[Dual-stream temporal-frequency convolutional network with additive attention for auditory attention decoding].

RAMamba-Net: A Reliability-Aware and Mamba-Based Multimodal Fusion Network for Auditory Attention Detection

RAMamba-Net is proposed, a reliability-aware Mamba-based multimodal fusion network for AAD that effectively exploits complementary EEG-EOG information, yielding accuracy gains over unimodal baselines, and is robust to signal perturbation and parameter variation.

Thai speech envelope auditory attention detection using eeg

Auditory attention detection (AAD) identifies which of several competing talkers a listener is attending to, a key step toward neuro-steered hearing devices for real-world listening environments with multiple speakers. Most AAD work to date has examined non-tonal languages, leaving tonal languages underexplored even th...

A Neural Network with Multi-Type Attention for Enhancing Motor Imagery EEG Decoding.

Despite the widespread adoption of deep learning techniques in motor imagery (MI) electroencephalogram (EEG) decoding, the limited decoding performance persists due to the low signal-to-noise ratio of EEG signals and insufficient exploration of MI-related information from temporal, frequency and spatial domains. Theref...

AFA-Net: A Differential Attention Approach for Auditory Attention Detection

AFA-Net is among the first frameworks to explicitly try to combat EEG noise to improve AAD, a machine learning framework that replaces vanilla attention with a simple yet flexible differential attention mechanism to help focus on task-relevant neural activity.

FBDA-TSRNet: A Filter Bank Dual-Path Attention and Temporal–Spatial Residual Network for Motor Imagery Decoding

Electroencephalography (EEG)-based motor imagery (MI) decoding is an important task in noninvasive brain-computer interfaces (BCIs). However, reliable MI-EEG decoding remains challenging because of low signal-to-noise ratios, inter-session and inter-subject variability, and multiscale spatiotemporal patterns. This stud...

A spatial-channel reconstruction guided frequency-time-space deep neural framework for SSVEP-based brain-computer interface EEG decoding