Current non-invasive brain-computer interfaces such as those based on electroencephalography (EEG) [1] suffer from the problem of low signal-to-noise ratio, making fine-grained moment-by-moment control tedious and exhausting for users. To address this problem, we have previously proposed an adaptive hierarchical approach to brain-computer interfacing: users teach the BCI system new skills on-th...

OBJECTIVE While brain-computer interfaces (BCIs) for communication have reached considerable technical maturity, there is still a great need for state-of-the-art evaluation by the end-users outside laboratory environments. To achieve this primary objective, it is necessary to augment a BCI with a series of components that allow end-users to type text effectively. APPROACH This work presents t...

Brain-computer interfaces (BCIs) enable people with paralysis to communicate with their environment. Motor imagery can be used to generate distinct patterns of cortical activation in the electroencephalogram (EEG) and thus control a BCI. To elucidate the cortical correlates of BCI control, users of a sensory motor rhythm (SMR)-BCI were classified according to their BCI control performance. In a...

As the proliferation of technology dramatically infiltrates all aspects of social life, engineering will continue to intertwine the human brain with technology, thus forming integrated neurotechnological systems. Major forerunners of such a conception are brain–computer interfaces (BCIs), which are based on a direct communication pathway between the human brain and an external device. First dev...

A case of partial recovery after stroke and its associated brain reorganization in a chronic patient after combined brain computer interface (BCI) training and physiotherapy is presented. A multimodal neuroimaging approach based on fMRI and diffusion tensor imaging was used to investigate plasticity of the brain motor system in parallel with longitudinal clinical assessments. A convergent assoc...

This paper investigates neurotechnologies for developing brain-computer interaction (BCI) based on the generative deep learning Stable Diffusion model. An algorithm modeling BCI is proposed and its training testing artificial data described. The results are encouraging researchers can be used in various areas of BCI, such as distance learning, remote medicine creation robotic humanoids, etc.

This project discussed about a brain controlled robot based on Brain–computer interfaces (BCI). BCIs are systems that can bypass conventional channels of communication (i.e., muscles and thoughts) to provide direct communication and control between the human brain and physical devices by translating different patterns of brain activity into commands in real time. With these commands a mobile ro...

Brain-Computer Interfaces (BCI) based on motor imagery translate mental images recognized from the electroencephalogram (EEG) to control commands. EEG patterns of different imagination tasks, e.g. hand and foot movements, are effectively classified with machine learning techniques using band power features. Recently, also Convolutional Neural Networks (CNNs) that learn both effective features c...

Motor imagery (MI)-based brain–computer interfaces (BCI) have shown increased potential for the rehabilitation of stroke patients; nonetheless, their implementation in clinical practice has been restricted due to low accuracy performance. To date, although a lot research carried out benchmarking and highlighting most valuable classification algorithms BCI configurations, them use offline data a...