Papers Brain Computer Interface
“Brain Computer Interface” 태그가 달린 논문 466편 · 필터 해제
Spatiotemporal Pooling on Appropriate Topological Maps Represented as Two-Dimensional Images for EEG Classification
Motor imagery classification based on electroencephalography (EEG) signals is one of the most important brain-computer interface applications, although it needs further improvement. Several methods have attempted to obta…
Brain Computer InterfaceClassificationEEGMotor ImageryPhysics-informed and Unsupervised Riemannian Domain Adaptation for Machine Learning on Heterogeneous EEG Datasets
Combining electroencephalogram (EEG) datasets for supervised machine learning (ML) is challenging due to session, subject, and device variability. ML algorithms typically require identical features at train and test time…
Brain Computer Interfacechannel selectionDomain AdaptationEEG+4FingerNet: EEG Decoding of A Fine Motor Imagery with Finger-tapping Task Based on A Deep Neural Network
Brain-computer interface (BCI) technology facilitates communication between the human brain and computers, primarily utilizing electroencephalography (EEG) signals to discern human intentions. Although EEG-based BCI syst…
Brain Computer InterfaceEEGEeg DecodingMotor Imagery+1NeuSpeech: Decode Neural signal as Speech
Decoding language from brain dynamics is an important open direction in the realm of brain-computer interface (BCI), especially considering the rapid growth of large language models. Compared to invasive-based signals wh…
Brain Computer InterfaceEEGStimulation technology for brain and nerves, now and future
In individuals afflicted with conditions such as paralysis, the implementation of Brain-Computer-Interface (BCI) has begun to significantly impact their quality of life. Furthermore, even in healthy individuals, the anti…
Brain Computer InterfaceTowards Decoding Brain Activity During Passive Listening of Speech
The aim of the study is to investigate the complex mechanisms of speech perception and ultimately decode the electrical changes in the brain accruing while listening to speech. We attempt to decode heard speech from intr…
Brain Computer InterfaceSpeech SynthesisArEEG_Chars: Dataset for Envisioned Speech Recognition using EEG for Arabic Characters
Brain-computer interfaces is an important and hot research topic that revolutionize how people interact with the world, especially for individuals with neurological disorders. While extensive research has been done in EE…
Brain Computer InterfaceEEGspeech-recognitionSpeech RecognitionEarly feasibility of an embedded bi-directional brain-computer interface for ambulation
Current treatments for paraplegia induced by spinal cord injury (SCI) are often limited by the severity of the injury. The accompanying loss of sensory and motor functions often results in reliance on wheelchairs, which …
Brain Computer InterfaceClassification of Emerging Neural Activity from Planning to Grasp Execution using a Novel EEG-Based BCI Platform
There have been different reports of developing Brain-Computer Interface (BCI) platforms to investigate the noninvasive electroencephalography (EEG) signals associated with plan-to-grasp tasks in humans. However, these r…
Brain Computer InterfaceEEGImproving EEG Signal Classification Accuracy Using Wasserstein Generative Adversarial Networks
Electroencephalography (EEG) plays a vital role in recording brain activities and is integral to the development of brain-computer interface (BCI) technologies. However, the limited availability and high variability of E…
Brain Computer InterfaceEEGEEG Signal ClassificationGenerative Adversarial NetworkStimulus-Informed Generalized Canonical Correlation Analysis for Group Analysis of Neural Responses to Natural Stimuli
Various new brain-computer interface technologies or neuroscience applications require decoding stimulus-following neural responses to natural stimuli such as speech and video from, e.g., electroencephalography (EEG) sig…
Brain Computer InterfaceEEGWavelet Analysis of Noninvasive EEG Signals Discriminates Complex and Natural Grasp Types
This research aims to decode hand grasps from Electroencephalograms (EEGs) for dexterous neuroprosthetic development and Brain-Computer Interface (BCI) applications, especially for patients with motor disorders. Particul…
Brain Computer InterfaceEEGFeature ImportanceSubject-Independent Deep Architecture for EEG-based Motor Imagery Classification
Motor imagery (MI) classification based on electroencephalogram (EEG) is a widely-used technique in non-invasive brain-computer interface (BCI) systems. Since EEG recordings suffer from heterogeneity across subjects and …
Brain Computer InterfaceEEGElectroencephalogram (EEG)Motor ImageryA Systematic Evaluation of Euclidean Alignment with Deep Learning for EEG Decoding
Electroencephalography (EEG) signals are frequently used for various Brain-Computer Interface (BCI) tasks. While Deep Learning (DL) techniques have shown promising results, they are hindered by the substantial data requi…
Brain Computer InterfaceEEGEeg DecodingTransfer LearningUsing i-vectors for subject-independent cross-session EEG transfer learning
Cognitive load classification is the task of automatically determining an individual's utilization of working memory resources during performance of a task based on physiologic measures such as electroencephalography (EE…
Brain Computer InterfaceEEGTransfer LearningA Temporal-Spectral Fusion Transformer with Subject-Specific Adapter for Enhancing RSVP-BCI Decoding
The Rapid Serial Visual Presentation (RSVP)-based Brain-Computer Interface (BCI) is an efficient technology for target retrieval using electroencephalography (EEG) signals. The performance improvement of traditional deco…
Brain Computer InterfaceEEGBrain-Conditional Multimodal Synthesis: A Survey and Taxonomy
In the era of Artificial Intelligence Generated Content (AIGC), conditional multimodal synthesis technologies (e.g., text-to-image, text-to-video, text-to-audio, etc) are gradually reshaping the natural content in the re…
Brain Computer InterfaceBrain DecodingSurveyMultiagent Copilot Approach for Shared Autonomy between Human EEG and TD3 Deep Reinforcement Learning
Deep reinforcement learning (RL) algorithms enable the development of fully autonomous agents that can interact with the environment. Brain-computer interface (BCI) systems decipher human implicit brain signals regardles…
Brain Computer InterfaceDeep Reinforcement LearningEEGReinforcement Learning (RL)3D-CLMI: A Motor Imagery EEG Classification Model via Fusion of 3D-CNN and LSTM with Attention
Due to the limitations in the accuracy and robustness of current electroencephalogram (EEG) classification algorithms, applying motor imagery (MI) for practical Brain-Computer Interface (BCI) applications remains challen…
Autonomous VehiclesBrain Computer InterfaceClassificationEEG+2Brain Computer Interface Technology for Future Battlefield
With the development of artificial intelligence and unmanned equipment, human-machine hybrid formations will be the main focus in future combat formations. With the development of big data and various situational awarene…
Brain Computer InterfaceDecision Making