Eric S Luth, Peter Juo
A Versatile Semester-Long Course-Based Undergraduate Research Experience using Optogenetics and RNAi to Identify Genes Important for Synapse Function
Journal of undergraduate neuroscience education : JUNE : a publication of FUN, Faculty for Undergraduate Neuroscience
aid brain computer interface bci users communicate receive signals wirelessly wired connections operate smart devices bci classifiers architecture quite difficult since numerous elements combined elements made brain signals also include high levels weak sounds could provide reliable participant recordings daily activities must use computer vision techniques create model order control information high dimension volume signals present classification classifier primary obstacles due extracted classified brain activity study also presented novel hierarchical recursive feature elimination method refer hrfe embracing noisy additions hrfe makes variety categorization suggestions eliminate bias classifying bci systems different types put hrfe test two bci signal data setsspecifically dataset bci contests iiiusing shallow deep convolution network classification techniques grid assets used create electrocorticography ecog signals contralateral right motor cortex signals recorded bci contests iii database using ecog signals choose top 20 features biggest effects distortion classification selection simulation findings show hrfe significant computer vision enhancement compared comparable feature selection methods literature particularly ecog signal achieves 93 reliability