[HTML][HTML] Cognitive neuroscience and robotics: Advancements and future research directions
In recent years, brain-based technologies that capitalise on human abilities to facilitate
human–system/robot interactions have been actively explored, especially in brain robotics …
human–system/robot interactions have been actively explored, especially in brain robotics …
A comprehensive survey on emotion recognition based on electroencephalograph (EEG) signals
Emotion recognition using electroencephalography (EEG) is becoming an interesting topic
among researchers. It has made a remarkable entry in the domain of biomedical, smart …
among researchers. It has made a remarkable entry in the domain of biomedical, smart …
A transformer-based approach combining deep learning network and spatial-temporal information for raw EEG classification
The attention mechanism of the Transformer has the advantage of extracting feature
correlation in the long-sequence data and visualizing the model. As time-series data, the …
correlation in the long-sequence data and visualizing the model. As time-series data, the …
Convolutional neural network based approach towards motor imagery tasks EEG signals classification
This paper introduces a methodology based on deep convolutional neural networks (DCNN)
for motor imagery (MI) tasks recognition in the brain-computer interface (BCI) system. More …
for motor imagery (MI) tasks recognition in the brain-computer interface (BCI) system. More …
Adaptive Tunable Q Wavelet Transform-Based Emotion Identification
Emotion is a neuronic transient that drives a person to a certain action. Emotion recognition
from electroencephalogram (EEG) signals plays a vital role in the development of a brain …
from electroencephalogram (EEG) signals plays a vital role in the development of a brain …
Enhanced grasshopper optimization algorithm with extreme learning machines for motor‐imagery classification
KR Balmuri, SR Madala, PB Divakarachari… - Asian Journal of …, 2023 - Wiley Online Library
Abstract In Brain Computer Interface (BCI), achieving a reliable motor‐imagery classification
is a challenging task. The set of discriminative and relevant feature vectors plays a crucial …
is a challenging task. The set of discriminative and relevant feature vectors plays a crucial …
EEG emotion recognition based on TQWT-features and hybrid convolutional recurrent neural network
M Zhong, Q Yang, Y Liu, B Zhen, B **e - Biomedical signal processing …, 2023 - Elsevier
Electroencephalogram (EEG)-based emotion recognition has gained high attention in Brain-
Computer Interfaces. However, due to the non-linearity and non-stationarity of EEG signals …
Computer Interfaces. However, due to the non-linearity and non-stationarity of EEG signals …
Detection of Parkinson's disease using automated tunable Q wavelet transform technique with EEG signals
Deep brain simulations play an important role to study physiological and neuronal behavior
during Parkinson's disease (PD). Electroencephalogram (EEG) signals may faithfully …
during Parkinson's disease (PD). Electroencephalogram (EEG) signals may faithfully …
Differentiating brain states via multi-clip random fragment strategy-based interactive bidirectional recurrent neural network
EEG is widely adopted to study the brain and brain computer interface (BCI) for its non-
invasiveness and low costs. Specifically EEG can be applied to differentiate brain states …
invasiveness and low costs. Specifically EEG can be applied to differentiate brain states …
A classification method for EEG motor imagery signals based on parallel convolutional neural network
Deep learning has been used popularly and successfully in state of art researches to
classify different types of images. However, so far, the applications of deep learning methods …
classify different types of images. However, so far, the applications of deep learning methods …