This review outlines the technological principles of neural–computer interface (NCI) construction, classifying them according to: (1) the degree of intervention (invasive, semi-invasive, and non-invasive); (2) the direction of signal communication, including BCI (brain–computer interface) for converting neural activity into commands for external devices, CBI (computer–brain interface) for translating artificial signals into stimuli for the CNS, and BBI (brain–brain interface) for direct brain-to-brain interaction systems that account for agency; and (3) the mode of user interaction with technology (active, reactive, passive). For each NCI type, we detail the fundamental data processing principles, covering signal registration, digitization, preprocessing, classification, encoding, command execution, and stimulation, alongside engineering implementations ranging from EEG/MEG to intracortical implants and from transcranial magnetic stimulation (TMS) to intracortical microstimulation (ICMS). We also review mathematical modeling methods for NCIs, focusing on optimizing the extraction of informative features from neural signals—decoding for BCI and encoding for CBI—followed by a discussion of quasi-real-time operation and the use of DSP and neuromorphic chips. Quantitative metrics and rehabilitation measures for evaluating NCI system effectiveness are considered. Finally, we highlight promising future research directions, such as the development of electrochemical interfaces, biomimetic hierarchical systems, and energy-efficient technologies capable of expanding brain functionality.

Sense of agency (SoA) refers to an individual’s awareness of their own actions. SoA studies seek to find objective indicators for the feeling of agency. These indicators, being related to the feeling of control, have practical application in vehicle design. However, they have not been investigated for actions related to the agent’s body movement inherent to steering a vehicle. In our study, participants operated a robotic wheelchair under three conditions: active control by a participant, direct control by the experimenter and remote control by the experimenter. In each trial, a participant drove the wheelchair until a sound signal occurred, after which they stopped the wheelchair and estimated the travelled distance. The subjective estimates were significantly greater when participants operated the wheelchair by themselves. This result contrasts with observations under static settings in previous studies. In an additional study on the electroencephalographic response to a sound presented at a random time after movement onset, the observed latencies in the N1 component implied that participants might have a higher sense of control when they drove the wheelchair. The proposed methodology might become useful to indirectly assess the degree of operator control of a vehicle, primarily in the field of rehabilitation technologies.