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 …
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 …
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 …
Evaluation of power spectral and machine learning techniques for the development of subject-specific BCI
Abstract Evaluation and interpretation of massive amounts of brain data are a big challenge
for the design of functional brain-computer interface (BCI) devices. In this chapter, three …
for the design of functional brain-computer interface (BCI) devices. In this chapter, three …
Feature extraction of four-class motor imagery EEG signals based on functional brain network
Q Ai, A Chen, K Chen, Q Liu, T Zhou… - Journal of neural …, 2019 - iopscience.iop.org
Objective. A motor-imagery-based brain–computer interface (MI-BCI) provides an alternative
way for people to interface with the outside world. However, the classification accuracy of MI …
way for people to interface with the outside world. However, the classification accuracy of MI …
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) …
Comparison of signal decomposition methods in classification of EEG signals for motor-imagery BCI system
In this study, three popular signal processing techniques (Empirical Mode Decomposition,
Discrete Wavelet Transform, and Wavelet Packet Decomposition) were investigated for the …
Discrete Wavelet Transform, and Wavelet Packet Decomposition) were investigated for the …
A new framework for automatic detection of motor and mental imagery EEG signals for robust BCI systems
Nonstationary signal decomposition (SD) is a primary procedure to extract monotonic
components or modes from electroencephalogram (EEG) signals for the development of …
components or modes from electroencephalogram (EEG) signals for the development of …
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 …
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 …