Multiclass EEG signal classification utilizing Rényi min-entropy-based feature selection from wavelet packet transformation
This paper proposes a novel feature selection method utilizing Rényi min-entropy-based
algorithm for achieving a highly efficient brain–computer interface (BCI). Usually, wavelet …
algorithm for achieving a highly efficient brain–computer interface (BCI). Usually, wavelet …
Classification of multiclass motor imagery EEG signal using sparsity approach
Motor imagery (MI) based brain–computer interface systems involving multiple tasks are
highly required in many real-time applications such as hands and touch-free text entry …
highly required in many real-time applications such as hands and touch-free text entry …
[HTML][HTML] Alpha and high gamma phase amplitude coupling during motor imagery and weighted cross-frequency coupling to extract discriminative cross-frequency …
Motor imagery modulates specific neural oscillations like actual movement does.
Representatively, suppression of the alpha power (eg, event-related desynchronization …
Representatively, suppression of the alpha power (eg, event-related desynchronization …
Development of a human–robot hybrid intelligent system based on brain teleoperation and deep learning SLAM
To achieve the better navigation performance of a mobile robot in the unknown
environments, a novel human-robot hybrid system incorporating a motor-imagery (MI)-based …
environments, a novel human-robot hybrid system incorporating a motor-imagery (MI)-based …
Modeling and classification of voluntary and imagery movements for brain–computer interface from fNIR and EEG signals through convolutional neural network
Practical brain–computer interface (BCI) demands the learning-based adaptive model that
can handle diverse problems. To implement a BCI, usually functional near-infrared …
can handle diverse problems. To implement a BCI, usually functional near-infrared …
A multi-classification algorithm based on multi-domain information fusion for motor imagery BCI
J Wang, W Chen, M Li - Biomedical Signal Processing and Control, 2023 - Elsevier
The current problem of motor imagery Electroencephalogram (EEG) signal classification is
low classification accuracy and fixed EEG channel selection. We proposed a novel …
low classification accuracy and fixed EEG channel selection. We proposed a novel …
The performance impact of data augmentation in CSP-based motor-imagery systems for BCI applications
Objective This work investigates the performance impact of time-delay data-augmentation in
motor-imagery classifiers for brain-computer-interface (BCI) applications. Methods The …
motor-imagery classifiers for brain-computer-interface (BCI) applications. Methods The …
Hilbert transform-based event-related patterns for motor imagery brain computer interface
Event-related patterns (EPs) play an essential role in detecting motor imagery (MI)
movements of the subject. Due to the difference in the spatial and temporal distribution of …
movements of the subject. Due to the difference in the spatial and temporal distribution of …
An approach of one-vs-rest filter bank common spatial pattern and spiking neural networks for multiple motor imagery decoding
H Wang, C Tang, T Xu, T Li, L Xu, H Yue, P Chen… - Ieee …, 2020 - ieeexplore.ieee.org
Motor imagery (MI) is a typical BCI paradigm and has been widely applied into many
aspects (eg brain-driven wheelchair and motor function rehabilitation training). Although …
aspects (eg brain-driven wheelchair and motor function rehabilitation training). Although …
Correlation-based common spatial pattern (CCSP): A novel extension of CSP for classification of motor imagery signal
K Darvish Ghanbar, T Yousefi Rezaii, A Farzamnia… - Plos one, 2021 - journals.plos.org
Common spatial pattern (CSP) is shown to be an effective pre-processing algorithm in order
to discriminate different classes of motor-based EEG signals by obtaining suitable spatial …
to discriminate different classes of motor-based EEG signals by obtaining suitable spatial …