Capsule Network for ERP Detection in Brain-Computer Interface

Mixed Effects Models for Single-Trial ERP Detection in Noninvasive Brain Computer Interface Design

Single-trial evoked response potential detection is a fundamental problem that needs to be solved with high accuracy before noninvasive brain computer interfaces (BCI) can become a widely used practical tool that enables seamless communication with and control of a computer and any peripheral devices that can be connected to it. While in current BCI prototypes multi-trial inference is utilized ...

P 300 detection for brain computer interface

Comparison of Different Linear Filter Design Methods for Handling Ocular Artifacts in Brain Computer Interface System

 Brain-computer interfaces (BCI) record brain signals, analyze and translate them into control commands which are relayed to output devices that carry out desired actions. These systems do not use normal neuromuscular output pathways. Actually, the principal goal of BCI systems is to provide better life style for physically-challenged people which are suffered from cerebral palsy, amyotrophic l...

Computer-Aided Tinnitus Detection based on Brain Network Analysis of EEG Functional Connectivity

Background: Tinnitus known as a central nervous system disorder is correlated with specific oscillatory activities within auditory and non-auditory brain areas. Several studies in the past few years have revealed that in the most tinnitus cases, the response pattern of neurons in auditory system is changed due to auditory deafferentation, which leads to variation and disruption of the brain net...

Optimum Spatio-Spectral Filtering Network for Brain-Computer Interface

This paper proposes a feature extraction method for motor imagery brain-computer interface (BCI) using electroencephalogram. We consider the primary neurophysiologic phenomenon of motor imagery, termed event-related desynchronization, and formulate the learning task for feature extraction as maximizing the mutual information between the spatio-spectral filtering parameters and the class labels....