Brain-Computer Interfaces (BCIs) allow users to control devices and communicate by using brain activity only. BCIs based on broad-band visual stimulation can outperform BCIs using other stimulation paradigms. Visual stimulation with pseudo-random bit-sequences evokes specific Broad-Band Visually Evoked Potentials (BBVEPs) that can be reliably used in BCI for high-speed communication in speller ...

Brain-Computer Interfaces (BCIs) are devices offering alternative means of communication when conventional means are permanently, or nonpermanently, impaired. The latter is commonly induced in general anesthesia and is necessary for the conduction of the surgery. However, in some cases it is possible that the patient regains consciousness during surgery, but cannot directly communicate this to ...

Brain-Computer Interfaces (BCIs) measure brain signals activity, intentionally and unintentionally induced by users, and provides a communication channel without depending on the brain’s normal peripheral nerves and muscles output pathway. Feature Selection (FS) is a global optimization machine learning problem that reduces features, removes irrelevant and noisy data resulting in acceptable rec...

Brain-computer interfaces (BCIs) are an emerging novel technology for stroke rehabilitation. Little is known about how dose-response relationships for BCI therapies affect brain and behavior changes. We report preliminary results on stroke patients (n = 16, 11 M) with persistent upper extremity motor impairment who received therapy using a BCI system with functional electrical stimulation of th...

INTRODUCTION Brain computer interfaces (BCIs) are systems that enable the brain to send and receive information to and from a computer, bypassing the body’s own efferent and afferent pathways. BCIs have been used in experimental animal models to augment perception, motor control and even memory (Velliste et al., 2008; Berger et al., 2011; Torab et al., 2011). Human BCIs include cochlear implant...

A brain computer interface (BCI) enables direct communication between a brain and a computer translating brain activity into computer commands using preprocessing, feature extraction and classification operations. Classification is crucial as it has a substantial effect on the BCI speed and bit rate. Recent developments of brain–computer interfaces (BCIs) bring forward some challenging problems...

Electroencephalographic (EEG) signals are generally non-stationary, however, nearly stationary brain responses, such as steady-state visually evoked potentials (SSVEP), can be recorded in response to repetitive stimuli. Although Fourier transform has precise resolution with long time windows (5 or 10 s for instance) to extract SSVEP response (1-100 Hz ranges), its resolution with shorter window...

Brain-Computer Interfaces (BCIs) allow users to control a computer application by brain activity as acquired, e.g., by EEG. In our classic Machine Learning approach to BCIs, the participants undertake a calibration measurement without feedback to acquire data to train the BCI system. After the training, the user can control a BCI and improve the operation through some type of feedback. However,...

Recently, methodologies for developing brain-computer interface (BCI) games using the BCI have been actively researched. The existing general framework for processing brain waves does not provide the functions required to develop BCI games. Thus, developing BCI games is difficult and requires a large amount of time. Effective BCI game development requires a BCI game framework. Therefore the BCI...

We present a hybrid brain-computer interface (HBCI) composed of a motor imagery-based brain switch and a headtracking device. Normal gaze-only (either head or eye gaze) interfaces suffer from a Midas Touch problem where unwanted selection of commands is triggered by subjects gazing at objects for too long. We use a BCI to provide a nontouch communication channel. Subjects were able to select an...