How to convert data in .txt format collected from Shimadzu near-infrared equipment into rawnirx format that can be processed by MNE?

💬 Support & Discussions
1

How to convert data in .txt format collected from Shimadzu near-infrared equipment into rawnirx format that can be processed by MNE?
the .txt file is :[File Information] [Data Line] 36
Measured Date 2024/04/11 18:05:47
ID 002 Version 11.0
Name lt1 [HeaderType] 11.0/11.0
Comment study comment
[Measurement Info.]
Holder Location 0
Block Design Off
Pre.Rest Time 0
Task Time 0
Pos.Rest Time 0
Loop 0
Averaging 1
Total Points 18002
[.INF File]
Filename test.inf
[Condition-1]
HV Switching OFF
R1 ,R2 ,R3 ,R4 ,R5 ,R6 ,R7 ,R8 ,R9 ,R10,R11,R12
738.00,783.00,841.00,702.00,683.00,831.00,726.00,828.00,701.00,857.00,736.00,890.00
Gain(X1,X4,X16,X64)
R1 ,R2 ,R3 ,R4 ,R5 ,R6 ,R7 ,R8 ,R9 ,R10,R11,R12
1,1,1,1,1,1,1,1,1,1,1,1
[Analysis Condition]
Smoothed Type None
Smoothed Points 0
Smoothed Loop 0
Baseline Method None
Baseline location
[Text Info.]
Output Mode Continious Task No. Data Type Hb
Time Range 0.00 378.02 Averaing 1
(1,1)(2,1)(1,2)(3,1)(2,3)(3,2)(3,3)(4,2)(3,4)(5,3)(4,4)(5,4)(4,5)(6,4)(5,6)(6,5)(6,6)(7,7)(8,7)(7,8)(9,7)(8,9)(9,8)(9,9)(10,8)(9,10)(11,9)(10,10)(11,10)(10,11)(12,10)(11,12)(12,11)(12,12)
ch- 1 ch- 1 ch- 1 ch- 2 ch- 2 ch- 2 ch- 3 ch- 3 ch- 3 ch- 4 ch- 4 ch- 4 ch- 5 ch- 5 ch- 5 ch- 6 ch- 6 ch- 6 ch- 7 ch- 7 ch- 7 ch- 8 ch- 8 ch- 8 ch- 9 ch- 9 ch- 9 ch-10 ch-10 ch-10 ch-11 ch-11 ch-11 ch-12 ch-12 ch-12 ch-13 ch-13 ch-13 ch-14 ch-14 ch-14 ch-15 ch-15 ch-15 ch-16 ch-16 ch-16 ch-17 ch-17 ch-17 ch-18 ch-18 ch-18 ch-19 ch-19 ch-19 ch-20 ch-20 ch-20 ch-21 ch-21 ch-21 ch-22 ch-22 ch-22 ch-23 ch-23 ch-23 ch-24 ch-24 ch-24 ch-25 ch-25 ch-25 ch-26 ch-26 ch-26 ch-27 ch-27 ch-27 ch-28 ch-28 ch-28 ch-29 ch-29 ch-29 ch-30 ch-30 ch-30 ch-31 ch-31 ch-31 ch-32 ch-32 ch-32 ch-33 ch-33 ch-33 ch-34 ch-34 ch-34
Time(sec) Task Mark Count oxyHb deoxyHb totalHb oxyHb deoxyHb totalHb oxyHb deoxyHb totalHb oxyHb deoxyHb totalHb oxyHb deoxyHb totalHb oxyHb deoxyHb totalHb oxyHb deoxyHb totalHb oxyHb deoxyHb totalHb oxyHb deoxyHb totalHb oxyHb deoxyHb totalHb oxyHb deoxyHb totalHb oxyHb deoxyHb totalHb oxyHb deoxyHb totalHb oxyHb deoxyHb totalHb oxyHb deoxyHb totalHb oxyHb deoxyHb totalHb oxyHb deoxyHb totalHb oxyHb deoxyHb totalHb oxyHb deoxyHb totalHb oxyHb deoxyHb totalHb oxyHb deoxyHb totalHb oxyHb deoxyHb totalHb oxyHb deoxyHb totalHb oxyHb deoxyHb totalHb oxyHb deoxyHb totalHb oxyHb deoxyHb totalHb oxyHb deoxyHb totalHb oxyHb deoxyHb totalHb oxyHb deoxyHb totalHb oxyHb deoxyHb totalHb oxyHb deoxyHb totalHb oxyHb deoxyHb totalHb oxyHb deoxyHb totalHb oxyHb deoxyHb totalHb
0.000 00 0Z 0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
0.021 00 0 0 -0.006160 0.011949 0.005789 0.015555 -0.018410 -0.002855 -0.079755 0.074338 -0.005417 -0.052837 0.052125 -0.000712 0.140793 -0.143544 -0.002751 0.036355 -0.031682 0.004673 -0.101740 0.142909 0.041168 0.048988 -0.052649 -0.003661 -0.118876 0.127869 0.008992 0.010694 0.001246 0.011940 0.016240 -0.015152 0.001087 0.007327 -0.005945 0.001383 -0.012007 0.012511 0.000503 0.025073 -0.024120 0.000954 -0.005174 0.019963 0.014789 0.010344 -0.009157 0.001187 -0.020369 0.016199 -0.004170 -0.009940 0.013243 0.003303 0.006296 -0.008471 -0.002175 -0.002663 0.002626 -0.000038 -0.020785 0.021312 0.000528 -0.008192 0.006971 -0.001220 0.026555 -0.035482 -0.008927 -0.025491 0.030381 0.004891 0.001797 -0.019130 -0.017333 0.029858 -0.016259 0.013598 -0.016654 0.012757 -0.003896 -0.013601 0.002113 -0.011488 0.013581 -0.007965 0.005615 0.033378 -0.045274 -0.011896 0.016133 -0.004644 0.011489 -0.018165 -0.023078 -0.041243 0.008916 -0.006010 0.002906 0.060388 -0.046250 0.014137
0.042 00 0 0 0.009880 -0.018773 -0.008893 0.013683 -0.018304 -0.004621 -0.017991 0.025041 0.007050 -0.451420 0.538934 0.087514 0.067943 -0.094891 -0.026948 -0.056775 0.085034 0.028259 0.133093 -0.143020 -0.009926 0.052382 -0.050700 0.001682 -0.030789 0.018250 -0.012539 0.059997 -0.046654 0.013344 0.011289 -0.009115 0.002174 -0.009831 0.010001 0.000170 -0.013517 0.011936 -0.001580 0.007153 -0.006119 0.001035 -0.025426 0.032412 0.006986 0.005110 0.000312 0.005422 0.050359 -0.051803 -0.001444 -0.014842 0.017575 0.002732 0.007440 -0.008726 -0.001286 0.057609 -0.042961 0.014648 -0.016536 0.016247 -0.000289 -0.008005 0.004058 -0.003947 0.038476 -0.045322 -0.006846 -0.028217 0.019670 -0.008547 -0.055549 0.032228 -0.023321 -0.018638 0.012658 -0.005980 0.006991 -0.010938 -0.003947 -0.038311 0.027751 -0.010560 -0.031260 0.035117 0.003857 -0.006488 -0.000624 -0.007112 -0.012022 0.025240 0.013218 -0.035746 0.005979 -0.029767 0.000878 0.007694 0.008572 0.003574 0.007531 0.011105
0.063 00 0 0 -0.006916 0.005621 -0.001295 -0.009190 0.009956 0.000766 -0.021878 0.031277 0.009399 -0.165847 0.189875 0.024028 0.063469 -0.076296 -0.012828 -0.040845 0.073470 0.032625 -0.016937 0.030262 0.013325 0.037087 -0.036959 0.000128 -0.118615 0.131567 0.012952 0.024526 -0.027619 -0.003093 0.003933 0.001058 0.004991 0.015362 -0.021752 -0.006390 -0.005144 0.009779 0.004634 0.004272 -0.000325 0.003947 -0.065356 0.062498 -0.002857 0.009859 -0.008791 0.001068 -0.034004 0.036882 0.002878 -0.025254 0.030273 0.005020 -0.003258 0.004232 0.000974 0.021454 -0.023181 -0.001727 0.007568 -0.014749 -0.007181 -0.012436 0.002233 -0.010203 0.012903 -0.023224 -0.010321 0.020149 -0.017054 0.003095 0.012533 -0.028201 -0.015668 0.002480 0.007708 0.010188 0.011011 -0.023890 -0.012879 -0.003066 -0.002865 -0.005931 -0.038198 0.046603 0.008405 -0.009254 0.008691 -0.000563 -0.004091 0.010419 0.006328 0.037012 -0.053427 -0.016415 -0.002585 0.010719 0.008133 0.037573 -0.042629 -0.005056
0.084 00 0 0 -0.011347 0.003337 -0.008010 0.021463 -0.021980 -0.000517 -0.038256 0.043004 0.004748 -0.030916 0.022373 -0.008543 0.060922 -0.077088 -0.016166 -0.045819 0.082347 0.036528 0.108121 -0.117308 -0.009187 0.011343 -0.011312 0.000031 0.003480 -0.020141 -0.016661 -0.002601 -0.001794 -0.004395 -0.003952 0.011589 0.007637 -0.024762 0.013793 -0.010969 -0.006042 0.010604 0.004563 0.017682 -0.010051 0.007631 -0.024645 0.022855 -0.001790 0.014931 -0.011722 0.003209 -0.021125 0.024283 0.003158 -0.028332 0.029298 0.000966 0.001823 -0.006242 -0.004419 -0.013370 0.017085 0.003715 -0.008546 0.011998 0.003452 0.012930 -0.022355 -0.009425 0.042444 -0.051998 -0.009555 0.057316 -0.046512 0.010804 -0.034705 0.013125 -0.021580 -0.034337 0.035570 0.001233 0.010264 -0.014227 -0.003963 -0.010998 0.010041 -0.000957 -0.046538 0.043307 -0.003230 0.001726 0.005979 0.007705 -0.004664 0.019813 0.015150 -0.000802 -0.003058 -0.003860 -0.010276 0.028771 0.018495 0.083247 -0.060752 0.022494

2

It doesn’t seem that MNE-Python has a built-in reader for this dataformat. You will probably have a write a piece of Python code yourself to read this data as a numpy array. Then, you can “manually” create a Raw object from this array: Creating MNE-Python data structures from scratch — MNE 1.6.1 documentation

1 Like
3

Thank you for your repley,but I’m looking for better solutions

4

Let us know when you found one!

1 Like