In this paper we present an off-line Kalman filter approach to remove transcranial magnetic stimulation (TMS)-induced artifacts from electroencephalographic (EEG) recordings. Two dynamic models describing EEG and TMS signals generation are identified from data and the Kalman filter is applied to the linear system arising from their combination. The keystone of the approach is the use of time-varying covariance matrices suitably tuned on the physical parameters of the problem that allow to model the nonstationary components of the EEG-TMS signal. This guarantees an efficient deletion of TMS-induced artifacts while preserving the integrity of EEG signals around TMS impulses. Experimental results show that the Kalman filter is more effective than stationary filters (Wiener filter) for the problem under investigation.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1016/j.jneumeth.2006.12.013 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Carbon Dioxide Sensing Based on Off-Axis Integrated Cavity Absorption Spectroscopy Combined with the Informer and Multilayer Perceptron Models.
Anal Chem
January 2025
Key Laboratory of Nondestructive Test (Ministry of Education), Nanchang Hangkong University, Nanchang 330063, China.
Off-axis integrated cavity output spectroscopy (OA-ICOS) allows the laser to be reflected multiple times inside the cavity, increasing the effective absorption path length and thus improving sensitivity. However, OA-ICOS systems are affected by various types of noise, and traditional filtering methods offer low processing efficiency and perform limited feature extraction. Deep learning models enable us to extract important features from large-scale, complex spectral data and analyze them efficiently and accurately.
View Article and Find Full Text PDFAccurately estimating phase is crucial in continuous-variable quantum key distribution systems, directly impacting the final secret key rate. In previous systems that utilize the local local oscillator, phase estimation is closely tied to the amplitude and signal-to-noise ratio (SNR) of the pilot signal. As SNR decreases, so does the accuracy of phase estimation, leading to increased excess noise and a potential loss of the system's secret key rate.
View Article and Find Full Text PDFAn Extended Langevinized Ensemble Kalman Filter for non-Gaussian Dynamic Systems.
Comput Stat
September 2024
Department of Statistics, Purdue University, West Lafayette, IN 47907.
State estimation for large-scale non-Gaussian dynamic systems remains an unresolved issue, given nonscalability of the existing particle filter algorithms. To address this issue, this paper extends the Langevinized ensemble Kalman filter (LEnKF) algorithm to non-Gaussian dynamic systems by introducing a latent Gaussian measurement variable to the dynamic system. The extended LEnKF algorithm can converge to the right filtering distribution as the number of stages become large, while inheriting the scalability of the LEnKF algorithm with respect to the sample size and state dimension.
View Article and Find Full Text PDFAn Improved Unscented Kalman Filter Applied to Positioning and Navigation of Autonomous Underwater Vehicles.
Sensors (Basel)
January 2025
College of Mechatronics Engineering, North University of China, Taiyuan 030051, China.
To enhance the positioning accuracy of autonomous underwater vehicles (AUVs), a new adaptive filtering algorithm (RHAUKF) is proposed. The most widely used filtering algorithm is the traditional Unscented Kalman Filter or the Adaptive Robust UKF (ARUKF). Excessive noise interference may cause a decrease in filtering accuracy and is highly likely to result in divergence by means of the traditional Unscented Kalman Filter, resulting in an increase in uncertainty factors during submersible mission execution.
View Article and Find Full Text PDFEnhancing Off-Road Topography Estimation by Fusing LIDAR and Stereo Camera Data with Interpolated Ground Plane.
Sensors (Basel)
January 2025
Engineering Design, KTH Royal Institute of Technology, SE-100 44 Stockholm, Sweden.
Topography estimation is essential for autonomous off-road navigation. Common methods rely on point cloud data from, e.g.
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!