Biomedical Signal and Data Processing Lab - EDSB Lab

1. Elaborazione di Dati, Segnali e Immagini Biomediche, EDSB Lab Anna M. Bianchi

2. Biomedical Signal and Data Processing Lab Head: Prof. S. Cerutti • Staff: 7 faculty • 5 Post doc • 15 PhD students Processing tools • 12 workstations for signal and image processing • Dedicated software • 32 channel EEG recorder • ProComp device for biomedical signal recording: ECG, respiration, skin condictance • Finapres-Omeda device for continuous wave Blood pressure recording • LD fluximeter

3. Research activities I • Cardiovascular and Autonomic Nervous System • Heart rate and cardiovascular variability signals • Time-variant and time-frequency methods • Wavelet analysis • Non-Linear dynamics • Modeling of Cardiovascular Control • Multivariate parametric modeling • Genomics and Proteinomics data analysis

4. Fp1 F3 C3 Research activities related to EEG • Central Nervous System • EEG analysis and EEG map • Parametric AR methods • Wavelet analysis • Non-Linear dynamics • Evoked potentials • Single sweep analysis (ARX model) • High resolution EEG • Dipole localization • Cortical mapping • Laplacian filtering • Signal and Image fusion • EEG – MR interatio • EEG – fMRI integration • EEG – fNIRS interation

5. Role of EDSB in the NICE project

6. Movement planning 6 PLANNING (predictivevoluntarymovement) Frontoorbitalcortex BA 11 and 12 Whatto do Fronto lateral cortex BA 44 and 45 Howto do it Supplementary motor area BA 6 When to do it

7. Movement execution EXECUTION (motor realization) FEEDBACK (reactive motor control) Feedback motor correction …100 ms … Error Feed-forward motor command Peripheral feedback Current sensory state

8. Post synaptic potentials in piramidal neurons EEGsum of the activities of millions of neurons Synchronized activity of neural populations Measure of synchronization The EEG signal

9. Investigation of cognitive and motor functions EEG Signal Advanced methods of signal processing Correlation to cognitive or motor functions (physiological or pathological conditions) Experimental protocol Subject

10. Amplitude of EEG rhythms is modulated during cognitive/ motor tasks Event Related Desynchronization/Synchronization (ERD/ERS) Pfurtscheller 1991

11. ERD/ERS during the voluntary movement of the forefinger: methods G. Foffani, A.M. Bianchi, A. Priori, G. Baselli, Adaptive autoregressive identification with spectral power decomposition for studying movement-related activity in scalp EEG signals and basal ganglia local fiels potentials, J. Neurol. Eng., 1165-173, 2004

12. ERD/ERS during the voluntary movement of the forefinger: results Power Frequency

13. Combining movement and EEG analysis: Experimental setup Jasper, 1958 10/20 EEG montage + Cb1 e Cb2 +EOG + 1 deriv ECG + 3 deriv EMG (biceps, triceps, delthoid) Rab markerization Rab, 2002

14. Neural parameters ERS / ERD method • EEG signal was filtered in the: • ALPHA band (7-13 Hz) • BETA band (13-25 Hz) • Epochs were extracted : • -0.5 to 1.5 srelative tocuepresentation • -2.5 to 5.0 srelative totargetpresentation ERD alpha (cue) ERS alpha (cue) ERD-ERS alpha (cue) ERD beta (target) ERS beta (target) ERD-ERS beta (target) EEG power was calculated in each band Power normalization at lead C3, with respect to the corresponding mean rest value, taken before the cue

15. Velocity • GoingMovementDuration (GMD) • PeakofVelocityIndex (PV) Go Back • Indexof Curvature (IC) • Average Inter-Quartile RangeIndex (IQR) with a=x,y,z Kinematic parameters STOP TRAJECTORY Ltr Lo • AverageJerkIndex (AJ) START

16. Alfa ERD – motor planning ERD/ERS HHT TOOL 1 TOOL 2 TOOL 3 TOOL 1 TOOL 2 TOOL 3 AVERAGE TOOLS EARLY INTERMEDIATE LATE EARLY INTERMEDIATE LATE AVERAGE STAGES

17. 17 Beta ERD/ERS – motor execution ERD/ERS HHT AVERAGE TOOLS TOOL 1 TOOL 2 TOOL 3 TOOL 1 TOOL 2 TOOL 3 AVERAGE STAGES EARLY INTERMEDIATE LATE EARLY INTERMEDIATE LATE

18. Neuro-kinematic correlations GrandAverage (180 trials) PLANNINGAJ positively correlates with ERD/ERS IQR negatively correlates with ERS EXECUTIONPV negatively correlates with ERD AJ negatively correlates with ERD and ERS GMD positively correlates with ERD

19. Study of the EEG characteristics before and after rehabilitation protocols • Quantitative parameters for describing the CNS functionality related to the movement • Relate CNS changes to the new functional situation • Investigate cerebral plasticity induced by treatment • More complete view of the effects of the treatment • Better interpretation of the effects of the rehabilitation therapy • Help for a more focused therapeutic intervention

20. Phase-locking to events Event Related Potentials might (at least in part) be generated by a phase reset of ongoing ascillatory EEG activity

21. Averaging • Used in clinical and reserach apllications: requires 100 – 1000 sweep for a reliable reduction fo noise • Provides information about the “average” behaviour of the evoked potentials during the whole recording session • Not reliable for protocols that imply variations in the responses or in the subject’s conditions • Learning • Abituation • ..

22. ARX Model Cerutti S., Baselli G., Liberati D., Pavesi G., Single sweep analysis of visual evoked potentials through a model of parametric identification. Biol Cybern. 1987;56(2-3):111-20

23. Learning protocol

24. Signals F3 C3 NoFb-NoKr Kr Fb O1

25. PARAMETERS RELATED TO P300 WAVE ELECTRODE F3 NOT-LEARNING SUBJECT LEARNING SUBJECT Kr NoFb-NoKr

26. Independent components (ICA)

27. Letter Presentation Letter Recognition LORETA fMRI overlap