Deep learning techniques for classification of electroencephalogram (EEG) motor imagery (MI) signals: A review
The brain–computer interface (BCI) is an emerging technology that has the potential to
revolutionize the world, with numerous applications ranging from healthcare to human …
revolutionize the world, with numerous applications ranging from healthcare to human …
Brain-computer interface: Advancement and challenges
Brain-Computer Interface (BCI) is an advanced and multidisciplinary active research domain
based on neuroscience, signal processing, biomedical sensors, hardware, etc. Since the …
based on neuroscience, signal processing, biomedical sensors, hardware, etc. Since the …
Environment sound classification using a two-stream CNN based on decision-level fusion
With the popularity of using deep learning-based models in various categorization problems
and their proven robustness compared to conventional methods, a growing number of …
and their proven robustness compared to conventional methods, a growing number of …
A hybrid feature selection approach based on information theory and dynamic butterfly optimization algorithm for data classification
A Tiwari, A Chaturvedi - Expert Systems with Applications, 2022 - Elsevier
The ubiquitous usage of feature selection in search space optimization, information retrieval,
data mining, signal processing, software fault prediction, and bioinformatics is paramount to …
data mining, signal processing, software fault prediction, and bioinformatics is paramount to …
A novel deep learning approach with data augmentation to classify motor imagery signals
Z Zhang, F Duan, J Sole-Casals… - IEEE …, 2019 - ieeexplore.ieee.org
Brain-computer interface provides a new communication bridge between the human mind
and devices, depending largely on the accurate classification and identification of non …
and devices, depending largely on the accurate classification and identification of non …
Exploiting dimensionality reduction and neural network techniques for the development of expert brain–computer interfaces
Background: Analysis and classification of extensive medical data (eg
electroencephalography (EEG) signals) is a significant challenge to develop effective brain …
electroencephalography (EEG) signals) is a significant challenge to develop effective brain …
[HTML][HTML] A blockchain security module for brain-computer interface (BCI) with multimedia life cycle framework (MLCF)
A brain-computer interface (BCI) affords real-time communication, significantly improving the
quality of lifecycle, brain-to-internet (B2I) connectivity, and communication between the brain …
quality of lifecycle, brain-to-internet (B2I) connectivity, and communication between the brain …
Multi-modal emotion recognition using EEG and speech signals
Abstract Automatic Emotion Recognition (AER) is critical for naturalistic Human–Machine
Interactions (HMI). Emotions can be detected through both external behaviors, eg, tone of …
Interactions (HMI). Emotions can be detected through both external behaviors, eg, tone of …
Motor imagery EEG signals classification based on mode amplitude and frequency components using empirical wavelet transform
As one of the key techniques determining the overall system performances, efficient and
reliable algorithms for improving the classification accuracy of motor imagery (MI) based …
reliable algorithms for improving the classification accuracy of motor imagery (MI) based …
Attention-inception and long-short-term memory-based electroencephalography classification for motor imagery tasks in rehabilitation
In recent years, the contributions of deep learning have had a phenomenal impact on
electroencephalography-based brain-computer interfaces. While the decoding accuracy of …
electroencephalography-based brain-computer interfaces. While the decoding accuracy of …