Assessment of Multivariate Neural Time Series by Phase Synchrony Clustering in a Time-Frequency-Topography Representation

<div>Depiction of how an iTFT map (blue background) is constructed. Following the same principle of a grand average for ERP (computing modes instead), the scalp maps in the green TFT map contain the ITCM of epochs <span class="nowrap"><svg height="10.2124pt" id="M142" style="vertical-align:-1.576501pt" version="1.1" viewbox="-0.0498162 -8.6359 56.4048 10.2124" width="56.4048pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"><g transform="matrix(.013,0,0,-0.013,0,0)"><path d="M384 0V27C293 34 287 42 287 114V635C232 613 172 594 109 583V559L157 557C201 555 205 550 205 499V114C205 42 199 34 109 27V0H384Z" id="g113-50"></path></g><g transform="matrix(.013,0,0,-0.013,6.24,0)"><path d="M95 130C70 130 46 113 46 88C46 72 54 64 59 64C93 55 121 33 121 -3C121 -41 93 -68 44 -88L55 -117C117 -98 186 -56 186 22C186 91 131 130 95 130Z" id="g113-45"></path></g><g transform="matrix(.013,0,0,-0.013,11.383,0)"><path d="M412 140C382 77 369 73 315 73H129L270 222C362 320 402 379 402 466C402 571 322 635 234 635C177 635 130 609 99 576L42 495L64 475C90 514 133 568 201 568C274 568 318 519 318 435C318 349 255 267 193 193C144 135 87 78 32 23V0H405C417 45 427 89 440 131L412 140Z" id="g113-51"></path></g><g transform="matrix(.013,0,0,-0.013,17.681,0)"><path d="M95 130C70 130 46 113 46 88C46 72 54 64 59 64C93 55 121 33 121 -3C121 -41 93 -68 44 -88L55 -117C117 -98 186 -56 186 22C186 91 131 130 95 130Z" id="g113-45"></path></g><g transform="matrix(.013,0,0,-0.013,22.824,0)"><path d="M113 -12C146 -12 170 11 170 46C170 78 146 103 114 103S58 78 58 46C58 11 82 -12 113 -12Z" id="g113-47"></path></g><g transform="matrix(.013,0,0,-0.013,27.967,0)"><path d="M113 -12C146 -12 170 11 170 46C170 78 146 103 114 103S58 78 58 46C58 11 82 -12 113 -12Z" id="g113-47"></path></g><g transform="matrix(.013,0,0,-0.013,33.11,0)"><path d="M113 -12C146 -12 170 11 170 46C170 78 146 103 114 103S58 78 58 46C58 11 82 -12 113 -12Z" id="g113-47"></path></g><g transform="matrix(.013,0,0,-0.013,38.287,0)"><path d="M95 130C70 130 46 113 46 88C46 72 54 64 59 64C93 55 121 33 121 -3C121 -41 93 -68 44 -88L55 -117C117 -98 186 -56 186 22C186 91 131 130 95 130Z" id="g113-45"></path></g><g transform="matrix(.013,0,0,-0.013,43.43,0)"><path d="M962 650H795L470 145L347 650H176L170 622C268 613 275 606 244 503L190 322C150 188 132 126 118 91C102 50 80 33 18 28L12 0H245L251 28C175 35 170 48 174 93C177 128 191 180 220 284L292 542H294C331 392 383 150 409 4H432L774 555H776L714 137C700 40 694 34 612 28L606 0H868L874 28C793 34 784 37 797 137L849 533C859 612 863 616 956 622L962 650Z" id="g113-78"></path></g></svg>.</span> For illustration purposes only, let us consider the final step, that is, computing the modes of every window over the discrete time axis, setting <svg height="8.55521pt" id="M143" style="vertical-align:-0.2063904pt" version="1.1" viewbox="-0.0498162 -8.34882 27.5497 8.55521" width="27.5497pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"><g transform="matrix(.013,0,0,-0.013,0,0)"><path d="M457 332C457 400 429 448 397 448C384 448 371 441 365 435L366 428C376 413 388 372 388 315C388 152 306 45 220 45C166 45 134 85 158 180L197 332C204 361 207 385 207 402C207 434 196 448 174 448C125 448 66 406 23 342L43 319C84 368 110 383 121 383C130 383 129 374 122 343L76 156C71 137 69 120 69 105C69 25 125 -12 181 -12C314 -12 457 166 457 332Z" id="g113-242"></path></g><g transform="matrix(.013,0,0,-0.013,9.873,0)"><path d="M535 323V373H52V323H535ZM535 138V188H52V138H535Z" id="g117-34"></path></g><g transform="matrix(.013,0,0,-0.013,21.136,0)"><path d="M412 140C382 77 369 73 315 73H129L270 222C362 320 402 379 402 466C402 571 322 635 234 635C177 635 130 609 99 576L42 495L64 475C90 514 133 568 201 568C274 568 318 519 318 435C318 349 255 267 193 193C144 135 87 78 32 23V0H405C417 45 427 89 440 131L412 140Z" id="g113-51"></path></g></svg> samples (indicated with the red rectangles and arrows) with a sampling rate of 1 kHz. Thereby, each topographic map in the iTFT map represents the cluster modes of all samples of the array containing the ITCM within the window of size <svg height="6.1673pt" id="M144" style="vertical-align:-0.2063904pt" version="1.1" viewbox="-0.0498162 -5.96091 6.36301 6.1673" width="6.36301pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"><g transform="matrix(.013,0,0,-0.013,0,0)"><path d="M457 332C457 400 429 448 397 448C384 448 371 441 365 435L366 428C376 413 388 372 388 315C388 152 306 45 220 45C166 45 134 85 158 180L197 332C204 361 207 385 207 402C207 434 196 448 174 448C125 448 66 406 23 342L43 319C84 368 110 383 121 383C130 383 129 374 122 343L76 156C71 137 69 120 69 105C69 25 125 -12 181 -12C314 -12 457 166 457 332Z" id="g113-242"></path></g></svg> for each electrode.</div>

Computational Intelligence and Neuroscience

fig3

Figure 3

Figure 3: Assessment of Multivariate Neural Time Series by Phase Synchrony Clustering in a Time-Frequency-Topography Representation 