Electroencephalogram (EEG)
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Benchmarks
Most implemented
SGDR: Stochastic Gradient Descent with Warm Restarts
EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces
Learning Representations from EEG with Deep Recurrent-Convolutional Neural Networks
DeepSleepNet: a Model for Automatic Sleep Stage Scoring based on Raw Single-Channel EEG
Papers
NeuroXAI: Adaptive, robust, explainable surrogate framework for determination of channel importance in EEG application
Electroencephalogram (EEG)-based applications often require numerous channels to achieve high performance, which limits their widespread use. Various channel selection methods have been proposed to identify minimum EEG c…
channel selectionEEGElectroencephalogram (EEG)Explainable Artificial Intelligence (XAI)+1AFPM: Alignment-based Frame Patch Modeling for Cross-Dataset EEG Decoding
Electroencephalogram (EEG) decoding models for brain-computer interfaces (BCIs) struggle with cross-dataset learning and generalization due to channel layout inconsistencies, non-stationary signal distributions, and limi…
EEGEeg DecodingElectroencephalogram (EEG)Motor ImageryEEG Foundation Challenge: From Cross-Task to Cross-Subject EEG Decoding
Current electroencephalogram (EEG) decoding models are typically trained on small numbers of subjects performing a single task. Here, we introduce a large-scale, code-submission-based competition comprising two challenge…
EEGEeg DecodingElectroencephalogram (EEG)Brain2Vec: A Deep Learning Framework for EEG-Based Stress Detection Using CNN-LSTM-Attention
Mental stress has become a pervasive factor affecting cognitive health and overall well-being, necessitating the development of robust, non-invasive diagnostic tools. Electroencephalogram (EEG) signals provide a direct w…
DiagnosticEEGElectroencephalogram (EEG)Dataset combining EEG, eye-tracking, and high-speed video for ocular activity analysis across BCI paradigms
In Brain-Computer Interface (BCI) research, the detailed study of blinks is crucial. They can be considered as noise, affecting the efficiency and accuracy of decoding users' cognitive states and intentions, or as potent…
Brain Computer InterfaceEEGElectroencephalogram (EEG)Motor ImageryChannel-Imposed Fusion: A Simple yet Effective Method for Medical Time Series Classification
The automatic classification of medical time series signals, such as electroencephalogram (EEG) and electrocardiogram (ECG), plays a pivotal role in clinical decision support and early detection of diseases. Although Tra…
ClassificationEEGElectroencephalogram (EEG)Time Series+1