nt_banner | h=nt_banner(text,varargin) - annotate with text at head of figure |
nt_bias_cluster | [c0,c1,A,todss,pwr0,pwr1]=nt_bias_cluster(x,dsr,flags) - cluster covariance |
nt_bias_fft | [c0,c1]=nt_bias_fft(x,freq,nfft) - covariance with and w/o filter bias |
nt_bias_filter | [c0,c1]=nt_bias_filter(x,B,A) - covariance with and w/o filter bias |
nt_bias_mask | [c0,c1]=bias_hi_amp(x,mask) - covariance of masked signal |
nt_bsmean | [mn,sd,all]=nt_bsmean(x,N) - calculate mean, estimate sd using bootstrap |
nt_bsmean_diff | [mn,sd]=nt_bsmean_diff(x1,x2,N) - calculate mean, estimate sd using bootstrap |
nt_bsplot | nt_bsplot(x,sds,style,abscissa,zeroflag,rmsflag) - plot average with bootstrap standard deviation |
nt_bsplot2 | nt_bsplot(x,percentile,style,abscissa,zeroflag,rmsflag) - plot average with confidence interval |
nt_bsplot_diff | nt_bsplot_diff(x,y,sds,style,abscissa,zeroflag,rmsflag) - plot average difference with bootstrap standard deviation |
nt_bsrms | [rms,sd,all]=nt_bsrms(x,N) - calculate rms, estimate sd using bootstrap |
nt_bsrmsmean | [r,sd,all]=ft_bsrmsmean(x,N) - rms over channels of mean over trials, estimate sd using bootstrap |
nt_cluster_jd | [A,todss]=nt_cluster_jd(x,dsr,flags) - cluster with joint diagonalization |
nt_cov | [c,tw]=nt_cov(x,shifts,w) - time shift covariance |
nt_dataview | [p,data]=nt_dataview(data,p) - view data sets |
nt_decimate | y=nt_decimate(x,R) - apply matlab decimate function to columns of matrix |
nt_demean | [y,mn]=nt_demean(x,w) - remove weighted mean over cols |
nt_demean2 | y=nt_demean2(x,w) - remove mean of each row and page |
nt_detrend | y=nt_detrend(x,order,w,basis) - remove polynomial or sinusoidal trend |
nt_dft_filter | y=nt_dft_filter(x,transfer,N) - apply filter using DFT |
nt_dsample | y=nt_dsample(x,factor,resampel_flag) - downsample by averaging neighboring samples |
nt_dss0 | [todss,pwr1,pwr2]=nt_dss0(c0,c1,keep1,keep2) - dss from covariance |
nt_dss1 | [todss,pwr0,pwr1]=nt_dss1(x,w,keep1,keep2,sns_flag) - evoked-biased DSS denoising |
nt_enforce_stationarity | y=nt_enforce_stationarity(x,DSR,thresh) - locally project out non-stationary components |
nt_epochify | y=nt_epochify(x,tidx,bounds) - extract epochs based on trigger indices |
nt_filter_peak | [B,A] = nt_filter_peak(Wo,Q) - second order resonator filter |
nt_find_clipped | w=nt_find_clipped_trials(x,bounds) - find clipped trials |
nt_find_clipped_trials | |
nt_find_outlier_trials | [idx,d,mn,idx_unsorted]=nt_find_outlier_trials(x,criterion,plot,regress_flag) - find outlier trials |
nt_find_outlier_trials2 | [idx,d,mn,idx_unsorted]=nt_find_outlier_trials(x,criterion,mn,regress_flag) - find outlier trials |
nt_find_outlier_trials3 | [idx,d]=nt_find_outlier_trials3(x,criterion,norm_flag) - find outlier trials |
nt_find_outliers | w=nt_find_outliers(x,toobig1,toobig2) - find outliers (glitches, etc.). |
nt_find_outliers2 | w=nt_find_outliers2(x,cutoff) - outliers based on mahalanobis distance |
nt_find_triggers | tidx=nt_find_triggers(x,threshold,type,guard) - find triggers |
nt_fixsign | y=nt_fixsign(x) - flip signs to maximize inter-component correlation |
nt_fold | y=fold(x,epochsize) - fold 2D to 3D |
nt_greetings | nt_greetings - display message the first time the toolbox is used |
nt_growmask | ww=nt_growmask(w,margin) - widen mask |
nt_hi_amp | y=nt_hi_amp(x) - decompose into high-amplitude components |
nt_imagescc | imagescc - plot image with symmetric scaling |
nt_kurtosis | [todss,K]=nt_kurtosis(x,nIterations,exponent,w,smooth)- find high-kurtosis components |
nt_linecolors | nt_colorlines(h,permutation) - apply different colors to lines of plot |
nt_linestyles | nt_stylelines(h,property,values) - apply different styles to lines of plot |
nt_lower_to_full | b=nt_lower_to_full(a,ind) - transform lower diagonal to full covariance |
nt_mark | nt_mark(idx,labels) |
nt_mat2trial | [y]=nt_trial2mat(x) - convert 3D matrix to trial cell array |
nt_mmat | y=nt_mmat(x,m) - matrix multiplication (with convolution) |
nt_mmx | [y,abscissa]=mmx(x, N) - calculate min-max pairs |
nt_multiscale | z=nt_multiscale(x,depth) - apply smoothing at multiple scales |
nt_multishift | z=nt_multishift(x,shifts,amplitudes) - apply multiple shifts to matrix |
nt_multishift2 | z=nt_multishift2(x,nshifts) - apply multiple shifts/smoothing to matrix |
nt_narrowband_scan | A=nt_narrowband_scan(x,freqs,sr,Q,plotflag) - scan for narrowband components using DSS |
nt_normcol | [y,norm]=nt_normcol(x,w) - normalize each column so its weighted msq is 1 |
nt_normpage | y=nt_normpage(x,w) - normalize each page so its weighted msq is 1 |
nt_normpagecol | y=nt_normpagecol(x,w) - normalize each column of each page so its weighted msq is 1 |
nt_normrow | y=nt_normcol(x) - normalize each row so its msq is 1 |
nt_pca | [z,idx]=nt_pca(x,shifts,nkeep,threshold,w) - time-shift pca |
nt_pca0 | [topcs,pwr]=nt_pca0(x,shifts,nkeep,threshold,w) - time-shift pca |
nt_pca_kmeans | [topcs,pwr]=nt_pca_kmeans(x,nkeep) - PCA preceded by kmeans for speed |
nt_pca_nodemean | [z,idx]=nt_pca_nodemean(x,shifts,keep,threshold,w) - time-shift pca |
nt_pcarot | [topcs,eigenvalues]=pcarot(cov,N) - PCA matrix from covariance |
nt_peaksign | sgn=peaksign(x,dim) - sign of largest extremum |
nt_peaky | [tocomps,ii]=nt_peaky(c,x,T,nSmooth) - find components that maximize peakiness |
nt_qca | [squares,quads,D]=nt_qca(x,npcs,nsmooth,nquads) - maximize induced power using quadratic component analysis |
nt_qca0 | [tosquares,quads,D]=nt_qca0(x,npcs,nsmooth,nquads) - maximize induced power using quadratic component analysis |
nt_qca02 | [tosquare,quad,tosquare2,quad2,D]=nt_qca02(x,npcs,nsmooth) - maximize induced power using quadratic component analysis |
nt_qca2 | [square,quad,square2,quad2,D]=nt_qca(x,npcs,nsmooth) - maximize induced power using quadratic component analysis |
nt_qpca | [squares,quads]=nt_qpca(x,npcs,nsmooth,nquads) - quadratic PCA |
nt_qpca0 | [tosquares,quads,D]=nt_qpca0(x,npcs,nsmooth,nquads) - quadratic PCA |
nt_quad2square | [tosquare,D]=nt_quad2square(toquad,order) - quadratic to squared linear component |
nt_regcov | r=nt_regcov(cxy,cyy,keep,threshold) - regression matrix from cross covariance |
nt_repeatability | [score]=nt_repeatability(x,demean_flag) - repeatability score |
nt_resample | RESAMPLE Change the sampling rate of a signal. |
nt_rms | RMS - Root-mean-square |
nt_sgram | [s,f,t]=nt_sgram(x,window,noverlap,nfft,sr,flags) - spectrogram |
nt_smooth | y=nt_smooth(x,T,nIterations,nodelayflag) - smooth by convolution with square window |
nt_sns | y=nt_sns(x,nneigbors,skip,w) - sensor noise suppression |
nt_sns0 | r=nt_sns0(c,nneigbors,skip,wc) - sensor noise suppression |
nt_sns1 | y=nt_sns1(x,nneigbors,skip,w,threshold) - sensor noise suppression |
nt_spect_plot | nt_spect_plot - plot power spectrum |
nt_spect_plot2 | nt_spect_plot2 - plot power spectrum |
nt_split | [idx,score_vector,score]=nt_split(x,depth,thresh,guard) - split time series into intervals |
nt_split_jd | [idx,score_vector,todss]=nt_split_dss(x,thresh,depth) - segmentation based on joint diagonalization |
nt_statmatrix | stats=nt_statMatrix(x,plot_params) - calculate statistics arrays for each dim of matrix |
nt_trial2mat | [y,w]=nt_trial2mat(x,max_nsamples) - convert trial cell array to 3D matrix |
nt_tsr | [y,idx,w]=nt_tsr(x,ref,shifts,wx,wref,keep,thresh) - time-shift regression (TSPCA) |
nt_tsr_nodemean | [y,idx,w]=nt_tsr_nodemean(x,ref,shifts,wx,wref,keep,thresh) - time-shift regression (TSPCA) |
nt_tsregress | [z,idx]=nt_tsregress(x,y,shifts,xw,yw,keep,threshold) - time-shift regression |
nt_tsxcov | [c,tw]=nt_tsxcov(x,y,shifts,w) - cross-covariance of X and time-shifted Y |
nt_unfold | y=nt_fold(x) - unfold 3D to 2D |
nt_varplot | varplot(x,mode) - plot variance maps (time*trials, time*chans, chans*trials) |
nt_vecadd | y=nt_vecadd(x,v) - add vector to all rows or columns of matrix |
nt_vecmult | y=nt_vecmult(x,v) - multiply all rows or columns of matrix by vector |
nt_version | |
nt_whoss | size=nt_whoss - total Gbytes used by variables |
nt_wmean | y=wmean(x,w,dim) - weighted average over columns |
nt_wpwr | [y,tweight]=nt_wpwr(x,w) - weighted power |
nt_xcov | [c,tw]=nt_xcov(x,y,shifts,w) - cross-covariance of X and time-shifted Y |
nt_xprod | [y,ind]=nt_xprod(x,flag,dsratio,normrow_flag) - form all crossproducts |
nt_xprod2 | [y,ind]=nt_xprod(x1,x2,dsratio) - form all crossproducts |
nt_xxcorr | [C,idx]=nt_xxcorr(A,B,MAXLAG) - true unbiased cross-correlation |
nt_yulewalk_whiten | [y,B,A]=nt_yulewalk_whiten(x,order,freqs) - whiten spectrally |
test_nt_bias_fft | test nt_bias_fft |
zz_package | |