Papers Brain Computer Interface
“Brain Computer Interface” 태그가 달린 논문 466편 · 필터 해제
Low-cost brain computer interface for everyday use
With the growth in electroencephalography (EEG) based applications the demand for affordable consumer solutions is increasing. Here we describe a compact, low-cost EEG device suitable for daily use. The data are transfer…
Brain Computer InterfaceEEGElectroencephalogram (EEG)EEGDnet: Fusing Non-Local and Local Self-Similarity for 1-D EEG Signal Denoising with 2-D Transformer
Electroencephalogram (EEG) has shown a useful approach to produce a brain-computer interface (BCI). One-dimensional (1-D) EEG signal is yet easily disturbed by certain artifacts (a.k.a. noise) due to the high temporal re…
Brain Computer InterfaceDenoisingEEGElectroencephalogram (EEG)Online Optimization of Stimulation Speed in an Auditory Brain-Computer Interface under Time Constraints
The decoding of brain signals recorded via, e.g., an electroencephalogram, using machine learning is key to brain-computer interfaces (BCIs). Stimulation parameters or other experimental settings of the BCI protocol typi…
Bayesian OptimizationBrain Computer InterfaceEEG-based Classification of Drivers Attention using Convolutional Neural Network
Accurate detection of a drivers attention state can help develop assistive technologies that respond to unexpected hazards in real time and therefore improve road safety. This study compares the performance of several at…
Brain Computer InterfaceClassificationEEGElectroencephalogram (EEG)+2Generating Music and Generative Art from Brain activity
Nowadays, technological advances have influenced all human activities, creating new dynamics and ways of communication. In this context, some artists have incorporated these advances in their creative process, giving ris…
Brain Computer InterfaceA SPA-based Manifold Learning Framework for Motor Imagery EEG Data Classification
The electroencephalography (EEG) signal is a non-stationary, stochastic, and highly non-linear bioelectric signal for which achieving high classification accuracy is challenging, especially when the number of subjects is…
Brain Computer InterfaceEEGElectroencephalogram (EEG)Motor Imagery+1Voxel selection framework based on meta-heuristic search and mutual information for brain decoding
Visual stimulus decoding is an increasingly important challenge in neuroscience. The goal is to classify the activity patterns from the human brain; during the sighting of visual objects. One of the crucial problems in t…
Brain Computer InterfaceBrain DecodingDecoderHeuristic SearchClassification of Upper Arm Movements from EEG signals using Machine Learning with ICA Analysis
The Brain-Computer Interface system is a profoundly developing area of experimentation for Motor activities which plays vital role in decoding cognitive activities. Classification of Cognitive-Motor Imagery activities fr…
Brain Computer InterfaceEEGElectroencephalogram (EEG)Motor ImageryDAL: Feature Learning from Overt Speech to Decode Imagined Speech-based EEG Signals with Convolutional Autoencoder
Brain-computer interface (BCI) is one of the tools which enables the communication between humans and devices by reflecting intention and status of humans. With the development of artificial intelligence, the interest in…
Brain Computer InterfaceEEGElectroencephalogram (EEG)EEG-ConvTransformer for Single-Trial EEG based Visual Stimuli Classification
Different categories of visual stimuli activate different responses in the human brain. These signals can be captured with EEG for utilization in applications such as Brain-Computer Interface (BCI). However, accurate cla…
Brain Computer InterfaceClassificationDiversityEEG+1Complex common spatial patterns on time-frequency decomposed EEG for brain-computer interface
Motor imagery brain-computer interface (MI-BCI) has many promising applications but there are problems such as poor classification accuracy and robustness which need to be addressed. We propose a novel approach called ti…
Brain Computer InterfaceClassificationEEGEEG Signal Classification+2Partial Maximum Correntropy Regression for Robust Trajectory Decoding from Noisy Epidural Electrocorticographic Signals
The Partial Least Square Regression (PLSR) exhibits admirable competence for predicting continuous variables from inter-correlated brain recordings in the brain-computer interface. However, PLSR is in essence formulated …
Brain Computer InterfaceDimensionality ReductionregressionTransformer-based Spatial-Temporal Feature Learning for EEG Decoding
At present, people usually use some methods based on convolutional neural networks (CNNs) for Electroencephalograph (EEG) decoding. However, CNNs have limitations in perceiving global dependencies, which is not adequate …
Brain Computer InterfaceEEGEeg DecodingElectroencephalogram (EEG)Wheelchair automation by a hybrid BCI system using SSVEP and eye blinks
This work proposes a hybrid Brain Computer Interface system for the automation of a wheelchair for the disabled. Herein a working prototype of a BCI-based wheelchair is detailed that can navigate inside a typical home en…
Bayesian OptimizationBrain Computer InterfaceDenoisingEEG+3Subject-Independent Brain-Computer Interface for Decoding High-Level Visual Imagery Tasks
Brain-computer interface (BCI) is used for communication between humans and devices by recognizing status and intention of humans. Communication between humans and a drone using electroencephalogram (EEG) signals is one …
Brain Computer InterfaceEEGElectroencephalogram (EEG)Generating Ten BCI Commands Using Four Simple Motor Imageries
The brain computer interface (BCI) systems are utilized for transferring information among humans and computers by analyzing electroencephalogram (EEG) recordings.The process of mentally previewing a motor movement witho…
Brain Computer InterfaceEEGElectroencephalogram (EEG)Motor ImageryAn overview of deep learning techniques for epileptic seizures detection and prediction based on neuroimaging modalities: Methods, challenges, and future works
Epilepsy is a disorder of the brain denoted by frequent seizures. The symptoms of seizure include confusion, abnormal staring, and rapid, sudden, and uncontrollable hand movements. Epileptic seizure detection methods inv…
Brain Computer InterfaceCloud ComputingPredictionSeizure DetectionCNN-based Approaches For Cross-Subject Classification in Motor Imagery: From The State-of-The-Art to DynamicNet
Motor imagery (MI)-based brain-computer interface (BCI) systems are being increasingly employed to provide alternative means of communication and control for people suffering from neuro-motor impairments, with a special …
Brain Computer InterfaceDeep LearningEEGMotor Imagery+1LGGNet: Learning from Local-Global-Graph Representations for Brain-Computer Interface
Neuropsychological studies suggest that co-operative activities among different brain functional areas drive high-level cognitive processes. To learn the brain activities within and among different functional areas of th…
Brain Computer InterfaceEEGEEG Emotion RecognitionElectroencephalogram (EEG)+1A magnetoencephalography dataset for motor and cognitive imagery-based brain-computer interface
Recent advancements in magnetoencephalography (MEG)-based brain-computer interfaces (BCIs) have shown great potential. However, the performance of current MEG-BCI systems is still inadequate and one of the main reasons f…
Brain Computer InterfaceMotor Imagery