Artifact cancellation and nonparametric spectral analysis Outline Artifact
- Slides: 27
Artifact cancellation and nonparametric spectral analysis
Outline Ø Artifact processing Ø Artifact cancellation Ø Nonparametric spectral analysis
Introduction Ø Artifact processing l l Rejection cancellation Rejection main alternative • one would hope to retain data l Cancellation requirements • clinical information • no new artifacts • spike detectors l l Additive/multiplicative model Artifact reduction using linear filtering
Artifact cancellation Ø Using linearly combined reference signals Ø Adaptive artfact cancellation using linearly combined reference signals Ø Using filtered reference signals
Linearly combined reference signals Ø Eye movements & blinks l l several referene signals positioning additive model EOG linearly trasferred to EEG • weights
In detail Ø Uncorrelated Ø Mean square error Ø Minimization, differentation Ø Spatial correlation, cross correlation l l fixed over time zero gradient Ø Estimation l blinks, eye-movements at onset
In detail 2 Ø Number of reference signals Ø Only EOG cancelled Ø ECG Ø Rejection used a lot (in MEG) l expect when lots of blinks (ssp)
Adaptive version Ø Time-varying changes Ø Tracking of slow changes Ø Adaptive algorithm l l LMS weight(s) function of time • optimal solution changes with time l l method of steepest descent negative error gradient vector
In detail Ø Parameter selection l l Ø time noise Expectation l l instantaneous value zero setting performance estimation fluctuation of weights
Filtered reference signals Ø EOG potentials exhibit frequency dependence l l in trasfer to EEG sensor through tissue blinks and eye movements Ø Improved cancellation with transfer function replacement l l l spatial and temporal information v 0 estimation FIR (lengths)
Details Ø Stationary processes l l Second order characterisrics Correlation information fixed
Details 2 Ø No a priori information l can be implemented, modified error Ø Also adaptive version exists l a priori impulse responses calculated at calibration
Nonparametric spectral analysis Ø Richer characterization of background activity that with 1 D histograms Ø EEG rhythms Ø Correlate signals with sines and cosines Ø When? l Gaussian stationary signals • Stationary estimatation l Normal spontaneous waking activity
Nonparametric 2 Ø Fourier-based power spectrum analysis l no modeling assumptions Ø Spectral parameters l interpretation
Fourier-based power spectrum analysis Ø Power spectrum characterized by correlation function (stationary) l l l If ergodic, approximate with time average estimator (negative lags) combination called periodogram equals squared magnitude of DFT
Fourier considerations Ø Periodogram biased l l l window dependent (convolution) smearing (main lobe) leakage (side lobes) • synchronized rhythm better described by power in frequency band l variance periogoram • does not approach zero with sample increase l consistency
Periodogram Ø Windowing and averaging l leakage & periodogram variance reduction Ø Windows l from rectangular to smaller sidelobes • wider main lobe, spectral resolution Ø Variance reduction l nonoverlapping segments, averaging • resolution decrease, trade-off • combinations, degree of overlap
And then what. . .
Spectral parametrs Ø Resulting power spectrum often not readilty interpreted l l Condensed into compact set of parameters feature extraction • parameters describing prominent features of the spectrum l peaks, frequencies • general usage
Spectral choices Ø Visual inspection l l format selection assessing represantiveness Ø Scaling l scope of the analysis
Parameters Ø Power in frequency bands Ø Peak frequency Ø Spectral slope Ø Hjort descriptors Ø Spectral purity index
Power in frequency bands Ø Fixed/statistical bands l l alpha, beta, theta etc. from data Ø Ratio of, absolute power l comparison, nonphysiological factors
Peak frequency Ø Frequency, amplitude, width Ø ad hoc methods for determining peaks Ø more than just maximum l median, mean
Spectral slope Ø EEG activity made of 2 component l rhythmic, unstructured Ø Based on decay of high frequency components l one parameters approximation • least squares error Ø Quantifcation of EEG Ø Preconditioning of power estimate
Hjort descriptors Ø Spectral moments l l l H 0 (activity) H 1 (mobility) H 2 (complexity) Signal power, dominant frequency, bandwidth Ø Effectively in time domain Ø Clinically useful Ø
Spectral purity index (SPI) Ø Heuristic Ø Reflects signal bandwidth (H 2) Ø How well signal is described by a single frequency l noise susceptibility
Summary Ø Artifact cancellation l l reference signals linear combinations, filtering • adaptive version(s) Ø Spectral parameters l nonparametric • no modelling l parametric • interpretation
- Parametric vs nonparametric tests
- Types of statistics
- When do we use friedman test
- Parametric vs nonparametric test
- Nonparametric test
- Nonparametric methods
- Douwe postmus
- Vernier spectroscopy
- Yagami doc
- Trig cancellation property
- Wms adani login
- Srinivas kotni
- Unit cancellation
- Van riper approach to stuttering
- Pcda dts
- Unit cancellation
- Clutter cancellation
- Absa home loan cancellation
- Feature engineering
- Ksrtc cancellation charges
- Acoustic echo cancellation challenge
- Pgl cancellation policy
- Explorica travel protection plan
- Quotation sandwiches
- Spectral graph theory and its applications
- Spectral graph theory and its applications
- Factors affecting width and intensity of spectral lines
- Spectral regrowth