 nt_3Dmat2cell | [y]=3Dmat2cell(x) - convert 3D matrix to trial cell array |
| nt_back | [iBest,y,topo]=nt_back(x,z,layout) - back-project component |
| 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_bsmean | [mn,sd,all]=nt_bsmean(x,N,w) - calculate mean, estimate sd using bootstrap |
| nt_bsplot | nt_bsplot(x,w,style,abscissa,zeroflag,rmsflag) - plot average with bootstrap standard deviation |
| nt_bsrms | [rms,sd,all]=nt_bsrms(x,N,w) - calculate rms, estimate sd using bootstrap |
| nt_cca | [A,B,R]=nt_cca(x,y,shifts,C,m,thresh,demeanflag) - canonical correlation |
| nt_cca_aad | [D,E,R]=nt_cca_match_aad(xA,xB,y,ssize) - calculate metrics for match-mismatch task |
| nt_cca_crossvalidate | [AA,BB,RR,iBest]=nt_cca_crossvalidate(xx,yy,shifts,ncomp,A0,B0) - CCA with cross-validation |
| nt_cca_crossvalidate2 | [A,B,RR]=nt_cca_crossvalidate2(xx,yy,shifts) - CCA with cross-validation |
| nt_cca_crossvalidate_3 | [AA,BB,RR,iBest]=nt_cca_crossvalidate(xx,yy,shifts,ncomp,A0,B0) - CCA with cross-validation |
| nt_cca_mm | [D,E,R]=nt_cca_mm(x,y,ssize,ldaflag,nccs) - calculate metrics for match-mismatch task |
| nt_cca_mm3 | [D,E,R]=nt_cca_mm(x,y,ssize,ldaflag,nccs) - calculate metrics for match-mismatch task |
| nt_cell2mat | y=nt_cell2mat(x) - convert cell matrix of nD matrices to (n+1)D matrix |
| nt_cluster1D | [C,A,score]=nt_cluster1D_b(x) - cluster 1D data into 2 clusters |
| nt_cluster_jd | [IDX,todss,SCORE,COVS]=nt_cluster_jd(x,dsr,smooth,flags,init,verbose) - cluster with joint diagonalization |
| nt_cov | [c,tw]=nt_cov(x,shifts,w) - time shift covariance |
| nt_cov_lags | [C,tw,m]=nt_cov_lags(x,y,shifts,nodemeanflag) - covariance of [x,y] with lags |
| nt_dataview | [p,data]=nt_dataview(data,p) - view data sets |
| nt_deboing | y=nt_deboing(x,events) - fit, remove ringing associated with events |
| 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_destep | [y,stepList]=nt_destep(x,thresh,guard,depth,minstep) - remove step glitch from MEG data |
| nt_detrend (original) | [y,w,r]=nt_detrend(x,order,w,basis,thresh,niter,wsize) - robustly remove trend |
| nt_detrend | [y,w,r]=nt_detrend(x,order,w,basis,thresh,niter,wsize) - robustly remove trend |
| nt_detrend2 | [y,w,r]=nt_detrend2(x,varargin) - robustly remove trend, |
| nt_dft_filter | y=nt_dft_filter(x,transfer,N) - apply filter using DFT |
| nt_double2int | nt_double2int() - recode/decode double as integer to save space |
| nt_dprime | [d,e]=nt_dprime(x,y,jd_flag) - calculate d' (discriminability) of two distributions |
| nt_dsample | [y,yy,yyy]=nt_dsample(x,dsr,method) - 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) - evoked-biased DSS denoising |
| nt_epoch | y=nt_epochify(x,idx,bounds) - extract epochs based on trigger indices |
| nt_eyeblink | [y,z,mask]=nt_eyeblink(x,eyechans,nremove) - project out eyeblinks |
| nt_filter_comb | [B,A] = nt_filter_comb(T,sign) - simple comb filter |
| nt_filter_peak | [B,A] = nt_filter_peak(Wo,Q) - second order resonator filter |
| nt_find_bad_channels | [iBad,toGood]=nt_find_bad_channels(x,proportion,thresh1,thresh2,thresh3) - find bad channels |
| nt_find_outlier_trials | [idx,d,mn,idx_unsorted]=nt_find_outlier_trials(x,criterion,plot,regress_flag) - find outlier trials |
| 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_idx | i=nt_idx(x,scale,i) - index a data matrix |
| nt_idx_disp | nt_idx_disp(name,field,explainflag) - display contents of index file |
| nt_idxx | nt_idxx(fname,p) - create an index file to summarize large data file |
| nt_imagescc | nt_imagescc - plot image with symmetric scaling |
| nt_index | [status,p]=nt_index(name,p,forceUpdate) - index data files & directories |
| nt_inpaint | function y=nt_inpaint(x,w) - weighted interpolation based on correlation structure |
| nt_interpolate_bad_channels | y=interpolate_bad_channels(x,iBad,coordinates,n) - interpolate bad channels from good |
| nt_iplot | [hh,ii]=nt_iplot(fname) - plot data file based on index |
| 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_lsp | [Y,scores,removed]=nt_LSP(X,npass,thresh,tol,guard) - local subspace pruning |
| nt_mark | nt_mark(idx,labels,line_params,text_params) |
| nt_mat2trial | [y]=nt_mat2trial(x,w) - convert 3D matrix to trial cell array |
| nt_mcca | [A,score,AA]=nt_mcca(C,N) - multi-set cca |
| nt_mfilt | y=nt_mfilt(x,M,B,A,expand) - multichannel filter |
| nt_mmat | y=nt_mmat(x,m) - matrix multiplication (with convolution) |
| nt_mmx | [y,abscissa]=nt_mmx(x, N) - calculate min-max pairs |
| nt_morton | [iMorton,toMorton]=nt_morton(nrows,ncols) - indices for Morton scan of image |
| nt_multishift | y=nt_multishift(x,shifts,pad) - apply multiple shifts to matrix |
| nt_multismooth | z=nt_multismooth(x,smooth,alignment,diff_flag) - apply multiple smoothing kernels |
| 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_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_outliers | [w,y]=nt_outliers(x,w,thresh,niter) - detect outliers based on weighted correlation structure |
| nt_pca | [z,idx]=nt_pca(x,shifts,nkeep,threshold,w) - time-shift pca |
| nt_pca0 | [topcs,pwr,y]=nt_pca0(x,shifts,nkeep,threshold,w) - time-shift pca |
| nt_pcarot | [topcs,eigenvalues]=pcarot(cov,nkeep,threshold,N) - PCA matrix from covariance |
| nt_peaksign | sgn=peaksign(x,dim) - sign of largest extremum |
| nt_phase_scramble | y=nt_phase_scramble(x,allsameflag) - scramble time but preserve autocorrelation |
| nt_plot_mmx | nt_plot_mmx - plot data using min-max pairs |
| nt_plotxx | nt_plotxx(fname,bounds,chans) - plot using index file |
| nt_proximity | [closest,d]=nt_proximity(coordinates,N) - distance to neighboring channels |
| 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_read_data | [p,data]=nt_read_data(fname,flag) - read data from file |
| nt_read_header | [h,readwith]=nt_read_header(fname,flag) - read data from file |
| nt_regcov | r=nt_regcov(cxy,cyy,keep,threshold) - regression matrix from cross covariance |
| nt_regw (original) | [b,z]=nt_regw(y,x,w) - weighted regression |
| nt_regw | [b,z]=nt_regw(y,x,w) - weighted regression |
| nt_relshift | [xx,yy]=nt_relshift(x,y,shift,flag) - delay x relative to y |
| nt_rereference | [y,mn]=nt_rereference(x,w,factor) - rereference by subtracting weighted mean |
| nt_rereference2 | [y,mn]=nt_rereference2(x,mask,thresh,factor) - robust rereferencing |
| nt_resample | RESAMPLE Change the sampling rate of a signal. |
| nt_resample_interp1 | y=resample_interp1(x,p,q,method) - Resample using interp1 (no antialiasing) |
| nt_rms | RMS - Root-mean-square |
| nt_same_clim | nt_same_clim(h,clim) - harmonize color limits of plots within figure |
| nt_sca | [M,y,score,proportion]=nt_sca(x,ncomp) - shared component analysis |
| 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_sns_cluster | y=nt_sns_cluster(x,nneigbors,cluster_size) - sensor noise suppression within clusters |
| nt_sparse_filter | y=nt_sparse_filter(x,T,A) - convolve multichannel data with sparse impulse response |
| 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,minstep) - split time series into intervals |
| nt_split_jd | [idx,score_vector,todss]=nt_split_dss(x,thresh,depth) - segmentation based on joint diagonalization |
| nt_squeeze_all | y=nt_squeeze_all(x) - squeeze structs and cell arrays |
| nt_star | [y,w,ww]=nt_star(x,thresh,closest,depth) - sensor noise suppression |
| nt_star2 | [y,w,ww]=nt_star2(x,thresh,closest,w) - sensor noise suppression |
| nt_statmatrix | stats=nt_statMatrix(x,plot_params) - calculate statistics arrays for each dim of matrix |
| nt_subspace_prune | [Y]=nt_subspace_prune(X,npass,thresh) - local cleaning matrices |
| nt_subspace_prune5 | [Y]=nt_subspace_prune(X,npass,thresh) - local cleaning matrices |
| nt_subspace_prune6 | [Y]=nt_subspace_prune6(X,npass,thresh) - local cleaning matrices |
| nt_topoplot | nt_topoplot(cfg,data) - simple topoplot |
| 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_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_unique | [C,IA,IC,N] = nt_unique(A, varargin) - unique with counts |
| 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_verbose | previous=nt_verbose(new) - set/get global verbose flag |
| nt_version | |
| nt_video_sns | y=nt_video_sns(x,nneighbors) - apply SNS locally |
| nt_whiten | [A,y]=nt_whiten(x,N) - whiten spectrally using pca |
| nt_whoss | size=nt_whoss - total Gbytes used by variables |
| nt_wmean | y=nt_wmean(x,w,dim) - weighted average |
| 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,centerflag) - true normalized unbiased cross-correlation function |
| nt_zapline | [y,yy]=nt_zapline(x,fline,nremove,p,plotflag) - remove power line artifact |
| zz_package | |
| zz_upload_data | |
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