Differences

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--- gsr_data_analysis [2010/06/18 21:15] – altered the syntax for resampling (now as a xff method!) jochen
+++ gsr_data_analysis [2010/06/18 23:22] (current) – changed onsets to be in ms jochen
@@ Line 69: / Line 69: @@
 ===== (Pre-) Filtering data =====
-While a lot of the very high-frequency noise is (naturally) gone after the resampling, there is still a lot of signal variation that is (very likely) unrelated to any task/stimulation the subject could have reacted to. Further the signal pre-processing, I then applied a difference (first discreet derivative, ''diff'')-filter that removes artifacts in signals which are unlikely to be in the desired spectrum using the [[prefilter]] function:
+While a lot of the very high-frequency noise is (naturally) gone after the resampling, there is still a lot of signal variation that is (very likely) unrelated to any task/stimulation the subject could have reacted to. Further the signal pre-processing, I then applied a difference (first discreet derivative, ''diff'')-filter that removes artifacts in signals which are unlikely to be in the desired spectrum using the [[acq.Filter|ACQ::Filter]]/[[ntt.Filter|NTT::Filter]] method (which makes use of the [[prefilter]] function):
-<code matlab GSR_prefilter.m>% simply pass to prefilter
+<code matlab GSR_prefilter.m>% call obj.Filter with default parameters for first channel
-fdata = prefilter(rdata);
+gsr.Filter(1);
 % plotting same time index with stronger line
-set(plot(fdata(50400:51399)), 'LineWidth', 2);</code>
+data = gsr.ChannelData(1);
+set(plot(data(50400:51399)), 'LineWidth', 2);</code>
 {{:gsr_example_5secsfiltered.png|filtered GSR data}}
@@ Line 88: / Line 89: @@
 <code matlab GSR_acq_stimonsets.m>% all stimuli onsets (use 1 + to cope for the fact that the
 % first value in diff rather codes the second sample in the data!
-all_onsets = 1 + find(diff(gsr.RawData(8, :)') > 0);
+all_onsets = 1 + find(diff(gsr.ChannelData(8)) > 0);
 % now split into three conditions
-neu = all_onsets(gsr.RawData(2, all_onsets) > 0);
+neu = 0.01 * all_onsets(gsr.ChannelData(2, all_onsets) > 0);
-neg = all_onsets(gsr.RawData(3, all_onsets) > 0);
+neg = 0.01 * all_onsets(gsr.ChannelData(3, all_onsets) > 0);
-rea = all_onsets(gsr.RawData(4, all_onsets) > 0);
+rea = 0.01 * all_onsets(gsr.ChannelData(4, all_onsets) > 0);</code>
-% cope for resampling from 500Hz -> 100Hz !!
-neu = round(0.2 .* neu);
-neg = round(0.2 .* neg);
-rea = round(0.2 .* rea);</code>
 As window length for the inspection of the GSR data, we choose 15 seconds, so in the resolution of the resampled data that means 1,500 samples.
@@ Line 140: / Line 136: @@
 reacurves = cell(numel(rea), 2);
 for sc = 1:numel(neu)
-    neucurves{sc, 1} = sprintf('LookNeu %02d at t=%.3fs', sc, 0.01 * neu(sc));
+    neucurves{sc, 1} = sprintf('LookNeu %02d at t=%.3fs', sc, neu(sc));
-    neucurves{sc, 2} = [gsrchannel, neu(sc) - 100, neu(sc) + 1400];
+    neucurves{sc, 2} = [gsrchannel, neu(sc) - 2, neu(sc) + 14];
 end
 for sc = 1:numel(neg)
-    negcurves{sc, 1} = sprintf('LookNeg %02d at t=%.3fs', sc, 0.01 * neg(sc));
+    negcurves{sc, 1} = sprintf('LookNeg %02d at t=%.3fs', sc, neg(sc));
-    negcurves{sc, 2} = [gsrchannel, neg(sc) - 100, neg(sc) + 1400];
+    negcurves{sc, 2} = [gsrchannel, neg(sc) - 2, neg(sc) + 14];
 end
 for sc = 1:numel(rea)
-    reacurves{sc, 1} = sprintf('ReaNeg %02d at t=%.3fs', sc, 0.01 * rea(sc));
+    reacurves{sc, 1} = sprintf('ReaNeg %02d at t=%.3fs', sc, rea(sc));
-    reacurves{sc, 2} = [gsrchannel, rea(sc) - 100, rea(sc) + 1400];
+    reacurves{sc, 2} = [gsrchannel, rea(sc) - 2, rea(sc) + 14];
 end