Mutual information spectrum for selection of event-related spatial components. Application to eloquent motor cortex mapping

Ossadtchi, Alexei; Pronko, Platon; Baillet, Sylvain; Pflieger, Mark E.; Stroganova, Tatiana A.

doi:10.3389/fninf.2013.00053

Cited by 6 publications

(4 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These processing steps corresponded to the settings of our neurofeedback experiments where, after recording a subject's resting state activity for one minute, we adjusted the signal processing parameters and then proceeded to the main body of the experiment. To eliminate eye artifacts, we performed independent component analysis (ICA) on the training data, identified eye movement-related components by means of the mutual information spectrum [40], and removed the artifactual data characterized by the highest mutual information with the two frontal channels Fp1 and Fp2. Only the parietal P4 channel of the cleaned data was used for the analysis and it played the role of x[n] in (1).…”

Section: Methods Comparison 231 Data and Preprocessingmentioning

confidence: 99%

Digital filters for low-latency quantification of brain rhythms in real time

et al. 2020

Self Cite

View full text Add to dashboard Cite

Objective. The rapidly developing paradigm of closed-loop neuroscience has extensively employed brain rhythms as the signal forming real-time neurofeedback, triggering brain stimulation, or governing stimulus selection. However, the efficacy of brain rhythm contingent paradigms suffers from significant delays related to the process of extraction of oscillatory parameters from broad-band neural signals with conventional methods. To this end, real-time algorithms are needed that would shorten the delay while maintaining an acceptable speed-accuracy trade-off. Approach. Here we evaluated a family of techniques based on the application of the least-squares complex-valued filter (LSCF) design to real-time quantification of brain rhythms. These techniques allow for explicit optimization of the speed-accuracy trade-off when quantifying oscillatory patterns. We used EEG data collected from 10 human participants to systematically compare LSCF approach to the other commonly used algorithms. Each method being evaluated was optimized by scanning through the grid of its hyperparameters using independent data samples. Main results. When applied to the task of estimating oscillatory envelope and phase, the LSCF techniques outperformed in speed and accuracy both conventional Fourier transform and rectification based methods as well as more advanced techniques such as those that exploit autoregressive extrapolation of narrow-band filtered signals. When operating at zero latency, the weighted LSCF approach yielded 75% accuracy when detecting alpha-activity episodes, as defined by the amplitude crossing of the 95th-percentile threshold. Significance. The LSCF approaches are easily applicable to low-delay quantification of brain rhythms. As such, these methods are useful in a variety of neurofeedback, brain-computer-interface and other experimental paradigms that require rapid monitoring of brain rhythms.

show abstract

Section: Methods Comparison 231 Data and Preprocessingmentioning

confidence: 99%

Digital filters for low-latency quantification of brain rhythms in real time

et al. 2020

Self Cite

View full text Add to dashboard Cite

show abstract

“…MI is usually calculated using log with a base of 2 as introduced by the computational information theorists and is measured in bits (Souza et al, 2018;Woo et al, 2015). This was retained in the present study as the concept of bits to measure information also suits remote sensing images (Horkaew & Puttinaovarat, 2017;Liang et al, 2014;Ossadtchi et al, 2014).…”

Section: Data Assimilation Frameworkmentioning

confidence: 99%

A Mutual Information‐Based Likelihood Function for Particle Filter Flood Extent Assimilation

Dasgupta

Hostache

Ramsankaran

et al. 2021

Water Resources Research

View full text Add to dashboard Cite

Accurate flood inundation forecasts have the potential to minimize socioeconomic losses, but uncertainties in inflows propagated from the precipitation forecasts result in large prediction errors. Recent studies suggest that by assimilating independent flood observations, inherent uncertainty in hydraulic flood inundation modeling can be mitigated. Satellite observations from Synthetic Aperture Radar (SAR) sensors, with demonstrated flood monitoring capability, can thus be used to reduce flood forecast uncertainties through assimilation. However, researchers have struggled to develop an appropriate cost function to determine the innovation to be applied at each assimilation time step. Thus, a novel likelihood function based on mutual information (MI) is proposed here, for use with a particle filter‐based (PF) flood extent assimilation framework. Using identical twin experiments, synthetic SAR‐based probabilistic flood extents were assimilated into the hydraulic model LISFLOOD‐FP using the proposed PF‐MI algorithm. The 2011 flood event in the Clarence Catchment, Australia was used for this study. The impact of assimilating flood extents was evaluated in terms of subsequent flood extent evolution, floodplain water depths, flow velocities and channel water levels (WLs). Water depth and flow velocity simulations improved by ∼60% over the open loop on an average and persisted for up to 7 days, following the sequential assimilation of two post‐peak flood extent observations. Flood extents and channel WLs also showed mean improvements of ∼10% and ∼80% in accuracy, respectively, indicating that the proposed MI likelihood function can improve flood extent assimilation.

show abstract

“…It exercises the concept of spatial filtering, akin to already established techniques like Independent Component Analysis (ICA) (Makeig et al, 1995), Spatial-Spectral Decomposition (SSD) (Nikulin et al, 2011), or Source Power Co-modulation (SPoC) (Dähne et al, 2014). This ensures that the discovered spatial patterns are suitable for implementing a rigorous source localization procedure based on the notion of signal subspace (Mosher et al, 1992) as described for example in (Ossadtchi et al, 2014) in application to ICA.…”

Section: Introductionmentioning

confidence: 99%

Representational dissimilarity component analysis (ReDisCA)

Ossadtchi,

Semenkov,

Zhuravleva

et al. 2024

Preprint

Self Cite

View full text Add to dashboard Cite

The principle of Representational Similarity Analysis (RSA) posits that neural representations reflect the structure of encoded information, allowing exploration of spatial and temporal organization in brain information processing. Traditional RSA when applied to EEG or MEG data faces challenges in accessing activation timeseries at the brain source level due to modeling complexities and insufficient geometric/anatomical data. To address this, we introduce Representational Dissimilarity Component Analysis (ReDisCA), a method for estimating spatial-temporal components in EEG or MEG responses aligned with a target representational dissimilarity matrix (RDM). ReDisCA yields informative spatial filters and associated topographies, offering insights into the location of "representationally relevant" sources. Applied to evoked response timeseries, ReDisCA produces temporal source activation profiles with the desired RDM. Importantly, while ReDisCA does not require inverse modeling its output is consistent with EEG and MEG observation equation and can be used as an input to rigorous source localization procedures. Demonstrating ReDisCA's efficacy through simulations and comparison with conventional methods, we show superior source localization accuracy and apply the method to a real EEG dataset, revealing physiologically plausible representational structures without inverse modeling. ReDisCA adds to the family of inverse modeling free methods such as independent component analysis (Makeig et al., 1995), Spatial spectral decomposition (Nikulin et al., 2011) and Source power comodulation (Dahne et al., 2014) designed for extraction sources with desired properties from EEG or MEG data. Extending its utility beyond EEG and MEG analysis, ReDisCA is likely to find application in fMRI data analysis and exploration of representational structures emerging in multilayered artificial neural networks.

show abstract

Mutual information spectrum for selection of event-related spatial components. Application to eloquent motor cortex mapping

Cited by 6 publications

References 19 publications

Digital filters for low-latency quantification of brain rhythms in real time

Digital filters for low-latency quantification of brain rhythms in real time

A Mutual Information‐Based Likelihood Function for Particle Filter Flood Extent Assimilation

Representational dissimilarity component analysis (ReDisCA)

Contact Info

Product

Resources

About