OBJECTIVE Patients usually require long-term training for effective EEG-based brain-computer interface (BCI) control due to fatigue caused by the demands for focused attention during prolonged BCI operation. We intended to develop a user-friendly BCI requiring minimal training and less mental load. METHODS Testing of BCI performance was investigated in three patients with amyotrophic lateral ...

Several studies have explored brain computer interface (BCI) systems based on auditory stimuli, which could help patients with visual impairments. Usability and user satisfaction are important considerations in any BCI. Although background music can influence emotion and performance in other task environments, and many users may wish to listen to music while using a BCI, auditory, and other BCI...

Most Brain-Computer Interfaces (BCI) are based on machine learning and behave like black boxes, i.e., they cannot be interpreted. However, designing interpretable BCI would enable to discuss, verify or improve what the BCI has automatically learnt from brain signals, or possibly gain new insights about the brain. In this paper, we present an algorithm to design a fully interpretable BCI. It can...

This paper presents a virtual reality brain computer interface (BCI) system which allows the user to interact physical objects in the ubiquitous home. The system is designed for motion disabled people to control real home facilities by a simple motor imagery and minimal physical body movements. While BCI research has mostly focused on improving classification algorithm, our BCI system uses a si...

PURPOSE Previous results from the TransATAC study demonstrated that both the Breast Cancer Index (BCI) and the OncotypeDX Recurrence Score (RS) added significant prognostic information to clinicopathologic factors over a 10-year period. Here, we examined cross-stratification between BCI and RS to directly compare their prognostic accuracy at the individual patient level. EXPERIMENTAL DESIGN A...

Modulation of sensorimotor rhythms (SMR) was suggested as a control signal for brain-computer interfaces (BCI). Yet, there is a population of users estimated between 10 to 50% not able to achieve reliable control and only about 20% of users achieve high (80-100%) performance. Predicting performance prior to BCI use would facilitate selection of the most feasible system for an individual, thus c...

In the last years Brain Computer Interface (BCI) technology has benefited from the development of sophisticated machine leaning methods that let the user operate the BCI after a few trials of calibration. One remarkable example is the recent development of co-adaptive techniques that proved to extend the use of BCIs also to people not able to achieve successful control with the standard BCI pro...

A brain-computer interface (BCI) is a device that enables severely disabled people to communicate and interact with their environments using their brain waves. Most research investigating BCI in humans have used scalp-recorded electroencephalography (EEG). We have recently demonstrated that signals from intracranial electrocorticography (ECoG) and stereotactic depth electrodes (SDE) in the hipp...