Adaptive Second-Order Volterra Filtering Based on an H&amp;infin; Criterion

Matsuzaki, Hidenori

doi:10.9746/jcmsi.9.26

Cited by 1 publication

(2 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Most celebrated are the fixed and exponentially-weighted assumptions. The general usage of combined Volterra series and H ∞ filtering with exponential weight assumption (V-H ∞ /exp) was discussed in [19]. The author investigates the performance of the V-H ∞ /exp and compares it to Volterra RLS (V-RLS) and Volterra normalized LMS (V-nLMS).…”

Section: Introductionmentioning

confidence: 99%

“…Enhanced with the Volterra series expansion, we use the time-varying weight assumption for the H ∞ filter (as opposed to the exponential assumption as formulated in [19]). To enhance our method's accuracy for the harmonics of the fundamental movement frequency, we employ a framework that can identify all major contamination frequencies by creating the narrow-band filter-banked version of our reference signal and handle all target contamination frequencies of the EEG in a cascade filtering method (figure 3).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Characterization and real-time removal of motion artifacts from EEG signals

Kilicarslan

Contreras-Vidal

2019

J. Neural Eng.

View full text Add to dashboard Cite

Objective. Accurate implementation of real-time non-invasive brain-machine/computer interfaces (BMI/BCI) requires handling physiological and nonphysiological 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.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%