Papers Eeg Decoding
“Eeg Decoding” 태그가 달린 논문 130편 · 필터 해제
Embedding 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+2fMRI from EEG is only Deep Learning away: the use of interpretable DL to unravel EEG-fMRI relationships
The access to activity of subcortical structures offers unique opportunity for building intention dependent brain-computer interfaces, renders abundant options for exploring a broad range of cognitive phenomena in the re…
Decision MakingEEGEeg DecodingElectroencephalogram (EEG)CLEEGN: A Convolutional Neural Network for Plug-and-Play Automatic EEG Reconstruction
Human electroencephalography (EEG) is a brain monitoring modality that senses cortical neuroelectrophysiological activity in high-temporal resolution. One of the greatest challenges posed in applications of EEG is the un…
EEGEEG Artifact RemovalEeg DecodingElectroencephalogram (EEG)MAtt: A Manifold Attention Network for EEG Decoding
Recognition of electroencephalographic (EEG) signals highly affect the efficiency of non-invasive brain-computer interfaces (BCIs). While recent advances of deep-learning (DL)-based EEG decoders offer improved performanc…
EEGEeg DecodingElectroencephalogram (EEG)Partial Least Square Regression via Three-factor SVD-type Manifold Optimization for EEG Decoding
Partial least square regression (PLSR) is a widely-used statistical model to reveal the linear relationships of latent factors that comes from the independent variables and dependent variables. However, traditional metho…
EEGEeg DecodingElectroencephalogram (EEG)Motor Imagery+2Physics-inform attention temporal convolutional network for EEG-based motor imagery classification
The brain-computer interface (BCI) is a cutting-edge technology that has the potential to change the world. Electroencephalogram (EEG) motor imagery (MI) signal has been used extensively in many BCI applications to assis…
Brain Computer InterfaceEEGEEG 4 classesEeg Decoding+2A Decomposition-Based Hybrid Ensemble CNN Framework for Driver Fatigue Recognition
Electroencephalogram (EEG) has become increasingly popular in driver fatigue monitoring systems. Several decomposition methods have been attempted to analyze the EEG signals that are complex, nonlinear and non-stationary…
EEGEeg DecodingElectroencephalogram (EEG)Ensemble LearningTeam Cogitat at NeurIPS 2021: Benchmarks for EEG Transfer Learning Competition
Building subject-independent deep learning models for EEG decoding faces the challenge of strong covariate-shift across different datasets, subjects and recording sessions. Our approach to address this difficulty is to e…
EEGEeg DecodingElectroencephalogram (EEG)Motor Imagery+1ExBrainable: An Open-Source GUI for CNN-based EEG Decoding and Model Interpretation
We have developed a graphic user interface (GUI), ExBrainable, dedicated to convolutional neural networks (CNN) model training and visualization in electroencephalography (EEG) decoding. Available functions include model…
EEGEeg DecodingElectroencephalogram (EEG)Motor ImageryUncertainty Detection and Reduction in Neural Decoding of EEG Signals
EEG decoding systems based on deep neural networks have been widely used in decision making of brain computer interfaces (BCI). Their predictions, however, can be unreliable given the significant variance and noise in EE…
Data AugmentationDecision MakingEEGEeg Decoding+2Toward Open-World Electroencephalogram Decoding Via Deep Learning: A Comprehensive Survey
Electroencephalogram (EEG) decoding aims to identify the perceptual, semantic, and cognitive content of neural processing based on non-invasively measured brain activity. Traditional EEG decoding methods have achieved mo…
EEGEeg DecodingElectroencephalogram (EEG)EEGminer: Discovering Interpretable Features of Brain Activity with Learnable Filters
Patterns of brain activity are associated with different brain processes and can be used to identify different brain states and make behavioral predictions. However, the relevant features are not readily apparent and acc…
EEGEeg DecodingElectroencephalogram (EEG)Emotion Recognition+1Learning Signal Representations for EEG Cross-Subject Channel Selection and Trial Classification
EEG technology finds applications in several domains. Currently, most EEG systems require subjects to wear several electrodes on the scalp to be effective. However, several channels might include noisy information, redun…
channel selectionEEGEeg DecodingElectroencephalogram (EEG)Transformer-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)Deep Correlation Analysis for Audio-EEG Decoding
The electroencephalography (EEG), which is one of the easiest modes of recording brain activations in a non-invasive manner, is often distorted due to recording artifacts which adversely impacts the stimulus-response ana…
EEGEeg DecodingElectroencephalogram (EEG)Inter-subject Deep Transfer Learning for Motor Imagery EEG Decoding
Convolutional neural networks (CNNs) have become a powerful technique to decode EEG and have become the benchmark for motor imagery EEG Brain-Computer-Interface (BCI) decoding. However, it is still challenging to train C…
Brain Computer InterfaceDecoderEEGEeg Decoding+3Improving EEG Decoding via Clustering-based Multi-task Feature Learning
Accurate electroencephalogram (EEG) pattern decoding for specific mental tasks is one of the key steps for the development of brain-computer interface (BCI), which is quite challenging due to the considerably low signal-…
Brain Computer InterfaceClusteringEEGEeg Decoding+2Anchored-STFT and GNAA: An extension of STFT in conjunction with an adversarial data augmentation technique for the decoding of neural signals
Brain-computer interfaces (BCIs) enable communication between humans and machines by translating brain activity into control commands. Electroencephalography (EEG) signals are one of the most used brain signals in non-in…
ClassificationData AugmentationEEGEeg Decoding+1The "Sound of Silence" in EEG -- Cognitive voice activity detection
Speech cognition bears potential application as a brain computer interface that can improve the quality of life for the otherwise communication impaired people. While speech and resting state EEG are popularly studied, h…
Action DetectionActivity DetectionBrain Computer InterfaceEEG+4Decoding kinetic features of hand motor preparation from single‐trial EEG using convolutional neural networks
Building accurate movement decoding models from brain signals is crucial for many biomedical applications. Predicting specific movement features, such as speed and force, before movement execution may provide additional …
ClassificationEEGEeg DecodingElectroencephalogram (EEG)+4