Classification of Two Class Motor Imagery Tasks Using Hybrid GA-PSO Based <svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" style="vertical-align:-0.06579971pt" id="M1" height="11.4135pt" version="1.1" viewBox="-0.0657574 -11.3477 13.2565 11.4135" width="13.2565pt"><g transform="matrix(.017,0,0,-0.017,0,0)"><path id="g50-76" d="M771 650H519L511 619L537 615C578 610 579 600 539 564C444 476 379 428 336 396C312 378 281 357 252 347L283 514C300 605 304 610 388 619L395 650H134L127 619C212 610 216 605 199 514L130 140C112 41 103 38 28 31L18 0H281L288 31C201 40 198 41 216 140L246 311C274 328 289 324 315 284C393 167 458 86 528 0H686L695 31C635 37 612 45 570 97C521 156 420 285 361 367L596 549C663 601 684 609 764 620L771 650Z"/></g></svg>-Means Clustering

<div>Recording setup for the BCI experiment.</div>

Computational Intelligence and Neuroscience

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Figure 1

Figure 1: Classification of Two Class Motor Imagery Tasks Using Hybrid GA-PSO Based <svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" style="vertical-align:-0.06579971pt" id="M1" height="11.4135pt" version="1.1" viewBox="-0.0657574 -11.3477 13.2565 11.4135" width="13.2565pt"><g transform="matrix(.017,0,0,-0.017,0,0)"><path id="g50-76" d="M771 650H519L511 619L537 615C578 610 579 600 539 564C444 476 379 428 336 396C312 378 281 357 252 347L283 514C300 605 304 610 388 619L395 650H134L127 619C212 610 216 605 199 514L130 140C112 41 103 38 28 31L18 0H281L288 31C201 40 198 41 216 140L246 311C274 328 289 324 315 284C393 167 458 86 528 0H686L695 31C635 37 612 45 570 97C521 156 420 285 361 367L596 549C663 601 684 609 764 620L771 650Z"/></g></svg>-Means Clustering 

Computational Intelligence and Neuroscience

Classification of Two Class Motor Imagery Tasks Using Hybrid GA-PSO Based -Means Clustering

Figure 1