10 citations · 14 across the 8 of their papers we have counts for
8 papers
AESSI: An Around-Ear Silent Speech Interface for Cross-Day Online Reuse without Test-Day Calibration
Xiran Xu, Mochu Dong, Yujie Yan +3
Silent speech interfaces (SSIs) enable private communication without audible speech and may support people with post-stroke dysarthria. Everyday reuse requires articulation-related…
Cross-Subject Generalization in Decoding Perceived Speech from Non-Invasive Brain Recordings
Aoke Zhang, Bo Wang, Xihong Wu +2
Decoding perceived speech from non-invasive brain recordings has garnered significant attention in recent years due to its wide range of potential applications. However, existing m…
Beyond Parallel Tracking: Interactive Multi-Feature Fusion Drives Semantic Reconstruction from Non-invasive Brain Recordings
Boda Xiao, Xiran Xu, Songyi Li +4
Continuous semantic reconstruction from non-invasive neural recordings remains limited by the representational mismatch between semantic feature spaces and neural coding patterns,…
SHINE: Sequential Hierarchical Integration Network for EEG and MEG
Xiran Xu, Yujie Yan, Songyi Li +5
How natural speech is represented in the brain constitutes a major challenge for cognitive neuroscience. Reconstructing the speech envelope and Mel spectrogram from EEG and MEG pro…
Using Ear-EEG to Decode Auditory Attention in Multiple-speaker Environment
Haolin Zhu, Yujie Yan, Xiran Xu +4
Auditory Attention Decoding (AAD) can help to determine the identity of the attended speaker during an auditory selective attention task, by analyzing and processing measurements o…
Beware of Overestimated Decoding Performance Arising from Temporal Autocorrelations in Electroencephalogram Signals
Xiran Xu, Bo Wang, Boda Xiao +4
Researchers have reported high decoding accuracy (>95%) using non-invasive Electroencephalogram (EEG) signals for brain-computer interface (BCI) decoding tasks like image decoding,…
Self-supervised speech representation and contextual text embedding for match-mismatch classification with EEG recording
Bo Wang, Xiran Xu, Zechen Zhang +4
Relating speech to EEG holds considerable importance but is challenging. In this study, a deep convolutional network was employed to extract spatiotemporal features from EEG data.…
ConvConcatNet: a deep convolutional neural network to reconstruct mel spectrogram from the EEG
Xiran Xu, Bo Wang, Yujie Yan +4
To investigate the processing of speech in the brain, simple linear models are commonly used to establish a relationship between brain signals and speech features. However, these l…