Brain-Computer Interfaces最新文献

Electroencephalography (EEG)-based brain-computer interface (BCI) systems infer brain signals recorded via EEG without using common neuromuscular pathways. User brain response to BCI error is a contributor to non-stationarity of the EEG signal and poses challenges in developing reliable active BCI control. Many passive BCI implementations, on the other hand, have the detection of error-related brain activity as their primary goal. Therefore, reliable detection of this signal is crucial in both active and passive BCIs. In this work, we propose CREST: a novel covariance-based method that uses Riemannian and Euclidean geometry and combines spatial and temporal aspects of the feedback-related brain activity in response to BCI error. We evaluate our proposed method with two datasets: an active BCI for 1-D cursor control using motor imagery and a passive BCI for 2-D cursor control. We show significant improvement across participants in both datasets compared to existing methods.

Brain-Computer Interfaces (BCIs) have been used to restore communication and control to people with severe paralysis. However, non-invasive BCIs based on electroencephalogram (EEG) are particularly vulnerable to noise artifacts. These artifacts, including electro-oculogram (EOG), can be orders of magnitude larger than the signal to be detected. Many automated methods have been proposed to remove EOG and other artifacts from EEG recordings, most based on blind source separation. This work presents a performance comparison of ten different automated artifact removal methods. Unfortunately, all tested methods substantially and significantly reduced P3 Speller BCI performance, and all methods were more likely to reduce performance than increase it. The least harmful methods were titled SOBI, JADER, and EFICA, but even these methods caused an average of approximately ten percentage points drop in BCI accuracy. Possible mechanistic causes for this empirical performance deduction are proposed.

The Seventh International Brain-Computer Interface (BCI) Meeting was held May 21-25th, 2018 at the Asilomar Conference Grounds, Pacific Grove, California, United States. The interactive nature of this conference was embodied by 25 workshops covering topics in BCI (also called brain-machine interface) research. Workshops covered foundational topics such as hardware development and signal analysis algorithms, new and imaginative topics such as BCI for virtual reality and multi-brain BCIs, and translational topics such as clinical applications and ethical assumptions of BCI development. BCI research is expanding in the diversity of applications and populations for whom those applications are being developed. BCI applications are moving toward clinical readiness as researchers struggle with the practical considerations to make sure that BCI translational efforts will be successful. This paper summarizes each workshop, providing an overview of the topic of discussion, references for additional information, and identifying future issues for research and development that resulted from the interactions and discussion at the workshop.

Much brain-computer interface (BCI) research is intended to benefit people with disabilities (PWD), but inclusion of these individuals as study participants remains relatively rare. When participants with disabilities are included, they are described with a range of clinical and non-clinical terms with varying degrees of specificity, often leading to difficulty in interpreting or replicating results. This study examined trends in inclusion and description of study participants with disabilities across six International BCI Meetings from 1999 to 2016. Abstracts from each Meeting were analyzed by two trained independent reviewers. Results suggested a decline in participation by PWD across Meetings until the 2016 Meeting. Increased diagnostic specificity was noted at the 2013 and 2016 Meetings. Fifty-eight percent of the abstracts identified PWD as being the target beneficiaries of BCI research, though only twenty-two percent included participants with disabilities, suggesting evidence of a persistent translational gap. Participants with disabilities were most commonly described as having physical and/or communication impairments compared to impairments in other areas. Implementing participatory action research principles and user-centered design strategies continues to be necessary within BCI research to bridge the translational gap and facilitate use of BCI systems within functional environments for PWD.