Importance-weighted covariance estimation for robust common spatial pattern
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Summary
This paper shows a practical way to make Common Spatial Pattern (CSP), a classical feature extraction that is particularly useful in BCI, robust to non-stationarity, and makes the covariance estimation (used as input by every CSP variant) more robust toNon- stationarity.
- Type
- article
- Published
- 2014-11-10
- Cited by
- 14
- References
- 25
- OpenAlex
- https://openalex.org/W1802301915
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:46523959
Keywords
Computer science, Covariance, Weighting, Estimator, Artificial intelligence
References
- An Algorithm for Idle-State Detection in Motor-Imagery-Based Brain-Computer Interface
- Covariate Shift Adaptation by Importance Weighted Cross Validation
- Slow Sphering to Suppress Non-Stationaries in the EEG
- Learning under nonstationarity: covariate shift and class‐balance change
- A review of kernels on covariance matrices for BCI applications
- A Covariate Shift Minimisation Method to Alleviate Non-stationarity Effects for an Adaptive Brain-Computer Interface
- Finding stationary subspaces in multivariate time series.
- The Self-Paced Graz Brain-Computer Interface: Methods and Applications
- Classification of covariance matrices using a Riemannian-based kernel for BCI applications
- Stationary common spatial patterns for brain–computer interfacing
- Direct importance estimation for covariate shift adaptation
- Semi-supervised feature extraction for EEG classification
- A review of classification algorithms for EEG-based brain–computer interfaces
- Optimizing Spatial filters for Robust EEG Single-Trial Analysis
- The BCI competition III: validating alternative approaches to actual BCI problems
- A Least-squares Approach to Direct Importance Estimation
- Event-related EEG/MEG synchronization and desynchronization: basic principles.
- Divergence-Based Framework for Common Spatial Patterns Algorithms
- Classification of kinetics of movement for lower limb using covariate shift method for brain computer interface
- Regularizing Common Spatial Patterns to Improve BCI Designs: Unified Theory and New Algorithms
Cited by
- Riemannian Approaches in Brain-Computer Interfaces: A Review
- Development of a human computer interaction system based on multi-modal gaze tracking methods
- Robust Averaging of Covariances for EEG Recordings Classification in Motor Imagery Brain-Computer Interfaces
- Incremental learning algorithms and applications
- On robust parameter estimation in brain–computer interfacing
- Önem Tahminleme Tabanlı Tek Sınıf Sınıflayıcı ile Doku Tanıma
- EEG Signal Processing in MI-BCI Applications With Improved Covariance Matrix Estimators
- Analysis and classification of hybrid BCI based on motor imagery and speech imagery
- EEG Signal Processing in Motor Imagery Brain Computer Interfaces with Improved Covariance Estimators
- Classification of motor imagery using multisource joint transfer learning.
- Scatter-based common spatial patterns - a unified spatial filtering framework
- Clinical BCI Challenge-WCCI2020: RIGOLETTO - RIemannian GeOmetry LEarning, applicaTion To cOnnectivity
- RIGOLETTO -- RIemannian GeOmetry LEarning: applicaTion To cOnnectivity. A contribution to the Clinical BCI Challenge -- WCCI2020
- Linguistically Described Covariance Matrix Estimation
- Multiple Instance Learning Selecting Time-Frequency Features for Brain Computing Interfaces
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