Combining Generalized Eigenvalue Decomposing with Laplacian Filtering to Improve Cortical Decoding Performance
- Resource Type
- Conference
- Authors
- Khorasani, Abed; Samejima, Soshi; Shalchyan, Vahid; Daliri, Mohammad Reza; Moritz, Chet
- Source
- 2021 10th International IEEE/EMBS Conference on Neural Engineering (NER) Neural Engineering (NER), 2021 10th International IEEE/EMBS Conference on. :1140-1143 May, 2021
- Subject
- Bioengineering
Signal Processing and Analysis
Laplace equations
Filtering
Signal processing algorithms
Neural engineering
Signal processing
Eigenvalues and eigenfunctions
Brain-computer interfaces
- Language
- ISSN
- 1948-3554
Artifact removal is a key step toward designing real-world and efficient brain computer interfaces. Here we describe an automatic blind source separation algorithm applicable to real-time signal processing. The algorithm combines the generalized eigenvalue decomposition technique with Laplacian filtering to separate desired and undesired subspaces, exclude artifact sources and recover artifact-free cortical signals. The algorithm outperforms commonly used artifact removal methods in brain computer interfaces as measured by cortical decoding performance.