CONCEPTUAL FRAMEWORK FOR AN EEG-DRIVEN HUMAN BRAIN INTERFACE FOR NEURAL ENCODING AND DECODING

Abstract

A new way of communication has been introduced to the world of technologies in the form of brain-computer interfaces (BCIs) which enable direct interfacing between the human brain and the outer world. The study proposes a conceptual framework for the design of an EEG-based human brain interface for neural encoding and decoding. The design utilizes the EEG signals that effectively measure brain wave patterns and, in turn, transform them into meaningful data. During our study, we present the possibility of employing the most state-of-the-art signal processing methods, the best feature extraction techniques, and the most efficient deep learning models for the sake of getting the most accurate and reliable brain signal interpretation. One major area of work is to remove the noise from the brain signals, allow for real-time processing to cover individual differences in brain signals, and give ideas for expanding future research direction by using neural encoding and decoding mechanisms. The presented conceptual model is meant to be a step in the direction of developing much more versatile and adaptable BCI systems in neuroscience, assistive technology, and cognitive augmentation. This paper was written to examine the potential of an EEG-based human-brain interface (HBI) to help us understand and implement brain encoding and decoding mechanisms. The project is focused on grasping and converting electroencephalogram (EEG) waves which are the signals of cognitive states and the orders sent for communication and control to a computing system. The detailed design of the HBI and the experimental setup are described, including the use of non-invasive EEG recording equipment. In the development of the HBI non-invasive EEG recording equipment is used to characterize naturally occurring mental states and tasks. As for the main part of our approach, we use CT algorithms such as deep neural networks and support vector machines for the EEG data to perform pattern recognition and signal classification tasks. The results reveal a solid efficacy track record of the interface coupled with its phenomenal potential in the field of medical rehabilitation, where it can be seen as especially helpful for movement-impaired individuals, and in the field of human-computer interaction. The challenges covered are signal variability, system adaptability, and user-specific calibration. The rest of the discussion is about future research directions focused on increasing protection and user-friendliness of HBI, as well as on the theoretical applications in neurotherapy and educational settings.