Papers Brain Computer Interface
“Brain Computer Interface” 태그가 달린 논문 466편 · 필터 해제
Hybrid Paradigm-based Brain-Computer Interface for Robotic Arm Control
Brain-computer interface (BCI) uses brain signals to communicate with external devices without actual control. Particularly, BCI is one of the interfaces for controlling the robotic arm. In this study, we propose a knowl…
Brain Computer InterfaceEEGElectroencephalogram (EEG)Knowledge DistillationCalibration-Free Driver Drowsiness Classification based on Manifold-Level Augmentation
Drowsiness reduces concentration and increases response time, which causes fatal road accidents. Monitoring drivers' drowsiness levels by electroencephalogram (EEG) and taking action may prevent road accidents. EEG signa…
Brain Computer InterfaceData AugmentationDiversityDomain Generalization+2CropCat: Data Augmentation for Smoothing the Feature Distribution of EEG Signals
Brain-computer interface (BCI) is a communication system between humans and computers reflecting human intention without using a physical control device. Since deep learning is robust in extracting features from data, re…
Brain Computer InterfaceData AugmentationDeep LearningEEG+2Classification of Distraction Levels Using Hybrid Deep Neural Networks From EEG Signals
Non-invasive brain-computer interface technology has been developed for detecting human mental states with high performances. Detection of the pilots' mental states is particularly critical because their abnormal mental …
Autonomous DrivingBrain Computer InterfaceEEGElectroencephalogram (EEG)A Hybrid Brain-Computer Interface Using Motor Imagery and SSVEP Based on Convolutional Neural Network
The key to electroencephalography (EEG)-based brain-computer interface (BCI) lies in neural decoding, and its accuracy can be improved by using hybrid BCI paradigms, that is, fusing multiple paradigms. However, hybrid BC…
Brain Computer InterfaceEEGElectroencephalogram (EEG)Motor Imagery+1EEG Opto-processor: epileptic seizure detection using diffractive photonic computing units
Electroencephalography (EEG) analysis extracts critical information from brain signals, which has provided fundamental support for various applications, including brain-disease diagnosis and brain-computer interface. How…
Brain Computer Interfacechannel selectionEdge-computingEEG+2Closed loop BCI System for Cybathlon 2020
We present our Brain-Computer Interface (BCI) System, developed for the BCI discipline of Cybathlon 2020 competition. In the BCI discipline, subjects with tetraplegia are required to control a computer game with mental c…
Brain Computer InterfaceEEGElectroencephalogram (EEG)EEG-NeXt: A Modernized ConvNet for The Classification of Cognitive Activity from EEG
One of the main challenges in electroencephalogram (EEG) based brain-computer interface (BCI) systems is learning the subject/session invariant features to classify cognitive activities within an end-to-end discriminativ…
Brain Computer InterfaceEEGElectroencephalogram (EEG)image-classification+2Target-centered Subject Transfer Framework for EEG Data Augmentation
Data augmentation approaches are widely explored for the enhancement of decoding electroencephalogram signals. In subject-independent brain-computer interface system, domain adaption and generalization are utilized to sh…
Brain Computer InterfaceData AugmentationDomain AdaptationEEG+2Brain Computer Interface: Deep Learning Approach to Predict Human Emotion Recognition
Brain-Computer Interfaces allow controlling machines through signals coming from Electroencephalography (EEG) analysis. Nowadays, there are several cheap electroencephalographs available on the market that guarantee good…
Brain Computer InterfaceEEGEmotion RecognitionBrain informed transfer learning for categorizing construction hazards
A transfer learning paradigm is proposed for "knowledge" transfer between the human brain and convolutional neural network (CNN) for a construction hazard categorization task. Participants' brain activities are recorded …
Brain Computer InterfaceEEGElectroencephalogram (EEG)Transfer LearningMotor imagery classification using EEG spectrograms
The loss of limb motion arising from damage to the spinal cord is a disability that could effect people while performing their day-to-day activities. The restoration of limb movement would enable people with spinal cord …
Brain Computer InterfaceClassificationEEGElectroencephalogram (EEG)+3Seeing Beyond the Brain: Conditional Diffusion Model with Sparse Masked Modeling for Vision Decoding
Decoding visual stimuli from brain recordings aims to deepen our understanding of the human visual system and build a solid foundation for bridging human and computer vision through the Brain-Computer Interface. However,…
Brain Computer InterfaceTowards Fast Single-Trial Online ERP based Brain-Computer Interface using dry EEG electrodes and neural networks: a pilot study
Speeding up the spelling in event-related potentials (ERP) based Brain-Computer Interfaces (BCI) requires eliciting strong brain responses in a short span of time, as much as the accurate classification of such evoked po…
Brain Computer InterfaceEEGElectroencephalogram (EEG)ERPEmbedding neurophysiological signals
Neurophysiological time-series recordings of brain activity like the electroencephalogram (EEG) or local field potentials can be decoded by machine learning models in order to either control an application, e.g., for com…
Brain Computer InterfaceDomain AdaptationEEGEeg Decoding+2Stimulus-Informed Generalized Canonical Correlation Analysis of Stimulus-Following Brain Responses
In brain-computer interface or neuroscience applications, generalized canonical correlation analysis (GCCA) is often used to extract correlated signal components in the neural activity of different subjects attending to …
Brain Computer InterfaceFingerFlex: Inferring Finger Trajectories from ECoG signals
Motor brain-computer interface (BCI) development relies critically on neural time series decoding algorithms. Recent advances in deep learning architectures allow for automatic feature selection to approximate higher-ord…
Brain Computer InterfaceBrain DecodingDecoderfeature selection+3Toward the application of XAI methods in EEG-based systems
An interesting case of the well-known Dataset Shift Problem is the classification of Electroencephalogram (EEG) signals in the context of Brain-Computer Interface (BCI). The non-stationarity of EEG signals can lead to po…
Brain Computer InterfaceClassificationEEGElectroencephalogram (EEG)+3A Transformer-based deep neural network model for SSVEP classification
Steady-state visual evoked potential (SSVEP) is one of the most commonly used control signal in the brain-computer interface (BCI) systems. However, the conventional spatial filtering methods for SSVEP classification hig…
Brain Computer InterfaceClassificationEEGEEG Signal Classification+2Deep learning for ECoG brain-computer interface: end-to-end vs. hand-crafted features
In brain signal processing, deep learning (DL) models have become commonly used. However, the performance gain from using end-to-end DL models compared to conventional ML approaches is usually significant but moderate, t…
Brain Computer InterfaceMotor Imagery