Steady-state visual evoked potential (SSVEP)-brain-computer interface (BCI) has fast input speeds, high accuracy, and a large number of inputs. The problem with SSVEP is the stress caused by flickering stimuli risk developing photosensitivity. In this study, we attempted to reduce flicker high-frequency SSVEP-BCI. addition, measured critical fusion frequency (CFF) at which humans cannot perceiv...

Recent experiments with highresolution brain imaging techniques provide an amazing view on the complex spatio-temporal dynamics of cortical processes. There is ample of evidence pointing to frequent transitions between periods of large-scale synchronization and intermittent desynchronization at alpha-theta rates (period length of 0.1 s to 0.25s). These observations have been interpreted based o...

PURPOSE To propose a screening protocol that identifies requisite sensory, motor, cognitive and communication skills for people with locked-in syndrome (PLIS) to use the RSVP Keyboard™ brain-computer interface (BCI). METHOD A multidisciplinary clinical team of seven individuals representing five disciplines identified requisite skills for the BCI RSVP Keyboard™. They chose questions and subte...

This study describes a method for classifying electrocorticograms (ECoGs) based on motor imagery (MI) the brain–computer interface (BCI) system. is different from traditional feature extraction and classification method. In this paper, proposed employs deep learning algorithm extracting features classification. Specifically, we mainly use convolution neural network (CNN) to extract training dat...

A Brain-Computer Interface (BCI) is a communication system that decodes and transfers information directly from the brain to external devices. The electroencephalogram (EEG) technique used measure electrical signals corresponding commands occurring in control functions. for applications BCI are called Motor Imagery (MI) EEG signals. noisy, so it important use right methods recognize patterns co...

[Purpose] This study sought to determine the effects of brain-computer interface-based functional electrical stimulation (BCI-FES) on brain activation in patients with stroke. [Subjects] The subjects were randomized to in a BCI-FES group (n=5) and a functional electrical stimulation (FES) group (n=5). [Methods] Patients in the BCI-FES group received ankle dorsiflexion training with FES for 30 m...

The combination of signal processing and Artificial Intelligence (AI) is revolutionizing the robotics automation industry by deployment intelligent systems reducing human intervention. Reading brain through electroencephalography (EEG) has provided a new direction research that automate machines computer interface or Brain–Computer Interface (BCI). study also inspired same concept intelligently...

Cerebellar ataxia is a steadily progressive neurodegenerative disease associated with loss of motor control, leaving patients unable to walk, talk, or perform activities of daily living. Direct motor instruction in cerebella ataxia patients has limited effectiveness, presumably because an inappropriate closed-loop cerebellar response to the inevitable observed error confounds motor learning mec...

Brain Computer Interface (BCI) systems still suffer from lack of accuracy in real-time applications. This problem emerges from isolated optimization, and in some occasions from mismatching of feature extraction and classification stages. To unify optimization of both stages, this paper presents a novel scheme to integrate them and simultaneously optimize under a unit criterion. The proposed met...

Consider an increasingly growing field of research, Brain-Computer Interface (BCI) is to form a direct channel communication between computer and the brain. However, extracting features random time-varying EEG signals their classification major challenge that faces current BCI. This paper proposes modified grey wolf optimizer (MGWO) can select optimal channels be used in (BCIs), way identifies ...