Search Results for author:
Found 5 papers, 2 papers with code
Using Ear-EEG to Decode Auditory Attention in Multiple-speaker Environment
One kind of auditory spatial attention detection (ASAD) method, STAnet, was testified with this ear-EEG database, resulting in 93. 1% in 1-second decoding window.
Beware of Overestimated Decoding Performance Arising from Temporal Autocorrelations in Electroencephalogram Signals
Since these EEG data were usually collected with well-designed paradigms in labs, the reliability and robustness of the corresponding decoding methods were doubted by some researchers, and they argued that such decoding accuracy was overestimated due to the inherent temporal autocorrelation of EEG signals.
ConvConcatNet: a deep convolutional neural network to reconstruct mel spectrogram from the EEG
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.
Self-supervised speech representation and contextual text embedding for match-mismatch classification with EEG recording
Contrastive learning was used to relate EEG features to speech features.
A DenseNet-based method for decoding auditory spatial attention with EEG
ASAD methods are inspired by the brain lateralization of cortical neural responses during the processing of auditory spatial attention, and show promising performance for the task of auditory attention decoding (AAD) with neural recordings.
