1. Урок 1.00:08:01
    Broad introduction to neural time series analysis
  2. Урок 2.00:15:58
    Neural data science as source sepatation
  3. Урок 3.00:11:59
    What to expect from this course
  4. Урок 4.00:02:31
    A quick note about how this went from 2 to 1 course
  5. Урок 5.00:21:37
    Origin, significance, and interpretation of EEG
  6. Урок 6.00:13:40
    Overview of possible preprocessing steps
  7. Урок 7.00:19:02
    ICA for data cleaning
  8. Урок 8.00:15:49
    Signal artifacts (not) to worry about
  9. Урок 9.00:12:28
    Topographical mapping
  10. Урок 10.00:16:20
    Overview of time-domain analyses (ERPs)
  11. Урок 11.00:16:40
    Motivations for rhythm-based analyses
  12. Урок 12.00:16:12
    Interpreting time-frequency plots
  13. Урок 13.00:04:27
    The empirical datasets used in this course
  14. Урок 14.00:19:32
    MATLAB: EEG dataset
  15. Урок 15.00:08:15
    MATLAB: V1 dataset
  16. Урок 16.00:04:11
    Where to get more EEG data?
  17. Урок 17.00:21:58
    Simulating data to understand analysis methods
  18. Урок 18.00:04:09
    Problem set: introduction and explanation
  19. Урок 19.00:35:19
    Problem set (1/2): Simulating and visualizing data
  20. Урок 20.00:36:19
    Problem set (2/2): Simulating and visualizing data
  21. Урок 21.00:08:27
    Why simulate data?
  22. Урок 22.00:12:25
    Generating white and pink noise
  23. Урок 23.00:23:02
    The three important equations (sine, Gaussian, Euler's)
  24. Урок 24.00:06:42
    Generating "chirps" (frequency-modulated signals)
  25. Урок 25.00:05:28
    Non-stationary narrowband activity via filtered noise
  26. Урок 26.00:07:23
    Transient oscillation
  27. Урок 27.00:12:59
    The eeglab EEG structure
  28. Урок 28.00:09:31
    Project 1-1: Channel-level EEG data
  29. Урок 29.00:11:48
    Project 1-1: Solutions
  30. Урок 30.00:09:06
    Projecting dipoles onto EEG electrodes
  31. Урок 31.00:04:51
    Project 1-2: dipole-level EEG data
  32. Урок 32.00:10:47
    Project 1-2: Solutions
  33. Урок 33.00:17:43
    Event-related potential (ERP)
  34. Урок 34.00:16:56
    Lowpass filter an ERP
  35. Урок 35.00:07:10
    Compute the average reference
  36. Урок 36.00:06:12
    Butterfly plot and topo-variance time series
  37. Урок 37.00:12:58
    Topography time series
  38. Урок 38.00:09:38
    Simulate ERPs from two dipoles
  39. Урок 39.00:07:08
    Project 2-1: Quantify the ERP as peak-mean or peak-to-peak
  40. Урок 40.00:15:20
    Project 2-1: Solutions
  41. Урок 41.00:02:31
    Project 2-2: ERP peak latency topoplot
  42. Урок 42.00:08:09
    Project 2-2: Solutions
  43. Урок 43.00:09:43
    Time and frequency domains
  44. Урок 44.00:08:06
    Sine waves
  45. Урок 45.00:09:26
    MATLAB: Sine waves and their parameters
  46. Урок 46.00:14:24
    Complex numbers
  47. Урок 47.00:12:04
    Euler's formula
  48. Урок 48.00:12:25
    MATLAB: Complex numbers and Euler's formula
  49. Урок 49.00:09:41
    The dot product
  50. Урок 50.00:10:38
    MATLAB: Dot product and sine waves
  51. Урок 51.00:04:52
    Complex sine waves
  52. Урок 52.00:06:33
    MATLAB: Complex sine waves
  53. Урок 53.00:07:13
    The complex dot product
  54. Урок 54.00:13:55
    MATLAB: The complex dot product
  55. Урок 55.00:12:31
    Fourier coefficients
  56. Урок 56.00:15:23
    MATLAB: The discrete-time Fourier transform
  57. Урок 57.00:16:53
    MATLAB: Fourier coefficients as complex numbers
  58. Урок 58.00:12:23
    Frequencies in the Fourier transform
  59. Урок 59.00:14:34
    Positive and negative frequencies
  60. Урок 60.00:08:34
    Accurate scaling of Fourier coefficients
  61. Урок 61.00:17:28
    MATLAB: Positive/negative spectrum; amplitude scaling
  62. Урок 62.00:14:51
    MATLAB: Spectral analysis of resting-state EEG
  63. Урок 63.00:18:52
    MATLAB: Quantify alpha power over the scalp
  64. Урок 64.00:09:46
    The perfection of the Fourier transform
  65. Урок 65.00:06:52
    The inverse Fourier transform
  66. Урок 66.00:10:06
    MATLAB: Reconstruct a signal via inverse FFT
  67. Урок 67.00:10:15
    Frequency resolution and zero-padding
  68. Урок 68.00:16:39
    MATLAB: Frequency resolution and zero-padding
  69. Урок 69.00:08:06
    Estimation errors and Fourier coefficients
  70. Урок 70.00:13:07
    Signal nonstationarities
  71. Урок 71.00:10:32
    MATLAB: Examples of sharp nonstationarities on power spectra
  72. Урок 72.00:17:11
    MATLAB: Examples of smooth nonstationarities on power spectra
  73. Урок 73.00:11:14
    Welch's method for smooth spectral decomposition
  74. Урок 74.00:11:48
    MATLAB: Welch's method on phase-slip data
  75. Урок 75.00:06:28
    MATLAB: Welch's method on resting-state EEG data
  76. Урок 76.00:05:37
    MATLAB: Welch's method on V1 dataset
  77. Урок 77.00:33:01
    Problem set (1/2): Spectral analyses of real and simulated data
  78. Урок 78.00:37:10
    Problem set (2/2): Spectral analyses of real and simulated data
  79. Урок 79.00:04:19
    Program the Fourier transform from scratch!
  80. Урок 80.00:05:52
    Program the inverse Fourier transform from scratch!
  81. Урок 81.00:06:01
    Spectral separation on simulated dipole data
  82. Урок 82.00:12:38
    FFT of stationary and non-stationary simulated data
  83. Урок 83.00:12:01
    FFT and Welch's method on EEG resting state data
  84. Урок 84.00:16:51
    To taper or not to taper?
  85. Урок 85.00:04:54
    Extracting average power from a frequency band
  86. Урок 86.00:11:40
    Comparing average spectra vs. spectra of an average
  87. Урок 87.00:04:23
    Project 3-1: Topography of spectrally separated activity
  88. Урок 88.00:11:56
    Project 3-1: Solutions
  89. Урок 89.00:03:52
    Project 3-2: Topography of alpha-theta ratio
  90. Урок 90.00:11:22
    Project 3-2: Solutions
  91. Урок 91.00:17:48
    Morlet wavelets in time and in frequency
  92. Урок 92.00:14:10
    MATLAB: Getting to know Morlet wavelets
  93. Урок 93.00:23:37
    Convolution in the time domain
  94. Урок 94.00:14:21
    MATLAB: Time-domain convolution
  95. Урок 95.00:19:30
    Convolution as spectral multiplication
  96. Урок 96.00:08:29
    MATLAB: The five steps of convolution
  97. Урок 97.00:12:57
    MATLAB: Convolve real data with a Gaussian
  98. Урок 98.00:08:33
    MATLAB: Complex Morlet wavelets
  99. Урок 99.00:12:44
    Complex Morlet wavelet convolution
  100. Урок 100.00:07:55
    Convolution coding tips
  101. Урок 101.00:19:03
    MATLAB: Complex Morlet wavelet convolution
  102. Урок 102.00:08:27
    MATLAB: Convolution with all trials!
  103. Урок 103.00:09:51
    MATLAB: A full time-frequency power plot!
  104. Урок 104.00:13:07
    Averaging phase values
  105. Урок 105.00:15:17
    Inter-trial phase clustering (ITPC/ITC)
  106. Урок 106.00:13:19
    MATLAB: ITPC
  107. Урок 107.00:18:19
    Parameters of Morlet wavelet (time-frequency trade-off)
  108. Урок 108.00:18:56
    MATLAB: Time-frequency trade-off
  109. Урок 109.00:05:30
    The stationarity assumption of wavelet convolution
  110. Урок 110.00:14:57
    The "1/f" structure of spectral brain dynamics
  111. Урок 111.00:18:35
    Baseline normalization of time-frequency power
  112. Урок 112.00:13:43
    MATLAB: Baseline normalization of TF plots
  113. Урок 113.00:11:29
    Scale-free dynamics via detrended fluctuation analysis (DFA)
  114. Урок 114.00:21:19
    MATLAB: detrended fluctuation analysis
  115. Урок 115.00:23:07
    The filter-Hilbert time-frequency method
  116. Урок 116.00:17:28
    MATLAB: Filter-Hilbert
  117. Урок 117.00:07:34
    The short-time Fourier transform (STFFT)
  118. Урок 118.00:07:41
    MATLAB: STFFT
  119. Урок 119.00:13:23
    Comparing wavelet, filter-Hilbert, and STFFT
  120. Урок 120.00:11:04
    The multi-taper method
  121. Урок 121.00:16:17
    Within-subject, cross-trial regression
  122. Урок 122.00:23:42
    MATLAB: Cross-trial regression
  123. Урок 123.00:09:25
    Temporal resolution vs. precision, pre- and post-convolution
  124. Урок 124.00:17:07
    MATLAB: Downsampling time-frequency results
  125. Урок 125.00:13:46
    MATLAB: Linear vs. logarithmic frequency scaling
  126. Урок 126.00:12:24
    Separating phase-locked and non-phase-locked activity
  127. Урок 127.00:19:28
    MATLAB: Total, non-phase-locked, and phase-locked power
  128. Урок 128.00:09:42
    Edge effects, buffer zones, and data epoch length
  129. Урок 129.00:29:19
    Problem set (1/3): Time-frequency analysis
  130. Урок 130.00:21:19
    Problem set (2/3): Time-frequency analysis
  131. Урок 131.00:35:00
    Problem set (2/3): Time-frequency analysis
  132. Урок 132.00:09:58
    Create a family of complex Morlet wavelets
  133. Урок 133.00:12:40
    Create a time-frequency plot of a nonlinear chirp
  134. Урок 134.00:09:58
    Compare wavelet-derived spectrum and FFT
  135. Урок 135.00:11:25
    Wavelet convolution of close frequencies
  136. Урок 136.00:07:52
    Time-frequency power of multitrial EEG activity
  137. Урок 137.00:11:06
    Baseline normalize power with dB and % change
  138. Урок 138.00:11:02
    Exploring wavelet parameters in real data
  139. Урок 139.00:10:39
    Exploring wavelet parameters in simulated data
  140. Урок 140.00:09:45
    Inter-trial phase clustering before vs. after removing ERP
  141. Урок 141.00:07:38
    Downsampling time-frequency power
  142. Урок 142.00:06:56
    Visualize time-frequency power from all channels
  143. Урок 143.00:12:42
    Instantaneous frequency in simulated data
  144. Урок 144.00:08:19
    Instantaneous frequency in real data
  145. Урок 145.00:04:38
    Project 4-1: Phase-locked, non-phase-locked, and total power
  146. Урок 146.00:13:53
    Project 4-1: Solutions
  147. Урок 147.00:12:21
    Narrowband filtering and the Hilbert transform
  148. Урок 148.00:03:02
    Project 4-2: Time-frequency power plot via filter-Hilbert
  149. Урок 149.00:07:38
    Project 4-2: Solutions
  150. Урок 150.00:16:12
    Four things to keep in mind about connectivity
  151. Урок 151.00:10:30
    Volume conduction and what to do about it
  152. Урок 152.00:13:13
    Intuition about phase synchronization
  153. Урок 153.00:09:42
    Inter-site phase clustering (ISPC)
  154. Урок 154.00:17:32
    MATLAB: ISPC
  155. Урок 155.00:11:03
    Surface Laplacian for connectivity analyses
  156. Урок 156.00:10:20
    MATLAB: Laplacian in simulated data
  157. Урок 157.00:14:45
    MATLAB: Laplacian in real EEG data
  158. Урок 158.00:12:14
    Phase-lag-based connectivity
  159. Урок 159.00:14:40
    MATLAB: phase-lag index
  160. Урок 160.00:17:49
    When to use phase-lag vs. phase-clustering measures
  161. Урок 161.00:18:59
    MATLAB: Phase synchronization in voltage and Laplacian data
  162. Урок 162.00:07:17
    Connectivity over time vs. over trials
  163. Урок 163.00:07:43
    MATLAB: Connectivity over time vs. over trials
  164. Урок 164.00:17:38
    MATLAB: Simulating data to test connectivity methods
  165. Урок 165.00:10:20
    Two methods of power-based connectivity
  166. Урок 166.00:32:18
    Granger causality (prediction)
  167. Урок 167.00:22:37
    MATLAB: Granger causality
  168. Урок 168.00:12:58
    "Hubness" from graph theory
  169. Урок 169.00:23:00
    MATLAB: Connectivity hubs
  170. Урок 170.00:06:20
    When to use which connectivity method?
  171. Урок 171.00:22:30
    Problem set (1/2): Pairwise synchronization
  172. Урок 172.00:32:08
    Problem set (2/2): Pairwise synchronization
  173. Урок 173.00:12:18
    Synchronization in simulated noisy oscillators
  174. Урок 174.00:12:05
    Spurious connectivity in narrowband noise
  175. Урок 175.00:15:41
    Phase synchronization matrices in multitrial data
  176. Урок 176.00:17:11
    Power time series correlations
  177. Урок 177.00:11:14
    Power correlations over trials
  178. Урок 178.00:10:43
    Scalp Laplacian for electrode-level connectivity
  179. Урок 179.00:14:04
    All-to-all synchronization and "hubness" (graph theory)
  180. Урок 180.00:14:46
    Phase-lag index
  181. Урок 181.00:05:09
    Project 5-1: ISPC and PLI, with and without Laplacian
  182. Урок 182.00:05:32
    Project 5-1: Solutions
  183. Урок 183.00:03:51
    Project 5-2: Seeded phase vs. power coupling
  184. Урок 184.00:07:37
    Project 5-2: Solutions
  185. Урок 185.00:15:30
    Introduction: The basis of statistics, necessity, and levels
  186. Урок 186.00:14:34
    Parametric vs. nonparametric statistics
  187. Урок 187.00:25:59
    Permutation-based statistics
  188. Урок 188.00:20:03
    MATLAB: Permutation testing and shuffling
  189. Урок 189.00:23:58
    MATLAB: Permutation testing in real data
  190. Урок 190.00:08:35
    Multiple comparisons and limitations of Bonferroni method
  191. Урок 191.00:10:52
    Cluster-based multiple comparisons correction
  192. Урок 192.00:12:42
    MATLAB: Cluster correction
  193. Урок 193.00:09:49
    Extreme pixel-based multiple comparisons correction
  194. Урок 194.00:14:04
    MATLAB: Extreme pixel correction
  195. Урок 195.00:05:32
    Illustrating statistical significance in plots
  196. Урок 196.00:10:43
    Subject- vs. group-level analyses
  197. Урок 197.00:06:07
    Error bars and guessing significance
  198. Урок 198.00:18:37
    Three approaches for group-level statistics
  199. Урок 199.00:15:00
    MATLAB: Extracting features for group analyses
  200. Урок 200.00:14:00
    Circular inference ("double-dipping")
  201. Урок 201.00:19:52
    Permutation testing for one variable and two groups
  202. Урок 202.00:08:22
    Meta-permutation test for increased stability
  203. Урок 203.00:18:35
    Permutation testing in simulated time series
  204. Урок 204.00:13:56
    Permutation testing for cluster correction in simulated data
  205. Урок 205.00:13:20
    Permutation testing and cluster correction in real EEG data
  206. Урок 206.00:05:06
    Project 7-1: Effects of noise smoothness on cluster correction
  207. Урок 207.00:15:31
    Project 7-1: Solutions
  208. Урок 208.00:08:11
    Project 7-2: Simulate time-frequency data for statistical testing
  209. Урок 209.00:14:02
    Project 7-2: Solutions
  210. Урок 210.00:01:18
    Background knowledge for this section
  211. Урок 211.00:10:10
    Simulate multicomponent EEG data
  212. Урок 212.00:15:59
    Create covariance matrices based on time and on frequency
  213. Урок 213.00:11:16
    Principal components analysis (PCA) of simulated data
  214. Урок 214.00:10:58
    Time-based GED for source-separation in simulated data
  215. Урок 215.00:09:27
    Frequency-based GED for source-separation in simulated data
  216. Урок 216.00:10:48
    Project 6-1: GED for interacting alpha sources
  217. Урок 217.00:08:53
    Project 6-1: Solutions