Papers EEG Signal Classification
“EEG Signal Classification” 태그가 달린 논문 33편 · 필터 해제
ATCNet-CIAM for Multi-Session Motor Imagery EEG Signal Classification
Motor imagery (MI)-based electroencephalography is widely used in non-invasive brain--computer interfaces (BCIs), but robust decoding remains challenging due to inter-subject variability and cross-session non-stationarit…
EEG Signal ClassificationSleepExplain: Explainable Non-Rapid Eye Movement and Rapid Eye Movement Sleep Stage Classification from EEG Signal
Classification of sleep stages is one of the most important diagnostic approaches for a variety of sleep-related disorders. Electroencephalography (EEG) is regarded as a powerful tool for examining the association betwee…
EEG Signal ClassificationDeep Convolutional Architectures for EEG Classification: A Comparative Study with Temporal Augmentation and Confidence-Based Voting
Electroencephalography (EEG) classification plays a key role in brain-computer interface (BCI) systems, yet it remains challenging due to the low signal-to-noise ratio, temporal variability of neural responses, and limit…
EEG Signal ClassificationA Unified Framework for EEG Seizure Detection Using Universum-Integrated Generalized Eigenvalues Proximal Support Vector Machine
The paper presents novel Universum-enhanced classifiers: the Universum Generalized Eigenvalue Proximal Support Vector Machine (U-GEPSVM) and the Improved U-GEPSVM (IU-GEPSVM) for EEG signal classification. Using the comp…
EEG Signal ClassificationComputational EfficiencyBinary ClassificationSeizure DetectionCoSupFormer : A Contrastive Supervised learning approach for EEG signal Classification
Electroencephalography signals (EEGs) contain rich multi-scale information crucial for understanding brain states, with potential applications in diagnosing and advancing the drug development landscape. However, extracti…
EEG Signal ClassificationContrastive LearningMotor Imagery EEG Signal Classification Using Minimally Random Convolutional Kernel Transform and Hybrid Deep Learning
The brain-computer interface (BCI) establishes a non-muscle channel that enables direct communication between the human body and an external device. Electroencephalography (EEG) is a popular non-invasive technique for re…
EEG Signal ClassificationActivity RecognitionEAD: An EEG Adapter for Automated Classification
While electroencephalography (EEG) has been a popular modality for neural decoding, it often involves task specific acquisition of the EEG data. This poses challenges for the development of a unified pipeline to learn em…
ClassificationEEGEEG Signal ClassificationElectrophysiological Investigation of Insect Pain Threshold
The question of whether insects experience pain has long been debated in neuroscience and animal behavior research. Increasing evidence suggests that insects possess the ability to detect and respond to noxious stimuli, …
EEGEEG Signal ClassificationEEG-Based Mental Imagery Task Adaptation via Ensemble of Weight-Decomposed Low-Rank Adapters
Electroencephalography (EEG) is widely researched for neural decoding in Brain Computer Interfaces (BCIs) as it is non-invasive, portable, and economical. However, EEG signals suffer from inter- and intra-subject variabi…
Domain AdaptationEEGEEG Signal ClassificationMotor Imagery+2Can EEG resting state data benefit data-driven approaches for motor-imagery decoding?
Resting-state EEG data in neuroscience research serve as reliable markers for user identification and reveal individual-specific traits. Despite this, the use of resting-state data in EEG classification models is limited…
EEGEEG Signal ClassificationFunctional ConnectivityMotor Imagery+1NeuroAssist: Enhancing Cognitive-Computer Synergy with Adaptive AI and Advanced Neural Decoding for Efficient EEG Signal Classification
Traditional methods of controlling prosthetics frequently encounter difficulties regarding flexibility and responsiveness, which can substantially impact people with varying cognitive and physical abilities. Advancements…
Brain Computer InterfaceEEGEEG Signal ClassificationMotor ImageryOptimizing Brain-Computer Interface Performance: Advancing EEG Signals Channel Selection through Regularized CSP and SPEA II Multi-Objective Optimization
Brain-computer interface systems and the recording of brain activity has garnered significant attention across a diverse spectrum of applications. EEG signals have emerged as a modality for recording neural electrical ac…
Brain Computer Interfacechannel selectionEEGEEG Signal Classification+2EEG2Rep: Enhancing Self-supervised EEG Representation Through Informative Masked Inputs
Self-supervised approaches for electroencephalography (EEG) representation learning face three specific challenges inherent to EEG data: (1) The low signal-to-noise ratio which challenges the quality of the representatio…
EEGEEG Signal ClassificationRepresentation LearningImproving 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 NetworkImproved Motor Imagery Classification Using Adaptive Spatial Filters Based on Particle Swarm Optimization Algorithm
As a typical self-paced brain-computer interface (BCI) system, the motor imagery (MI) BCI has been widely applied in fields such as robot control, stroke rehabilitation, and assistance for patients with stroke or spinal …
Brain Computer InterfaceClassificationEEGEEG Signal Classification+2Reputation-Based Federated Learning Defense to Mitigate Threats in EEG Signal Classification
This paper presents a reputation-based threat mitigation framework that defends potential security threats in electroencephalogram (EEG) signal classification during model aggregation of Federated Learning. While EEG sig…
Brain Computer InterfaceData PoisoningEEGEEG Signal Classification+4Deep comparisons of Neural Networks from the EEGNet family
Most of the Brain-Computer Interface (BCI) publications, which propose artificial neural networks for Motor Imagery (MI) Electroencephalography (EEG) signal classification, are presented using one of the BCI Competition …
Brain Computer InterfaceEEGEEG Signal ClassificationElectroencephalogram (EEG)+2A 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+2A Hybrid Complex-valued Neural Network Framework with Applications to Electroencephalogram (EEG)
In this article, we present a new EEG signal classification framework by integrating the complex-valued and real-valued Convolutional Neural Network(CNN) with discrete Fourier transform (DFT). The proposed neural network…
EEGEEG Signal ClassificationElectroencephalogram (EEG)A Neural-Inspired Architecture for EEG-Based Auditory Attention Detection
Humans have the ability to focus on one of the sound sources in a noisy scene, which is critical for everyday communication. Auditory attention detection (AAD) seeks to detect selective attention from one’s brain signals…
Brain Computer InterfaceData VisualizationEEGEEG Signal Classification+1