Papers Brain Computer Interface
“Brain Computer Interface” 태그가 달린 논문 466편 · 필터 해제
On improving learning capability of ELM and an application to brain-computer interface
As a type of pseudoinverse learning, extreme learning machine (ELM) is able to achieve high performances in a rapid pace on benchmark datasets. However, when it is applied to real life large data, decline related to low-…
Brain Computer InterfaceProtecting Privacy of Users in Brain-Computer Interface Applications
Machine learning (ML) is revolutionizing research and industry. Many ML applications rely on the use of large amounts of personal data for training and inference. Among the most intimate exploited data sources is electro…
Brain Computer InterfaceEEGElectroencephalogram (EEG)Privacy PreservingDomain Adaptation with Optimal Transport on the Manifold of SPD matrices
In this paper, we address the problem of Domain Adaptation (DA) using Optimal Transport (OT) on Riemannian manifolds. We model the difference between two domains by a diffeomorphism and use the polar factorization theore…
Brain Computer InterfaceDomain AdaptationNoisy Neural Language Modeling for Typing Prediction in BCI Communication
Language models have broad adoption in predictive typing tasks. When the typing history contains numerous errors, as in open-vocabulary predictive typing with brain-computer interface (BCI) systems, we observe significan…
Brain Computer InterfaceLanguage ModelingLanguage ModellingSimultaneous induction of SSMVEP and SMR Using a Gaiting video stimulus: a novel hybrid brain-computer interface
We proposed a novel visual stimulus for brain-computer interface. The stimulus is in the form gaiting sequence of a human. The hypothesis is that observing such a visual stimulus would simultaneously induce 1) steady-sta…
Brain Computer InterfaceEEGElectroencephalogram (EEG)Rhythm+1Adaptive Subspace Sampling for Class Imbalance Processing-Some clarifications, algorithm, and further investigation including applications to Brain Computer Interface
Kohonen's Adaptive Subspace Self-Organizing Map (ASSOM) learns several subspaces of the data where each subspace represents some invariant characteristics of the data. To deal with the imbalance classification problem, e…
Brain Computer InterfaceClassificationEEGElectroencephalogram (EEG)+2A Survey on Deep Learning-based Non-Invasive Brain Signals:Recent Advances and New Frontiers
Brain-Computer Interface (BCI) bridges the human's neural world and the outer physical world by decoding individuals' brain signals into commands recognizable by computer devices. Deep learning has lifted the performance…
Brain Computer InterfaceDeep LearningAttention-based Transfer Learning for Brain-computer Interface
Different functional areas of the human brain play different roles in brain activity, which has not been paid sufficient research attention in the brain-computer interface (BCI) field. This paper presents a new approach …
Brain Computer InterfaceClassificationEEGElectroencephalogram (EEG)+2SPEAK YOUR MIND! Towards Imagined Speech Recognition With Hierarchical Deep Learning
Speech-related Brain Computer Interface (BCI) technologies provide effective vocal communication strategies for controlling devices through speech commands interpreted from brain signals. In order to infer imagined speec…
Brain Computer InterfaceGeneral Classificationspeech-recognitionSpeech Recognition+1On the Vulnerability of CNN Classifiers in EEG-Based BCIs
Deep learning has been successfully used in numerous applications because of its outstanding performance and the ability to avoid manual feature engineering. One such application is electroencephalogram (EEG) based brain…
Brain Computer InterfaceEEGElectroencephalogram (EEG)Feature EngineeringInformation Theoretic Feature Transformation Learning for Brain Interfaces
Objective: A variety of pattern analysis techniques for model training in brain interfaces exploit neural feature dimensionality reduction based on feature ranking and selection heuristics. In the light of broad evidence…
Brain Computer InterfaceDimensionality ReductionEEGElectroencephalogram (EEG)+2An Ensemble Learning Based Classification of Individual Finger Movement from EEG
Brain computer interface based assistive technology are currently promoted for motor rehabilitation of the neuromuscular ailed individuals. Recent studies indicate a high potential of utilising electroencephalography (EE…
Brain Computer InterfaceEEGElectroencephalogram (EEG)Ensemble LearningRecurrent Neural Networks for P300-based BCI
P300-based spellers are one of the main methods for EEG-based brain-computer interface, and the detection of the P300 target event with high accuracy is an important prerequisite. The rapid serial visual presentation (RS…
Brain Computer InterfaceEEGElectroencephalogram (EEG)A GA-based feature selection of the EEG signals by classification evaluation: Application in BCI systems
In electroencephalogram (EEG) signal processing, finding the appropriate information from a dataset has been a big challenge for successful signal classification. The feature selection methods make it possible to solve t…
Brain Computer InterfaceEEGElectroencephalogram (EEG)feature selection+1PFML-based Semantic BCI Agent for Game of Go Learning and Prediction
This paper presents a semantic brain computer interface (BCI) agent with particle swarm optimization (PSO) based on a Fuzzy Markup Language (FML) for Go learning and prediction applications. Additionally, we also establi…
Brain Computer InterfaceGame of GoReal-Time EEG Classification via Coresets for BCI Applications
A brain-computer interface (BCI) based on the motor imagery (MI) paradigm translates one's motor intention into a control signal by classifying the Electroencephalogram (EEG) signal of different tasks. However, most exis…
Brain Computer InterfaceClassificationData SummarizationEEG+3Exploring Embedding Methods in Binary Hyperdimensional Computing: A Case Study for Motor-Imagery based Brain-Computer Interfaces
Key properties of brain-inspired hyperdimensional (HD) computing make it a prime candidate for energy-efficient and fast learning in biosignal processing. The main challenge is however to formulate embedding methods that…
Brain Computer InterfaceEEGElectroencephalogram (EEG)Motor Imagery+1An Analysis of the Accuracy of the P300 BCI
The P300 Brain-Computer Interface (BCI) is a well-established communication channel for severely disabled people. The P300 event-related potential is mostly characterized by its amplitude or its area, which correlate wit…
Brain Computer InterfaceGeneral ClassificationUse of Neural Signals to Evaluate the Quality of Generative Adversarial Network Performance in Facial Image Generation
There is a growing interest in using generative adversarial networks (GANs) to produce image content that is indistinguishable from real images as judged by a typical person. A number of GAN variants for this purpose hav…
Brain Computer InterfaceGenerative Adversarial NetworkImage GenerationAn amplitudes-perturbation data augmentation method in convolutional neural networks for EEG decoding
Brain-Computer Interface (BCI) system provides a pathway between humans and the outside world by analyzing brain signals which contain potential neural information. Electroencephalography (EEG) is one of most commonly us…
Brain Computer InterfaceData AugmentationDeep LearningEEG+2