P300 Detection Based on EEG Shape Features

<div>Detailed accuracy <svg height="10.2124pt" id="M338" style="vertical-align:-3.42943pt" version="1.1" viewbox="-0.0498162 -6.78297 8.63352 10.2124" width="8.63352pt" 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="M642 419L635 448C586 435 552 411 532 392C518 379 509 351 496 304C484 260 462 196 445 150C424 93 388 40 314 30L413 508L406 510L344 491L257 36C205 46 175 84 175 169C175 195 180 241 185 277C189 304 190 331 190 358C190 413 175 448 141 448C111 448 69 429 23 373L30 347C51 365 68 375 81 375C93 375 108 368 108 327C108 307 104 268 101 239C98 211 95 180 95 155C95 33 159 -8 248 -14L213 -186C206 -220 221 -254 230 -261L261 -230L306 -11C339 -4 366 6 389 20C431 46 473 87 503 155C533 224 557 286 571 325C586 367 606 393 642 419Z" id="g113-245"></path><glyph.data ascent="3473" descent="-2876" horiz-adv-x="654" vert-adv-y="654"></glyph.data></g></svg> of classifiers SVM and SWLDA for the electrode 1 of Figure <a href="../fig8/">8</a> by using our shape-feature vector.</div>

Computational and Mathematical Methods in Medicine

P300 Detection Based on EEG Shape Features

Figure 9