Papers Eeg Decoding
“Eeg Decoding” 태그가 달린 논문 130편 · 필터 해제
The Sandwich meta-framework for architecture agnostic deep privacy-preserving transfer learning for non-invasive brainwave decoding
Machine learning has enhanced the performance of decoding signals indicating human behaviour. EEG decoding, as an exemplar indicating neural activity and human thoughts non-invasively, has been helpful in neural activity…
EEGEeg DecodingFederated LearningMotor Imagery+2EEG decoding with conditional identification information
Decoding EEG signals is crucial for unraveling human brain and advancing brain-computer interfaces. Traditional machine learning algorithms have been hindered by the high noise levels and inherent inter-person variations…
EEGEeg DecodingGeometric Neural Network based on Phase Space for BCI-EEG decoding
Objective: The integration of Deep Learning (DL) algorithms on brain signal analysis is still in its nascent stages compared to their success in fields like Computer Vision. This is particularly true for BCI, where the b…
Brain Computer InterfaceEEGEeg DecodingFingerNet: 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+1Detecting gamma-band responses to the speech envelope for the ICASSP 2024 Auditory EEG Decoding Signal Processing Grand Challenge
The 2024 ICASSP Auditory EEG Signal Processing Grand Challenge concerns the decoding of electroencephalography (EEG) measurements taken from participants who listened to speech material. This work details our solution to…
DecoderEEGEeg DecodingA 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 LearningLatent Alignment with Deep Set EEG Decoders
The variability in EEG signals between different individuals poses a significant challenge when implementing brain-computer interfaces (BCI). Commonly proposed solutions to this problem include deep learning models, due …
Deep LearningDomain AdaptationEEGEeg Decoding+3Spatial Variation Generation Algorithm for Motor Imagery Data Augmentation: Increasing the Density of Sample Vicinity
The imbalanced development between deep learning-based model design and motor imagery (MI) data acquisition raises concerns about the potential overfitting issue—models can identify training data well but fail to general…
Data AugmentationEEGEeg DecodingEEG Left/Right hand+1Functional Graph Contrastive Learning of Hyperscanning EEG Reveals Emotional Contagion Evoked by Stereotype-Based Stressors
This study delves into the intricacies of emotional contagion and its impact on performance within dyadic interactions. Specifically, it focuses on the context of stereotype-based stress (SBS) during collaborative proble…
Contrastive LearningEEGEeg DecodingElectroencephalogram (EEG)+1Large Transformers are Better EEG Learners
Pre-trained large transformer models have achieved remarkable performance in the fields of natural language processing and computer vision. However, the limited availability of public electroencephalogram (EEG) data pres…
Activity RecognitionEEGEeg DecodingElectroencephalogram (EEG)+3Distributionally Robust Cross Subject EEG Decoding
Recently, deep learning has shown to be effective for Electroencephalography (EEG) decoding tasks. Yet, its performance can be negatively influenced by two key factors: 1) the high variance and different types of corrupt…
Data AugmentationDecoderEEGEeg DecodingAggregating Intrinsic Information to Enhance BCI Performance through Federated Learning
Insufficient data is a long-standing challenge for Brain-Computer Interface (BCI) to build a high-performance deep learning model. Though numerous research groups and institutes collect a multitude of EEG datasets for th…
Brain Computer InterfaceEEGEeg DecodingFederated Learning+2Evaluating the structure of cognitive tasks with transfer learning
Electroencephalography (EEG) decoding is a challenging task due to the limited availability of labelled data. While transfer learning is a promising technique to address this challenge, it assumes that transferable data …
EEGEeg DecodingERPTransfer LearningEEG Decoding for Datasets with Heterogenous Electrode Configurations using Transfer Learning Graph Neural Networks
Brain-Machine Interfacing (BMI) has greatly benefited from adopting machine learning methods for feature learning that require extensive data for training, which are often unavailable from a single dataset. Yet, it is di…
Brain Computer InterfaceDomain AdaptationEEGEeg Decoding+2Weight Freezing: A Regularization Approach for Fully Connected Layers with an Application in EEG Classification
In the realm of EEG decoding, enhancing the performance of artificial neural networks (ANNs) carries significant potential. This study introduces a novel approach, termed "weight freezing", that is anchored on the princi…
Decision MakingEEGEeg DecodingTime-space-frequency feature Fusion for 3-channel motor imagery classification
Low-channel EEG devices are crucial for portable and entertainment applications. However, the low spatial resolution of EEG presents challenges in decoding low-channel motor imagery. This study introduces TSFF-Net, a nov…
EEGEeg DecodingMotor ImageryTime SeriesRelating EEG recordings to speech using envelope tracking and the speech-FFR
During speech perception, a listener's electroencephalogram (EEG) reflects acoustic-level processing as well as higher-level cognitive factors such as speech comprehension and attention. However, decoding speech from EEG…
DecoderEEGEeg DecodingElectroencephalogram (EEG)Deep Riemannian Networks for End-to-End EEG Decoding
State-of-the-art performance in electroencephalography (EEG) decoding tasks is currently often achieved with either Deep-Learning (DL) or Riemannian-Geometry-based decoders (RBDs). Recently, there is growing interest in …
EEGEeg DecodingElectroencephalogram (EEG)Enhancing Low-Density EEG-Based Brain-Computer Interfaces with Similarity-Keeping Knowledge Distillation
Electroencephalogram (EEG) has been one of the common neuromonitoring modalities for real-world brain-computer interfaces (BCIs) because of its non-invasiveness, low cost, and high temporal resolution. Recently, light-we…
EEGEeg DecodingElectroencephalogram (EEG)Knowledge Distillation+1Federated deep transfer learning for EEG decoding using multiple BCI tasks
Deep learning has been successful in BCI decoding. However, it is very data-hungry and requires pooling data from multiple sources. EEG data from various sources decrease the decoding performance due to negative transfer…
DecoderEEGEeg DecodingElectroencephalogram (EEG)+2