10 citations · 15 across the 9 of their papers we have counts for
5 papers · 1 filter
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…
A DenseNet-based method for decoding auditory spatial attention with EEG
Xiran Xu, Bo Wang, Yujie Yan +2
Auditory spatial attention detection (ASAD) aims to decode the attended spatial location with EEG in a multiple-speaker setting. ASAD methods are inspired by the brain lateralizati…