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yyigeren
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How do the different regions of brain interact each other in different frequency?
Dynamic network properties determine cross-frequency coupling (CFC) signatures: 1) phase–phase coupling occur under weakly coupling and do not co-occur with phase–frequency coupling; 2) phase–amplitude coupling is present when the FO is intermittent or sparse spiking; 3) amplitude–amplitude coupling requires asymmetrical slow oscillations [1].

Lots of works on amplitude–amplitude coupling in large-scale brain networks, but few examined the phase-phase coupling and phase-amplitude coupling in resting-state networks (RSN).

Florin and Baillet reported that the amplitude of high-gamma (80–150 Hz) bursts was linearly interpolated between the troughs and peaks of the low-frequency phase cycles with highest PAC to high-frequency amplitude [2].


References:
[1] Hyafil A, Giraud AL, Fontolan L and Gutkin B (2015). 'Neural
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(2015-12-17 00:47)
Brainstorm for the following brain research


Fig.1 Graph theoretical analysis with high-density EEG (in 10Hz). An orthonogalisation method was used to eliminate the signal leakage in the EEG source space.

Short term
1. The EEG based RSNs
2. How different brain areas in the same RSN communicate with each other.
3. The topology of functional connections. Graph theoretical analysis with the EEG in source space. Whether it meets the small-world property.
4. The dynamics of RSNs and functional topology in millisecond (the merits of EEG, compared with fMRI)
5. How the structural topology supports the emergence of fast and flexibly reconfigured functional n
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(2015-02-10 21:02)
标签:

股票

分类: EEG
Microstates are defined by topographies of electric potentials over a multichannel electrode array over scalp, which remain stable for 80-120ms. [1]

我认为这是下一个研究的热点,因为microstate恰恰说明了大脑既是dynamic又是stable。
microstates目前主要是sensor space。
既可以与fMRI结合,探索大脑的resting state network[2,3],又可以做到source space。
应用于大脑工作原理的探索,认知功能和疾病[4,5]检测。



An example (256-channel resting EEG, collected by EGI system)




Reference
[1] Khanna, A., Pascual-Leone, A., Michel, C. M. & Farzan, F. Microstates in resting-state EEG: Current status and future directions. Neuroscience and biobehavioral reviews
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分类: 信号处理
信号处理和模式识别方面的 matlab toolbox



Signal Processing (Top)

Filter Design with Motorola DSP56K
http://www.ee.ryerson.ca:8080/~mzeytin/dfp/index.html

Change Detection and Adaptive Filtering Toolbox

http://www.sigmoid.se/

Signal Processing Toolbox
http://www.mathworks.com/products/signal/

ICATU Toolbox
http://mole.imm.dtu.dk/toolbox/menu.html

Time-Frequency Toolbox for Matlab
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标签:

校园

分类: EEG
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标签:

校园

分类: EEG
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标签:

校园

分类: EEG
之前看到很多文章讲脑电的正问题、逆问题,一堆公式,望而却步。今天Dante花了一个半小时,给我讲了十二篇关于大脑建模的文章,终于是对这个方向有了初步的理解。感谢Dante!

我们采集到的头皮脑电,是由脑袋里面的神经元簇构成信号源,传导到头皮。所以如果要从采集到的头皮脑电推导脑电信号源的位置,就需要把脑电在脑内的投影求出来,这就是逆问题(inverse problem)。目前常用的解决办法有三种:LAURA、LORETA、sLORETA。(see in http://www.uzh.ch/keyinst/loreta.htm)

求解脑电逆问题需要先解决脑电正问题(forward problem),即脑袋里面的信号源发出来的电信号,是怎样传导到头皮的。这就引出了大脑建模的问题,即大脑由什么组织(tissue)组成,什么形状分布,分别为什么电导率。脑电正问题的解决依赖于大脑模型的建立。

最早的脑模型就是把大脑看成是均匀电导率的球体,这当然非常粗糙。后来就讲大脑看成是两层、三层、四层的球体(例如脑髓、脑髓液、头骨和头皮)。但是无论怎样,这样的模型都与人类大脑相去甚远。随着大脑影像技术的发展,我们已经可以用MRI
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