Journal Article10.1016/j.compeleceng.2021.107608
An efficient P300 detection algorithm based on Kernel Principal Component Analysis-Support Vector Machine
11
TL;DR: In this article , features were obtained from wavelet coefficients and feature dimensions were reduced, thereby enhancing the speed of classification along with a manifold improvement in the accuracy of P300 signal classification.
read more
About: This article is published in Computers & Electrical Engineering. The article was published on 01 Jan 2022. The article focuses on the topics: Computer science & Brain–computer interface.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
Evaluation of hybrid forecasting methods for organic Rankine cycle: Unsupervised learning-based outlier removal and partial mutual information-based feature selection
TL;DR: In this paper , a hybrid algorithm for ORC system prediction model construction is proposed on the basis of the data characteristics, information theory and unsupervised learning, which can remove outliers and reduce the dimensionality of features simultaneously.
20
An Interpretable Convolutional Neural Network for P300 Detection: Analysis of Time Frequency Features for Limited Data
TL;DR: Results of cross-subject classification indicate the promising ability of the method in eliminating calibration in BCI systems and revealed the efficiency of cTF images for accurate P300 detection in simple structure classifiers having the advantage of fewer data and less memory requirement.
13
Enhancing P300 Detection Using a Band-Selective Filter Bank for a Visual P300 Speller
TL;DR: In this paper , three novel methods based on Filter Bank and Canonical Correlation Analysis (CCA) are proposed for the recognition of P300 ERPs using a reduced number of trials.
11
An efficient deep learning framework for P300 evoked related potential detection in EEG signal
TL;DR: TGT-MHOG-CNN as discussed by the authors combines Gabor transform and histogram of oriented gradients (HOG) to detect P300 evoked related potential (ERP) in EEG signal.
8
A complete scheme for multi-character classification using EEG signals from speech imagery.
Hongguang Pan,Yiran Wang,Zhuoyi Li,Xin Chu,Bingyang Teng,Hongzheng Gao +5 more
- 12 Mar 2024
TL;DR: A complete scheme for multi-character classification using EEG signals from speech imagery achieves high accuracy and enables visualization of character clusters.
5
References
Kernel Principal Component Analysis
Bernhard Schölkopf,Alexander J. Smola,Klaus-Robert Müller +2 more
- 08 Oct 1997
TL;DR: A new method for performing a nonlinear form of Principal Component Analysis by the use of integral operator kernel functions is proposed and experimental results on polynomial feature extraction for pattern recognition are presented.
2.6K
Convolutional Neural Networks for P300 Detection with Application to Brain-Computer Interfaces
H Cecotti,A Graser +1 more
TL;DR: A new method for the detection of P300 waves is presented, based on a convolutional neural network (CNN), which provides a new way for analyzing brain activities due to the receptive field of the CNN models.
A Kernel-Based Feature Selection Method for SVM With RBF Kernel for Hyperspectral Image Classification
TL;DR: Experimental results show that the proposed kernel-based feature selection method with a criterion that is an integration of the previous work and the linear combination of features improves the classification performance of the SVM.
394
Automatic Artifact Rejection From Multichannel Scalp EEG by Wavelet ICA
TL;DR: The method here proposed is shown to yield improved success in terms of suppression of artifact components while reducing the loss of residual informative data, since the components related to relevant EEG activity are mostly preserved.
267
Exploiting dimensionality reduction and neural network techniques for the development of expert brain–computer interfaces
TL;DR: Empirical wavelet transform (EWT) helped to explore the hidden patterns of MI tasks by decomposing EEG data into different modes and regularization parameter tuning of NCA guaranteed to improve classification performance with significant features for each subject.
104