时空域的脑电模式研究

摘要

Experimental evidence proposes that complex cognitive funcTiO2s and behavioral acts are arranged at global levels in the brain and they come up from synchronized operaTiO2 of localize brain areas.In that sense the estimaTiO2 of the spatial-temporal organizaTiO2 of the brain activity is a promising approach for studying the integrative activity of the brain.This dissertaTiO2 focused on the method,developed by Professor Walter Freeman group,to study spatial-temporal patterns of brain activity in signals recorded at the cortical surface(ECoG).The main goal was to adapt the methodology,developed for ECoG signal,for it use on signal recorded at the scalp(EEG).It is important because EEG recording is a non-invasive procedure than can be applied repeatedly to patients, normal adults and children with virtually no risk or limitaTiO2 and makes it possible to take advantageboth of the cognitive and phenomenological awareness of a normal healthy subject. Initially an improved method to study the spatial-temporal patterns in ECoG signal was developed and compared with the classical cone fitting.The improved method significantly reduces the processing time while the coincidence level in time domain obtained by both methods is kept high.Consequently,it is a promising tool to study ECoG in real time.In addiTiO2 AM patterns can be clearly distinguishable using the improved method.Then the improved method was adapted for use on EEG signals and tested in two databases. Due to the well known differences in scale and resoluTiO2 between ECoG and EEG, adjustments in the techniques to identify stable frames and estimated frame gradient were necessary. The first database analyzed was a database previously uses to study event related potentials.In this case the experiments were conducted trial by trial:each trial was l second length after the stimuli presentaTiO2.Stable frames were used as time markers to extract high dimensional feature from the analytic power within the time samples that stable frames showed up.The mean level of classificaTiO2 for EEG was comparable to that for ECoG.which shows for the first time that.owing to the scale-free properties of brain activity.these techniques adapted from ECoG can be employed to extract useful informaTiO2 noninvasively from scalp EEG. The second database was continues recording of brain activity during a perceptual-cognitive task.in which the stimulus was a combinaTiO2 of two modalities,auditory and visual stimulaTiO2. The objective was to test the hypothesis that patterns of oscillaTiO2 related to perceptual categories occur in scalp EEG.Global amplitude patterns in the range of 15-22 Hz from all subjects were classified with respect to stimulus combinaTiO2 type significantly above chance levels shortly after the stimulus onset.At the same time the classificaTiO2 rate in the pre-stimulus period or late post-stimulus period was not statistically different from chance. The results showed that this adapted technique can provide the foundaTiO2 needed to investigate the cerebral dynamics of learning or at least offer some insights that may help substantially in devising further experimental exploraTiO2.

目录

英文文摘

List of Figures

List of Tables

List of Acronyms and Variables

Acknowledgments

1 Chapter l Introduction

1.1 Two theories of brain functioning

1.1.1 Choices of localizationists to process brain activity

1.1.2 Choices of globalists to process brain activity

1.2 Stability,instability,and metastability to explain brain functioning

1.2.1 Spatial-temporal patterns at mesoscopic scales

1.3 0bjectives and overview of the dissertation

2 Chapter 2 Studies of Brain Activity at Mesoscopic Scales

2.1 Applying the Hilbert Transform

2.1.1 Studding the Amplitude Modulation patterns

2.1.2 Studding the Phase Modulation patterns

2.2 Cone fitting method to detect frames

2.3 Improved method

2.3.1 Line fitting to estimated instantaneous grasienl

2.3.2 Using covariance to detect flames

2.4 Comparing cone fitting and improved method

2.5 An application in ECoG recording

2.5.1 ECoG recording

2.5.2 Signal selection and processing

2.5.3 Results and discussion

2.6 Summary

3 Chapter 3 Studies of Brain Activity at Macroscopic Scales

3.1 Data description

3.2 Data processing

3.2.1 Detecting stable frames

3.2.2 Estimating instantaneous frequency and gradient

3.3 AM patterns

3.3.1 Classification process

3.4 Results

3.5 Summary

4 Chapter 4 Studies in an EEG Database During a Perceptual-Cognitive Task

4.1 Experimental design and signal processing

4.1.1 Location of the AM patterns

4.1.2 Analyzing the patterns spatial distribution

4.2 Results

4.2.1 Study in phase resetting rate

4.2.2 Frames location and classification

4.2.3 Contribution of the electrodes to the spatial patterns

4.3 Discussion and Summary

5 Chapter 5 Conclusions

5.1 Innovations and future works

References

Appendix A List of Papers

Appendix B Representative Papers

Detecting stable phase structures in EEG signals to classify brain ativity amplitude patterns