Exercises Part 4 | Analyzing Neural Time Series Data
Exercises 22 | Surface Laplacian
Based on the topographical maps of ERPs in figure 22.6 (plate 12) , select one electrode whose activity you think might look similar before and after computing the surface Laplacian, and one electrode whose activity you think might look different before and after the surface Laplacian.
(1) look similar before and after computing the surface Laplacian: FC1
(2) look different before and after the surface Laplacian: PO8
Perform a time-frequency decomposition of t ...
Part 4 Connectivity | 功能连接分析
Chap.25 Introduction to the Various Connectivity Analyses
Connectivity:指在同一时刻下考虑多个信号的分析。
需区分的概念:
functional connectivity:linear or nonlinear covariation between fluctuations in activity recorded from distinct neural networks. 更接近于相关性(correlation)
effective connectivity:a causal influence of activity in one neural network over activity in another neural network. 更接近于因果关系(causation)
两个电极之间的连接性(connectivity)可以反映不同大脑区域之间的真实连接,也可能是由于这两个电极测量了来自同一脑源的活动。
Chap.26 Phase-Based ConnectivityISPC (Intersi ...
Exercises 5 | Connectivity
26 | Phase-Based Connectivity
Select one seed electrode and one frequency band and compute phase-based connectivity between that seed electrode and every other electrode. Use two methods for phase-based connectivity that were presented in this chapter, one that is volume conduction independent (e.g., PLI) and one that could produce spurious connectivity due to volume conduction (e.g., ISPC). Do not apply a baseline subtraction. Make topographical plots of seeded connectivity in a time window of ...
Part 4 Spatial Filters | Analyzing Neural Time Series Data
Chapter 22 Surface Laplacian概述
采用Surface Laplacian可以突出局部的空间特征,削弱空间上广泛分布的活动
“Surface Laplacian” 和 “Current Scalp Dencity” 的区别
Surface Laplacian 只是一种空间滤波的方法
Current Scalp Dencity 强调滤波后的结果
Surface Laplacian只能用于EEG(不能用于MEG),且通常用于电极数目大于64的EEG数据
The Laplacian is more sensitive to radial dipoles than it is to tangential dipoles.
Activity seen in the surface Laplacian is dominated (although not necessarily entirely driven) by radial dipoles in regions of the cortex close to the skull (such as gyral c ...
Exercises Part 3 | Analyzing Neural Time Series Data
Exercises 10 | Convolution10.6.1
Create two kernels for convolution: one that looks like a U and one that looks like a decay function. There is no need to be too sophisticated in generating, for example, a Gaussian and an exponential; numerical approximations are fine.
%% Exercises 10% 1. Create two kernels kernel_U = [1 0.8 0.3 0.1 0 0.1 0.3 0.8 1];kernel_decay = [1 0.9 0.8 0.7 0.5 0.3 0.2 0.1 0];
10.6.2
Convolve these two kernels with 50 time points of EEG data from one electrode. Make a plot ...
Part 3 Frequency and Time-Frequency Domains Analyses | Analyzing Neural Time Series Data
Chapter 10&11推荐3b1b的一段关于卷积、离散傅里叶变换(DFT)和快速傅里叶变换(FFT)的视频
【官方双语】那么……什么是卷积?
Supplementary Code for Figure 11.5对于书中所提供的图11.5对应代码的一些细节注释
%% Figure 11.5N = 10; % length of sequence 长度为10的向量data = randn(1,N); % random numberssrate = 200; % sampling rate in Hz 采样率200Hznyquist = srate/2; % Nyquist frequency -- the highest frequency you can measure in the data% initialize Fourier output matrixfourier = zeros(size(data)); % These are the actual frequencies in Hz that will ...
Exercises Part 2 | Analyzing Neural Time Series Data
9.8 Exercise9.8.1Compute the ERP at each electrode. Select five time points at which to show topographical plots (e.g., 0 to 400 ms in 100-ms steps). In one figure, make a series of topographical plots at these time points. To increase the signal-to-noise ratio, make each plot show the average of activity from 20 ms before until 20 ms after each time point. For example, the topographical plot from 200 ms should show average activity from 180 ms until 220 ms. Indicate the center time point in ...
Exercises Part 1 | Analyzing Neural Time Series Data
4.7 Exercise4.7.1 Exercises for Script A
Create a 4 × 8 matrix of randomly generated numbers.
Loop through all rows and columns, and test whether each element is greater than 0.5.
Report the results of the test along with the value of the matrix element and its row-column position. For example, your Matlab script should print The 3rd row and 8th column has a value of 0.42345 and is not bigger than 0.5 .
Make sure to add exceptions to print out 1st, 2nd, and 3rd, instead of 1th, 2th, and 3t ...
Part 2 Preprocessing and Time-Domain Analyses | Analyzing Neural Time Series Data
Chapter 7 数据预处理The Balance between Signal and Noise | 信号与噪声的取舍平衡保留更多的信号通常也意味着保留更多的噪声。
某些数据是信号还是噪声取决于实验的目的。
Creating Epochs提取epoch的过程可以认为是将一段完整的数据划分成不同的小段
1. 如何选择“time=0”的时刻
对于大多数实验,选择刺激开始的时刻作为time=0
对于其他情况,如有多个刺激时,可选择第一个刺激或最关键的刺激开始的时刻作为time=0
time-lock(time=0时刻的选择)是可以根据需要调节变化的
2. time=0前后需要包含多长时间对于ERPs分析,划分的小段只需要包含你想要分析的时间段加上baseline,例如相对于0时刻的-200ms到800ms。
对于时频分析,需要划分更长的时间段,以避免边缘伪影(edge artifacts),即需要预留足够长的缓冲区,使边缘伪影消退。此外,提取的频带越小,需要预留的缓冲区也应该越长。在时频功率谱中,边缘伪影很容易识别,因此可以先试分析一组数据再确定需要预留的缓冲区长度。通常情况下,将 ...
Part 1 Introduction | Analyzing Neural Time Series Data
2-D topographical locations of EEG electrodes
Brain rhythm frequency bands
delta (2-4 HZ)
theta (4-8 Hz)
alpha (8-12 Hz)
beta (15-30 Hz)
lower gamma (30-80 Hz)
upper gamma (80-150 Hz)
subdelta and omega (up to 600 HZ)
Intra- and Intertrial Timing
*Intratrial*:单个trail内。
*Intertial*: 不同trial之间。the duration of time between the end of one trial and the start of the next trial
Phase-Locked and Nonphase-Locked