Dewave: Discrete encoding of eeg waves for eeg to text translation
The translation of brain dynamics into natural language is pivotal for brain-computer
interfaces (BCIs), a field that has seen substantial growth in recent years. With the swift …
interfaces (BCIs), a field that has seen substantial growth in recent years. With the swift …
Dewave: Discrete eeg waves encoding for brain dynamics to text translation
The translation of brain dynamics into natural language is pivotal for brain-computer
interfaces (BCIs), a field that has seen substantial growth in recent years. With the swift …
interfaces (BCIs), a field that has seen substantial growth in recent years. With the swift …
Belt: Bootstrapping electroencephalography-to-language decoding and zero-shot sentiment classification by natural language supervision
This paper presents BELT, a novel model and learning framework for the pivotal topic of
brain-to-language translation research. The translation from noninvasive brain signals into …
brain-to-language translation research. The translation from noninvasive brain signals into …
Multi-band CNN with band-dependent kernels and amalgamated cross entropy loss for motor imagery classification
J Shin, W Chung - IEEE journal of biomedical and health …, 2023 - ieeexplore.ieee.org
In this paper, we present a novel MI classification method based on multi-band convolutional
neural network (CNN) with band-dependent kernel sizes, named MBK-CNN, to improve …
neural network (CNN) with band-dependent kernel sizes, named MBK-CNN, to improve …
Domain-specific denoising diffusion probabilistic models for brain dynamics
The differences in brain dynamics across human subjects, commonly referred to as human
artifacts, have long been a challenge in the field, severely limiting the generalizability of …
artifacts, have long been a challenge in the field, severely limiting the generalizability of …
Attention-Based Multiscale Spatial-Temporal Convolutional Network for Motor Imagery EEG Decoding
Y Zhang, P Li, L Cheng, M Li… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Motor imagery (MI) electroencephalography (EEG) has been used in consumer products
supported by brain-computer interfaces (BCI), with existing electronics covering a wide …
supported by brain-computer interfaces (BCI), with existing electronics covering a wide …
Temporal-Spatial Conversion Based Sequential Convolutional LSTM Architecture for Detecting Drug Addiction
H Ma, J Yao, J Huang, W Zhang… - IEEE Signal Processing …, 2024 - ieeexplore.ieee.org
Drug addiction (DA) is a long-term and relapsing brain disorder with limited effective
treatments. Electroencephalography (EEG) is a highly promising tool for investigating DA …
treatments. Electroencephalography (EEG) is a highly promising tool for investigating DA …
End-to-end translation of human neural activity to speech with a dual–dual generative adversarial network
In a recent study of auditory evoked potential (AEP) based brain–computer interface (BCI), it
was shown that, with an encoder–decoder framework, it is possible to translate human …
was shown that, with an encoder–decoder framework, it is possible to translate human …
EEG-Based TNN for Driver Vigilance Monitoring
J Sia, YC Chang, CT Lin… - 2023 IEEE Symposium …, 2023 - ieeexplore.ieee.org
Transformer neural network (TNN) has demonstrated its remarkable capacity to analyze and
discern complex sequential datasets. This approach has achieved unprecedented success …
discern complex sequential datasets. This approach has achieved unprecedented success …
BELT-2: Bootstrapping EEG-to-Language representation alignment for multi-task brain decoding
The remarkable success of large language models (LLMs) across various multi-modality
applications is evident. However, integrating large language models with humans, or brain …
applications is evident. However, integrating large language models with humans, or brain …