[HTML][HTML] A review of the role of machine learning techniques towards brain–computer interface applications
S Rasheed - Machine Learning and Knowledge Extraction, 2021 - mdpi.com
This review article provides a deep insight into the Brain–Computer Interface (BCI) and the
application of Machine Learning (ML) technology in BCIs. It investigates the various types of …
application of Machine Learning (ML) technology in BCIs. It investigates the various types of …
Epilepsy detection from EEG using complex network techniques: A review
Epilepsy is one of the most chronic brain disorder recorded from since 2000 BC. Almost one-
third of epileptic patients experience seizures attack even with medicated treatment. The …
third of epileptic patients experience seizures attack even with medicated treatment. The …
[HTML][HTML] A simplified CNN classification method for MI-EEG via the electrode pairs signals
X Lun, Z Yu, T Chen, F Wang, Y Hou - Frontiers in Human …, 2020 - frontiersin.org
A brain-computer interface (BCI) based on electroencephalography (EEG) can provide
independent information exchange and control channels for the brain and the outside world …
independent information exchange and control channels for the brain and the outside world …
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 …
Motor imagery BCI classification based on novel two‐dimensional modelling in empirical wavelet transform
Brain complexity and non‐stationary nature of electroencephalography (EEG) signal make
considerable challenges for the accurate identification of different motor‐imagery (MI) tasks …
considerable challenges for the accurate identification of different motor‐imagery (MI) tasks …
Toward the development of versatile brain–computer interfaces
Recent advances in artificial intelligence demand an automated framework for the
development of versatile brain–computer interface (BCI) systems. In this article, we …
development of versatile brain–computer interface (BCI) systems. In this article, we …
[HTML][HTML] Implementation of artificial intelligence and machine learning-based methods in brain–computer interaction
K Barnova, M Mikolasova, RV Kahankova… - Computers in Biology …, 2023 - Elsevier
Brain–computer interfaces are used for direct two-way communication between the human
brain and the computer. Brain signals contain valuable information about the mental state …
brain and the computer. Brain signals contain valuable information about the mental state …
[HTML][HTML] Investigating feature selection techniques to enhance the performance of EEG-based motor imagery tasks classification
Analyzing electroencephalography (EEG) signals with machine learning approaches has
become an attractive research domain for linking the brain to the outside world to establish …
become an attractive research domain for linking the brain to the outside world to establish …
A matrix determinant feature extraction approach for decoding motor and mental imagery EEG in subject-specific tasks
This study introduces a novel matrix determinant feature extraction approach for efficient
classification of motor and mental imagery activities from electroencephalography (EEG) …
classification of motor and mental imagery activities from electroencephalography (EEG) …
[HTML][HTML] Identification of motor and mental imagery EEG in two and multiclass subject-dependent tasks using successive decomposition index
The development of fast and robust brain–computer interface (BCI) systems requires non-
complex and efficient computational tools. The modern procedures adopted for this purpose …
complex and efficient computational tools. The modern procedures adopted for this purpose …