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Found 9 papers, 2 papers with code
Do self-supervised speech and language models extract similar representations as human brain?
Speech and language models trained through self-supervised learning (SSL) demonstrate strong alignment with brain activity during speech and language perception.
Neural Latent Aligner: Cross-trial Alignment for Learning Representations of Complex, Naturalistic Neural Data
The proposed framework learns more cross-trial consistent representations than the baselines, and when visualized, the manifold reveals shared neural trajectories across trials.
Evidence of state-dependence in the effectiveness of responsive neurostimulation for seizure modulation
State-dependent associations of RNS System stimulation parameters with changes in risk were estimated.
On Neural Phone Recognition of Mixed-Source ECoG Signals
The emerging field of neural speech recognition (NSR) using electrocorticography has recently attracted remarkable research interest for studying how human brains recognize speech in quiet and noisy surroundings.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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Brain2Char: A Deep Architecture for Decoding Text from Brain Recordings
These results set a new state-of-the-art on decoding text from brain and demonstrate the potential of Brain2Char as a high-performance communication BCI.
Spiking Linear Dynamical Systems on Neuromorphic Hardware for Low-Power Brain-Machine Interfaces
Neuromorphic architectures achieve low-power operation by using many simple spiking neurons in lieu of traditional hardware.
Deep learning as a tool for neural data analysis: speech classification and cross-frequency coupling in human sensorimotor cortex
A fundamental challenge in neuroscience is to understand what structure in the world is represented in spatially distributed patterns of neural activity from multiple single-trial measurements.
Union of Intersections (UoI) for Interpretable Data Driven Discovery and Prediction
The increasing size and complexity of scientific data could dramatically enhance discovery and prediction for basic scientific applications.
Modeling neural activity at the ensemble level
Here we demonstrate that the activity of neural ensembles can be quantitatively modeled.
Neurons and Cognition
