Laplacian filters are widely used in neuroscience. In the context of brain-computer interfacing, they might be preferred to data-driven approaches such as common spatial patterns (CSP) in a variety of scenarios such as, e.g., when no or few user data are available or a calibration session with a multi-channel recording is not possible, which is the case in various applications. In this paper we propose the use of an ensemble of local CSP patches (CSPP) which can be considered as a compromise between Laplacian filters and CSP. Our CSPP only needs a very small number of trials to be optimized and significantly outperforms Laplacian filters in all settings studied. Additionally, CSPP also outperforms multi-channel CSP and a regularized version of CSP even when only very few calibration data are available, acting as a CSP regularizer without the need of additional hyperparameters and at a very low cost: 2-5 min of data recording, i.e. ten times less than CSP.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1088/1741-2560/8/2/025012 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
A 6D Object Pose Estimation Algorithm for Autonomous Docking with Improved Maximal Cliques.
Sensors (Basel)
January 2025
Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai 201210, China.
Accurate 6D object pose estimation is critical for autonomous docking. To address the inefficiencies and inaccuracies associated with maximal cliques-based pose estimation methods, we propose a fast 6D pose estimation algorithm that integrates feature space and space compatibility constraints. The algorithm reduces the graph size by employing Laplacian filtering to resample high-frequency signal nodes.
View Article and Find Full Text PDFEnhancing detection of SSVEP-based BCIs via a novel temporally local canonical correlation analysis.
J Neurosci Methods
February 2025
School of Electronics and Communication Engineering, Guangzhou University, Guangzhou 510006, China.
Background: In recent years, spatial filter-based frequency recognition methods have become popular in steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) systems. However, these methods are ineffective in suppressing local noise, and they rely on the length of the data. In practical applications, enhancing recognition performance with short data windows is a significant challenge for the BCI systems.
View Article and Find Full Text PDFViTAD: Leveraging modified vision transformer for Alzheimer's disease multi-stage classification from brain MRI scans.
Brain Res
January 2025
United International University, Department of Computer Science and Engineering, Dhaka, Bangladesh.
Article Synopsis
- Alzheimer's disease (AD) is a serious neurological condition affecting millions, making early and accurate diagnosis crucial for better management and patient outcomes.* -
- This study presents ViTAD, a new method using a modified Vision Transformer model to classify five stages of AD from brain MRI images, achieving a remarkable 99.98% accuracy.* -
- ViTAD outperforms traditional CNN models in both speed and accuracy, demonstrating its potential as a valuable tool for early diagnosis of Alzheimer's in clinical settings.*
Node classification in the heterophilic regime via diffusion-jump GNNs.
Neural Netw
January 2025
Department of Computer Science and Artificial Intelligence, Alicante, Spain.
In the ideal (homophilic) regime of vanilla GNNs, nodes belonging to the same community have the same label: most of the nodes are harmonic (their unknown labels result from averaging those of their neighbors given some labeled nodes). In other words, heterophily (when neighboring nodes have different labels) can be seen as a "loss of harmonicity". In this paper, we define "structural heterophily" in terms of the ratio between the harmonicity of the network (Laplacian Dirichlet energy) and the harmonicity of its homophilic version (the so-called "ground" energy).
View Article and Find Full Text PDFMulti-level multi-view network based on structural contrastive learning for scRNA-seq data clustering.
Brief Bioinform
September 2024
Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Chenggong, 650500, Yunnan, China.
Clustering plays a crucial role in analyzing scRNA-seq data and has been widely used in studying cellular distribution over the past few years. However, the high dimensionality and complexity of scRNA-seq data pose significant challenges to achieving accurate clustering from a singular perspective. To address these challenges, we propose a novel approach, called multi-level multi-view network based on structural consistency contrastive learning (scMMN), for scRNA-seq data clustering.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!