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