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 …
Advancing task recognition towards artificial limbs control with ReliefF-based deep neural network extreme learning
In the rapidly advancing field of biomedical engineering, effective real-time control of
artificial limbs is a pressing research concern. Addressing this, the current study introduces a …
artificial limbs is a pressing research concern. Addressing this, the current study introduces a …
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 …
Masked EEG Modeling for Driving Intention Prediction
Driving under drowsy conditions significantly escalates the risk of vehicular accidents.
Recent endeavors to prevent driving accidents have focused on using …
Recent endeavors to prevent driving accidents have focused on using …
Adaptive search for broad attention based vision transformers
Abstract Vision Transformer (ViT) has prevailed among computer vision tasks for its powerful
capability of image representation recently. Frustratingly, the manual design of efficient …
capability of image representation recently. Frustratingly, the manual design of efficient …
Advancing biomedical engineering: Leveraging Hjorth features for electroencephalography signal analysis
Materials and methods At the heart of this investigation lies the MILimbEEG Dataset, an
extensive collection of EEG recordings harvested from a heterogeneous group of 60 …
extensive collection of EEG recordings harvested from a heterogeneous group of 60 …
Exploring CEEMDAN and LMD domains entropy features for decoding EEG-based emotion patterns
Electroencephalogram (EEG) signal-based emotion classification is vital in the ever-growing
human-computer interface (HCI) applications. However, the chaotic, non-stationary, and …
human-computer interface (HCI) applications. However, the chaotic, non-stationary, and …
A Neural Architecture Search CNN for Alzheimer's Disease Classification
The evolution of automated machine learning (AutoML) is gradually reengineering the
design of deep learning architectures for various imaging tasks. AutoML effectively develops …
design of deep learning architectures for various imaging tasks. AutoML effectively develops …
Estrategia de procesamiento de señales EEG en sistemas BCI utilizando aprendizaje profundo y medidas de conectividad
YA Gomez Rivera - repositorio.unal.edu.co
Las Interfaces Cerebro Computadora (BCI) basadas en Electroencefalografía (EEG) crean
una conexión directa entre el cerebro humano y una computadora. Los paradigmas de …
una conexión directa entre el cerebro humano y una computadora. Los paradigmas de …