for individual adaptation and the challenges of real-time operation are highlighted as significant problems that negatively affect system efficiency. Cybersecurity risks: There are cybersecurity risks associated with the remote control of BCIs, which pose a serious threat, particularly for medical implants and neurological devices. Improved signal analysis algorithms: The importance of algorithms, particularly approaches like SVM and LDA, for the classification of motor imagery signals and the correct analysis of signals is emphasized. The area of practical use of the research results: BCI systems can be used in the rehabilitation process for individuals suffering from neurological diseases or physical disabilities. These systems can help restore patients' physical and neural functions. BCI technologies can be applied in controlling robots, especially robotic prosthetics and interactions with the environment for individuals with disabilities. BCIs could allow users to control computers and other technological devices through thought, enabling more natural and comfortable interactions with technology. The use of EEG signals in biometrics could provide a novel approach for identifying individuals and ensuring data security. Innovative technological product: The innovative technological product is BCI systems. This technology connects brain activity directly with computers or other devices, enabling various applications. Specifically, BCIs open new possibilities in medical rehabilitation, robotics, human-computer interaction, security, and biometric identification. The article highlights the importance of applying artificial intelligence algorithms and new signal processing techniques to improve the efficiency of BCI systems. This aims to ensure that BCI systems operate more accurately, quickly, and reliably. Such technologies can lead to significant advancements, particularly in the fields of medical devices and robotics. Scope of the innovative technological product: BCI systems are widely used across various fields. In the medical sector, particularly in neurorehabilitation, they are extensively applied. Brain signals are utilized in the treatment of several neurological disorders. Additionally, in robotics, the development of brain-controlled robotic arms, exoskeletons, and autonomous systems that enhance human capabilities – especially for individuals with limited mobility – is closely linked to the integration of this technology into automation. BCI systems are also successfully implemented in Human-Computer Interaction (HCI), the education sector, and security fields. The uniqueness of brainwave patterns makes BCIs a promising tool for biometric authentication. Unlike traditional security methods, brain-based authentication systems offer a higher level of security. In the modern era, advancements in artificial intelligence, machine learning, and signal processing are making BCIs more efficient and accessible, enabling their broad integration into various aspects of daily life