20min 684 Single trial P300 identification for an auditory BCI: implementation of a 3D input for convolutional neural networks Eduardo Carabez*, Miho Sugi, Isao Nambu, Yasuhiro Wada (Japan) 20min 1007 Deep Learning-Based Classification for Brain-Computer Interfaces John Thomas*, Tomasz Maszczyk, Sinha Nishant, Kluge Tilmann, Justin Dauwels 20min 691 Driver’s Fatigue Prediction by Deep Covarianc...

The Coma Recovery Scale-Revised (CRS-R) is a consistent and sensitive behavioral assessment standard for disorders of consciousness (DOC) patients. However, the CRS-R has limitations due to its dependence on behavioral markers, which has led to a high rate of misdiagnosis. Brain-computer interfaces (BCIs), which directly detect brain activities without any behavioral expression, can be used to ...

Human-computer interface (HCI) has been a growing field of research and development in recent years [1]-[4]. Most of the effort has been dedicated to the design of user-friendly and ergonomic systems by means of innovative interfaces such as voice, vision, and other input/output devices in virtual reality [5]-[15]. Direct brain-computer interface (BCI) adds a new dimension to HCI [16]-[23]. Int...

Brain-computer interfaces (BCI) allow users to “communicate” with a computer without using their muscles. BCI based on sensori-motor rhythms use imaginary motor tasks, such as moving the right or left hand, to send control signals. The performances of a BCI can vary greatly across users but also depend on the tasks used, making the problem of appropriate task selection an important issue. This ...

This paper summarizes the Brain-Computer Interfaces for Communication and Control, The Second International Meeting, held in Rensselaerville, NY, in June 2002. Sponsored by the National Institutes of Health and organized by the Wadsworth Center of the New York State Department of Health, the meeting addressed current work and future plans in brain-computer interface (BCI) research. Ninety-two r...

Abstract Objective. In the last decade, advent of code-modulated brain-computer interfaces (BCIs) has allowed implementation systems with high information transfer rates (ITRs) and increased possible practicality such interfaces. this paper, we evaluate effect different numbers targets in stimulus display, modulation sequences generators, signal processing algorithms on accuracy ITR BCIs. Appro...

Brain-controlled wheelchairs (BCWs) are a promising solution for people with severe motor disabilities, who cannot use conventional interfaces. However, the low reliability of electroencephalographic signal decoding and high user's workload imposed by continuous control wheelchair requires effective approaches. In this article, we propose self-paced P300-based brain–computer interface (BCI) com...

A T9 ambiguous keyboard algorithm combined with context-sensitive word selection can reduce the decisions required to ‘type’ a word. This approach can benefit spelling brain-computer interfaces which typically have less than 100% accuracy and slow information transfer rates. We report on initial research in applying a modified context aware T9 predictive text algorithm to BCI spelling. To suppo...

We propose an adaptive classification method for the Brain Computer Interfaces (BCI) which uses Interaction Error Potentials (IErrPs) as a reinforcement signal and adapts the classifier parameters when an error is detected. We analyze the quality of the proposed approach in relation to the misclassification of the IErrPs. In addition we compare static versus adaptive classification performance ...

An electroencephalogram (EEG) signal classification procedure for use in real-time synchronous brain computer interfaces (BCI)is proposed. The features used to perform the classification consist in the coefficients of a discrete wavelet transform (DWT) computed for each trial. A support vector machine (SVM) algorithm has been applied to classify the resultant feature vectors. Some experimental ...