Objective: Accurate implementation of real-time non-invasive Brain-Machine / Computer Interfaces (BMI / BCI) requires handling physiological and non-physiological artifacts associated with the measurement modalities. For example, scalp electroencephalographic (EEG) measurements are often considered prone to excessive motion artifacts and other types of artifacts that contaminate the EEG recordings. Although the magnitude of such artifacts heavily depends on the task and the setup, complete minimization or isolation of such artifacts is generally not possible.
Approach: We present an adaptive de-noising framework with robustness properties, using a Volterra based non-linear mapping to characterize and handle the motion artifact contamination in EEG measurements. We asked healthy able-bodied subjects to walk on a treadmill at gait speeds of 1-to-4 mph, while we tracked the motion of select EEG electrodes with an infrared video-based motion tracking system. We also placed Inertial Measurement Unit (IMU) sensors on the forehead and feet of the subjects for assessing the overall head movement and segmenting the gait.
Main Results: We discuss in detail the characteristics of the motion artifacts and propose a real-time compatible solution to filter them. We report the effective handling of both the fundamental frequency of contamination (synchronized to the walking speed) and its harmonics. Event-Related Spectral Perturbation (ERSP) analysis for walking shows that the gait dependency of artifact contamination is also eliminated on all target frequencies. Significance: The real-time compatibility and generalizability of our adaptive filtering framework allows for the effective use of non-invasive BMI/BCI systems and greatly expands the implementation type and application domains to other types of problems where signal denoising is desirable. Combined with our previous efforts of filtering ocular artifacts, the presented technique allows for a comprehensive adaptive filtering framework to increase the EEG Signal to Noise Ratio (SNR). We believe the implementation will benefit all non-invasive neural measurement modalities, including studies discussing neural correlates of movement and other internal states, not necessarily of BMI focus.
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This content will become publicly available on November 6, 2024
EM-Based Radar Signal Processing and Tracking of Maneuvering Targets
The accuracy of radar tracks depends strongly on
the variances of the measurements, and those variances are
inversely proportional to the signal-to-noise (SNR) produced
the hardware and signal processor. The signal processor uses
matched filter processing, and the efficiency of that depends on
knowledge of the kinematics of the target. In particular, the
matched filter performance depends heavily on range rate and
range acceleration. Traditionally, the predicted state of the target
from the track filter is used for matched filter processing, but
the predicted kinematic state can have rather large errors, and
those errors result in match filter loss. This loss can be very
large for maneuvering (i.e., accelerating) targets. In this paper, an
expected-maximization (EM) approach is taken to jointly address
signal processing and tracking. The signal processor maximizes
the SNR using the predicted state and produces measurements.
The state estimator ( e.g., Kalman filter) uses those measurements
to produce expected values of the kinematic state (i.e. the nuisance
parameters). The signal processor then maximizes the SNR
using the new state estimates. This process continues until the
maximum likelihood values of the measurements are achieved.
In this paper, the Interacting Multiple Model (IMM) estimator
is introduced for the tracking function better address sudden
maneuvers. The EM-Based approach to join signal processing
and tracking are presented along with a discussion of the real-time
computing. Monte Carlo simulation results are given to
illustrate a 6 dB improvement in SNR and enhanced tracks for
a maneuvering target.
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- Award ID(s):
- 2008368
- NSF-PAR ID:
- 10470834
- Publisher / Repository:
- IEEE International Radar Conference 2023
- Date Published:
- Subject(s) / Keyword(s):
- ["Signal Processing","Target Tracking","Expectation-Maximization","Sensor Processing","Real-Time Computing"]
- Format(s):
- Medium: X
- Location:
- Sydney, Australia
- Sponsoring Org:
- National Science Foundation
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