Papers Brain Computer Interface
“Brain Computer Interface” 태그가 달린 논문 466편 · 필터 해제
Relevance Feedback with Brain Signals
The Relevance Feedback (RF) process relies on accurate and real-time relevance estimation of feedback documents to improve retrieval performance. Since collecting explicit relevance annotations imposes an extra burden on…
Brain Computer InterfaceRe-RankingA Literature Review on the Smart Wheelchair Systems
This study offers an in-depth analysis of smart wheelchair (SW) systems, charting their progression from early developments to future innovations. It delves into various Brain-Computer Interface (BCI) systems, including …
Autonomous NavigationBrain Computer InterfaceRhythmDTP-Net: Learning to Reconstruct EEG signals in Time-Frequency Domain by Multi-scale Feature Reuse
Electroencephalography (EEG) signals are easily corrupted by various artifacts, making artifact removal crucial for improving signal quality in scenarios such as disease diagnosis and brain-computer interface (BCI). In t…
Brain Computer InterfaceDenoisingEEGEEG Denoising+2Visual tracking brain computer interface
Brain-computer interfaces (BCIs) offer a way to interact with computers without relying on physical movements. Non-invasive electroencephalography (EEG)-based visual BCIs, known for efficient speed and calibration ease, …
Brain Computer Interfacecontinuous-controlContinuous ControlEEG+1Enhanced Generative Adversarial Networks for Unseen Word Generation from EEG Signals
Recent advances in brain-computer interface (BCI) technology, particularly based on generative adversarial networks (GAN), have shown great promise for improving decoding performance for BCI. Within the realm of Brain-Co…
Brain Computer InterfaceData AugmentationEEGspeech-recognition+1Sample Dominance Aware Framework via Non-Parametric Estimation for Spontaneous Brain-Computer Interface
Deep learning has shown promise in decoding brain signals, such as electroencephalogram (EEG), in the field of brain-computer interfaces (BCIs). However, the non-stationary characteristics of EEG signals pose challenges …
Brain Computer InterfaceEEGElectroencephalogram (EEG)EEG-DG: A Multi-Source Domain Generalization Framework for Motor Imagery EEG Classification
Motor imagery EEG classification plays a crucial role in non-invasive Brain-Computer Interface (BCI) research. However, the classification is affected by the non-stationarity and individual variations of EEG signals. Sim…
Brain Computer InterfaceClassificationDomain AdaptationDomain Generalization+2Neuro-GPT: Towards A Foundation Model for EEG
To handle the scarcity and heterogeneity of electroencephalography (EEG) data for Brain-Computer Interface (BCI) tasks, and to harness the power of large publicly available data sets, we propose Neuro-GPT, a foundation m…
Brain Computer InterfaceEEGmodelMotor ImageryImproved Motor Imagery Classification Using Adaptive Spatial Filters Based on Particle Swarm Optimization Algorithm
As a typical self-paced brain-computer interface (BCI) system, the motor imagery (MI) BCI has been widely applied in fields such as robot control, stroke rehabilitation, and assistance for patients with stroke or spinal …
Brain Computer InterfaceClassificationEEGEEG Signal Classification+2Neuromorphic Online Clustering and Classification
The bottom two layers of a neuromorphic architecture are designed and shown to be capable of online clustering and supervised classification. An active spiking dendrite model is used, and a single dendritic segment perfo…
Brain Computer InterfaceClassificationClusteringOnline Clustering+1Reputation-Based Federated Learning Defense to Mitigate Threats in EEG Signal Classification
This paper presents a reputation-based threat mitigation framework that defends potential security threats in electroencephalogram (EEG) signal classification during model aggregation of Federated Learning. While EEG sig…
Brain Computer InterfaceData PoisoningEEGEEG Signal Classification+4EEG motor imagery decoding: A framework for comparative analysis with channel attention mechanisms
The objective of this study is to investigate the application of various channel attention mechanisms within the domain of brain-computer interface (BCI) for motor imagery decoding. Channel attention mechanisms can be se…
Brain Computer InterfaceEEGMotor ImageryAutomatic Control of Reactive Brain Computer Interfaces
This article discusses practical and theoretical aspects of real-time brain computer interface control methods based on Bayesian statistics. We investigate and improve the performance of automatic control and feedback al…
Brain Computer InterfaceTransfer LearningA Convolutional Network Adaptation for Cortical Classification During Mobile Brain Imaging
Deep neural networks (DNN) have become increasingly utilized in brain-computer interface (BCI) technologies with the outset goal of classifying human physiological signals in computer-readable format. While our present u…
Brain Computer InterfaceClassificationEEGERPHappyFeat -- An interactive and efficient BCI framework for clinical applications
Brain-Computer Interface (BCI) systems allow users to perform actions by translating their brain activity into commands. Such systems usually need a training phase, consisting in training a classification algorithm to di…
Brain Computer Interfacefeature selectionFunctional ConnectivityMotor ImageryIs controlling a brain-computer interface just a matter of presence of mind? The limits of cognitive-motor dissociation
Brain-computer interfaces (BCI) are presented as a solution for people with global paralysis, also known as locked-in syndrome (LIS). The targeted population includes the most severe patients, with no residual eye moveme…
Brain Computer InterfaceUnidirectional brain-computer interface: Artificial neural network encoding natural images to fMRI response in the visual cortex
While significant advancements in artificial intelligence (AI) have catalyzed progress across various domains, its full potential in understanding visual perception remains underexplored. We propose an artificial neural …
Brain Computer InterfaceA Dynamic Domain Adaptation Deep Learning Network for EEG-based Motor Imagery Classification
There is a correlation between adjacent channels of electroencephalogram (EEG), and how to represent this correlation is an issue that is currently being explored. In addition, due to inter-individual differences in EEG …
Brain Computer InterfaceDomain AdaptationEEGElectroencephalogram (EEG)+1Phase Synchrony Component Self-Organization in Brain Computer Interface
Phase synchrony information plays a crucial role in analyzing functional brain connectivity and identifying brain activities. A widely adopted feature extraction pipeline, composed of preprocessing, selection of EEG acqu…
Brain Computer Interfacechannel selectionEEGMotor ImageryBrief Architectural Survey of Biopotential Recording Front-Ends since the 1970s
Measuring the bioelectric signals is one of the key functions in wearable healthcare devices and implantable medical devices. The use of wearable healthcare devices has made continuous and immediate monitoring of persona…
Brain Computer Interface