Stats for wavelengths when: Thinking of lists vs Calmly counting breaths


#1

I’ve been wondering a similar question to what I’ve seen elsewhere in this forum… Is it possible to distinguish between thinking about lists of items and calmly counting your breaths (as the Calm app does) just by looking at the outputs of the alpha, beta, delta, gamma, and theta values from Muse-io?

I wrote a small Python script to collect samples from muse-io, then ran some tests, collecting stats from these 5 wavelengths first when I was calmly counting my breath, then when I was thinking of lists of items. My eyes were closed for all tests, and I tried to remain motionless. The stats I calculated were:

  • The average across all 4 channels.
  • The standard deviation of those.
  • The average of the difference between the outer channels (the ear sensors, I think) and the inner channels (the forehead ones). …Just curious if this was a useful stat.

Here are the results, after 3 runs each:

Calmly counting my breath: [TABLE=“border: 1, cellpadding: 0, cellspacing: 0”]
[TR]
[TD]Wavelength[/TD]
[TD]Average[/TD]
[TD]StdDev[/TD]
[TD]AvgInnerOuterDiff[/TD]
[/TR]
[TR]
[TD]alpha[/TD]
[TD]0.4751213969[/TD]
[TD]0.1889237762[/TD]
[TD]0.6490456939[/TD]
[/TR]
[TR]
[TD]alpha[/TD]
[TD]0.4770162579[/TD]
[TD]0.2013411181[/TD]
[TD]0.6490616494[/TD]
[/TR]
[TR]
[TD]alpha[/TD]
[TD]0.4550054845[/TD]
[TD]0.1907574326[/TD]
[TD]0.6020445597[/TD]
[/TR]
[TR]
[TD]beta[/TD]
[TD]0.1752507709[/TD]
[TD]0.07666440809[/TD]
[TD]-0.2408093283[/TD]
[/TR]
[TR]
[TD]beta[/TD]
[TD]0.1726147429[/TD]
[TD]0.09328954243[/TD]
[TD]-0.258974685[/TD]
[/TR]
[TR]
[TD]beta[/TD]
[TD]0.1915382191[/TD]
[TD]0.09270198038[/TD]
[TD]-0.2700623787[/TD]
[/TR]
[TR]
[TD]delta[/TD]
[TD]0.09261829216[/TD]
[TD]0.03970330585[/TD]
[TD]-0.03596189471[/TD]
[/TR]
[TR]
[TD]delta[/TD]
[TD]0.09052062096[/TD]
[TD]0.0474494414[/TD]
[TD]-0.018616751[/TD]
[/TR]
[TR]
[TD]delta[/TD]
[TD]0.09159078626[/TD]
[TD]0.04311748433[/TD]
[TD]0.01822522013[/TD]
[/TR]
[TR]
[TD]gamma[/TD]
[TD]0.1239252001[/TD]
[TD]0.104735215[/TD]
[TD]-0.3472721423[/TD]
[/TR]
[TR]
[TD]gamma[/TD]
[TD]0.1241877593[/TD]
[TD]0.1041524482[/TD]
[TD]-0.3441326722[/TD]
[/TR]
[TR]
[TD]gamma[/TD]
[TD]0.1375192379[/TD]
[TD]0.1087980287[/TD]
[TD]-0.3715811601[/TD]
[/TR]
[TR]
[TD]theta[/TD]
[TD]0.1303571939[/TD]
[TD]0.0534570709[/TD]
[TD]-0.02298597303[/TD]
[/TR]
[TR]
[TD]theta[/TD]
[TD]0.1354593192[/TD]
[TD]0.05822344283[/TD]
[TD]-0.02764595506[/TD]
[/TR]
[TR]
[TD]theta[/TD]
[TD]0.1243291569[/TD]
[TD]0.04635326483[/TD]
[TD]0.02178784767[/TD]
[/TR]
[/TABLE]

Thinking of lists: [TABLE=“border: 1, cellpadding: 0, cellspacing: 0”]
[TR]
[TD]Wavelength[/TD]
[TD]Average[/TD]
[TD]StdDev[/TD]
[TD]AvgInnerOuterDiff[/TD]
[/TR]
[TR]
[TD]alpha[/TD]
[TD]0.4536775622[/TD]
[TD]0.1563255835[/TD]
[TD]0.4897681704[/TD]
[/TR]
[TR]
[TD]alpha[/TD]
[TD]0.4422713957[/TD]
[TD]0.1935869179[/TD]
[TD]0.6850267951[/TD]
[/TR]
[TR]
[TD]alpha[/TD]
[TD]0.4935361769[/TD]
[TD]0.1700687443[/TD]
[TD]0.5671967926[/TD]
[/TR]
[TR]
[TD]beta[/TD]
[TD]0.1827911929[/TD]
[TD]0.07407946822[/TD]
[TD]-0.213839425[/TD]
[/TR]
[TR]
[TD]beta[/TD]
[TD]0.190310952[/TD]
[TD]0.08349912336[/TD]
[TD]-0.2773680064[/TD]
[/TR]
[TR]
[TD]beta[/TD]
[TD]0.1749950064[/TD]
[TD]0.08500056212[/TD]
[TD]-0.276590995[/TD]
[/TR]
[TR]
[TD]delta[/TD]
[TD]0.1107012605[/TD]
[TD]0.04559013109[/TD]
[TD]0.03878559766[/TD]
[/TR]
[TR]
[TD]delta[/TD]
[TD]0.09613162568[/TD]
[TD]0.04031305809[/TD]
[TD]-0.0109942097[/TD]
[/TR]
[TR]
[TD]delta[/TD]
[TD]0.08270908896[/TD]
[TD]0.03629971425[/TD]
[TD]0.01569490284[/TD]
[/TR]
[TR]
[TD]gamma[/TD]
[TD]0.1280369129[/TD]
[TD]0.09080920795[/TD]
[TD]-0.3043224003[/TD]
[/TR]
[TR]
[TD]gamma[/TD]
[TD]0.144984072[/TD]
[TD]0.1082802882[/TD]
[TD]-0.3950253618[/TD]
[/TR]
[TR]
[TD]gamma[/TD]
[TD]0.1265461679[/TD]
[TD]0.09545922816[/TD]
[TD]-0.3337780315[/TD]
[/TR]
[TR]
[TD]theta[/TD]
[TD]0.1249191545[/TD]
[TD]0.04606365597[/TD]
[TD]-0.01077615387[/TD]
[/TR]
[TR]
[TD]theta[/TD]
[TD]0.1264480969[/TD]
[TD]0.05169248744[/TD]
[TD]-0.001383040112[/TD]
[/TR]
[TR]
[TD]theta[/TD]
[TD]0.1221867674[/TD]
[TD]0.045854098[/TD]
[TD]0.02779129104[/TD]
[/TR]
[/TABLE]

From these early results… I can’t see any significant difference between the two sets. At first maybe it looks like alpha averages higher when calmer, but there are exceptions. Or maybe alpha AvgInnerOuterDiff is higher or more consistent… Or maybe theta is higher on average when calm…

But nothing really jumps out as an obvious, “Aha, here’s the difference!” Is there some other way to manipulate these numbers to bring out the difference? Should I stop looking at the DSP values and try some direct analysis of the EEG values? If anyone has any recommendations, I’d love to hear them.


#2

I think that study confirms my suspicions that Muse does not measure brain waves but only galvanic skin response.


#3

nekrodezynfekator, what results would you expect, with those two mindstates? Which measurements would you expect to change, and how?


#4

Thinking that maybe averaging across all the different sensors was losing too much information, I ran a few more tests. These take the average and standard deviation for each sensor. There are some patterns starting to emerge now:

This is three sessions counting breaths: [TABLE=“border: 1, cellpadding: 0, cellspacing: 0”]
[TR]
[TD]Wavelength[/TD]
[TD]AvgLtEar[/TD]
[TD]StdDevLtEar[/TD]
[TD]AvgLt[/TD]
[TD]StdDevLt[/TD]
[TD]AvgRt[/TD]
[TD]StdDevRt[/TD]
[TD]AvgRtEar[/TD]
[TD]StdDevRtEar[/TD]
[/TR]
[TR]
[TD]alpha[/TD]
[TD]0.6330833603[/TD]
[TD]0.08772757074[/TD]
[TD]0.3794616338[/TD]
[TD]0.1007266785[/TD]
[TD]0.3580577516[/TD]
[TD]0.09642178723[/TD]
[TD]0.6263757892[/TD]
[TD]0.105560091[/TD]
[/TR]
[TR]
[TD]alpha[/TD]
[TD]0.6475729926[/TD]
[TD]0.1328247497[/TD]
[TD]0.4286780718[/TD]
[TD]0.1243937238[/TD]
[TD]0.3729810246[/TD]
[TD]0.1039645669[/TD]
[TD]0.5924465922[/TD]
[TD]0.1314230424[/TD]
[/TR]
[TR]
[TD]alpha[/TD]
[TD]0.6222156764[/TD]
[TD]0.08564277711[/TD]
[TD]0.3253626437[/TD]
[TD]0.1003814372[/TD]
[TD]0.33641845[/TD]
[TD]0.1160339359[/TD]
[TD]0.6097297871[/TD]
[TD]0.0884867466[/TD]
[/TR]
[TR]
[TD]beta[/TD]
[TD]0.1285608747[/TD]
[TD]0.03600065769[/TD]
[TD]0.2282925232[/TD]
[TD]0.05487325037[/TD]
[TD]0.1982182763[/TD]
[TD]0.05997271687[/TD]
[TD]0.1242210744[/TD]
[TD]0.04140761778[/TD]
[/TR]
[TR]
[TD]beta[/TD]
[TD]0.1012350922[/TD]
[TD]0.03921677833[/TD]
[TD]0.1737540467[/TD]
[TD]0.05359001228[/TD]
[TD]0.1915843689[/TD]
[TD]0.04978839574[/TD]
[TD]0.1199917053[/TD]
[TD]0.04164466884[/TD]
[/TR]
[TR]
[TD]beta[/TD]
[TD]0.1358209892[/TD]
[TD]0.04176110537[/TD]
[TD]0.2561816977[/TD]
[TD]0.06408753968[/TD]
[TD]0.2152229927[/TD]
[TD]0.05283680542[/TD]
[TD]0.1251112419[/TD]
[TD]0.039310799[/TD]
[/TR]
[TR]
[TD]delta[/TD]
[TD]0.08741310867[/TD]
[TD]0.04858370156[/TD]
[TD]0.1006711287[/TD]
[TD]0.03807214877[/TD]
[TD]0.1497869849[/TD]
[TD]0.06294931874[/TD]
[TD]0.09503044201[/TD]
[TD]0.04500831146[/TD]
[/TR]
[TR]
[TD]delta[/TD]
[TD]0.111204885[/TD]
[TD]0.1259510447[/TD]
[TD]0.1324524294[/TD]
[TD]0.09614228122[/TD]
[TD]0.1223602[/TD]
[TD]0.03566042126[/TD]
[TD]0.1271616598[/TD]
[TD]0.1391866915[/TD]
[/TR]
[TR]
[TD]delta[/TD]
[TD]0.08070543648[/TD]
[TD]0.03814205521[/TD]
[TD]0.120690532[/TD]
[TD]0.05708683051[/TD]
[TD]0.1200020449[/TD]
[TD]0.05526797734[/TD]
[TD]0.09488425109[/TD]
[TD]0.05262639567[/TD]
[/TR]
[TR]
[TD]gamma[/TD]
[TD]0.05166671813[/TD]
[TD]0.01727383346[/TD]
[TD]0.1157740172[/TD]
[TD]0.03379012941[/TD]
[TD]0.1131046424[/TD]
[TD]0.0398536945[/TD]
[TD]0.05719088971[/TD]
[TD]0.01937769755[/TD]
[/TR]
[TR]
[TD]gamma[/TD]
[TD]0.03935840631[/TD]
[TD]0.01665156885[/TD]
[TD]0.08930571446[/TD]
[TD]0.029377722[/TD]
[TD]0.1163374926[/TD]
[TD]0.04378806003[/TD]
[TD]0.04653561872[/TD]
[TD]0.01634758469[/TD]
[/TR]
[TR]
[TD]gamma[/TD]
[TD]0.05773186593[/TD]
[TD]0.0216002595[/TD]
[TD]0.1406972577[/TD]
[TD]0.04494724072[/TD]
[TD]0.1657416774[/TD]
[TD]0.06352824449[/TD]
[TD]0.05550387811[/TD]
[TD]0.02055051771[/TD]
[/TR]
[TR]
[TD]theta[/TD]
[TD]0.099656986[/TD]
[TD]0.0342752043[/TD]
[TD]0.1758580189[/TD]
[TD]0.05610860385[/TD]
[TD]0.1808491003[/TD]
[TD]0.04650289189[/TD]
[TD]0.09739059923[/TD]
[TD]0.03598991827[/TD]
[/TR]
[TR]
[TD]theta[/TD]
[TD]0.1008966567[/TD]
[TD]0.05195943592[/TD]
[TD]0.1756206919[/TD]
[TD]0.06327771511[/TD]
[TD]0.196719665[/TD]
[TD]0.0692328209[/TD]
[TD]0.1140703342[/TD]
[TD]0.05562641381[/TD]
[/TR]
[TR]
[TD]theta[/TD]
[TD]0.1038710376[/TD]
[TD]0.03587204967[/TD]
[TD]0.1577397582[/TD]
[TD]0.06798635849[/TD]
[TD]0.1628184577[/TD]
[TD]0.06706123199[/TD]
[TD]0.1142154974[/TD]
[TD]0.05581582762[/TD]
[/TR]
[/TABLE]

And this one is three sessions thinking of lists of things: [TABLE=“border: 1, cellpadding: 0, cellspacing: 0”]
[TR]
[TD]Wavelength[/TD]
[TD]AvgLtEar[/TD]
[TD]StdDevLtEar[/TD]
[TD]AvgLt[/TD]
[TD]StdDevLt[/TD]
[TD]AvgRt[/TD]
[TD]StdDevRt[/TD]
[TD]AvgRtEar[/TD]
[TD]StdDevRtEar[/TD]
[/TR]
[TR]
[TD]alpha[/TD]
[TD]0.5562515386[/TD]
[TD]0.136416809[/TD]
[TD]0.4116256586[/TD]
[TD]0.1214454701[/TD]
[TD]0.3772323489[/TD]
[TD]0.1185041929[/TD]
[TD]0.5887056427[/TD]
[TD]0.1158272143[/TD]
[/TR]
[TR]
[TD]alpha[/TD]
[TD]0.5984605196[/TD]
[TD]0.1101452147[/TD]
[TD]0.4287187207[/TD]
[TD]0.1319249949[/TD]
[TD]0.3829557648[/TD]
[TD]0.101728971[/TD]
[TD]0.6143920906[/TD]
[TD]0.09154856375[/TD]
[/TR]
[TR]
[TD]alpha[/TD]
[TD]0.5531643179[/TD]
[TD]0.1268527466[/TD]
[TD]0.4223026557[/TD]
[TD]0.1289953233[/TD]
[TD]0.3391431671[/TD]
[TD]0.09940068654[/TD]
[TD]0.5735562343[/TD]
[TD]0.1119991587[/TD]
[/TR]
[TR]
[TD]beta[/TD]
[TD]0.1433202388[/TD]
[TD]0.04750832431[/TD]
[TD]0.2075970961[/TD]
[TD]0.05753094883[/TD]
[TD]0.1871676999[/TD]
[TD]0.05812190289[/TD]
[TD]0.1167713236[/TD]
[TD]0.03897640794[/TD]
[/TR]
[TR]
[TD]beta[/TD]
[TD]0.1341577512[/TD]
[TD]0.04479316822[/TD]
[TD]0.2200587237[/TD]
[TD]0.06468441912[/TD]
[TD]0.1978852484[/TD]
[TD]0.05307824593[/TD]
[TD]0.1149758352[/TD]
[TD]0.03951896366[/TD]
[/TR]
[TR]
[TD]beta[/TD]
[TD]0.1362562722[/TD]
[TD]0.05292896299[/TD]
[TD]0.2204638271[/TD]
[TD]0.07000256265[/TD]
[TD]0.1935879713[/TD]
[TD]0.04257633241[/TD]
[TD]0.1166913674[/TD]
[TD]0.03448335219[/TD]
[/TR]
[TR]
[TD]delta[/TD]
[TD]0.1256038433[/TD]
[TD]0.1056452161[/TD]
[TD]0.1186496703[/TD]
[TD]0.05795226699[/TD]
[TD]0.1141691902[/TD]
[TD]0.05863366646[/TD]
[TD]0.1293931029[/TD]
[TD]0.09375207861[/TD]
[/TR]
[TR]
[TD]delta[/TD]
[TD]0.1043915682[/TD]
[TD]0.0565698046[/TD]
[TD]0.1068389159[/TD]
[TD]0.04847449597[/TD]
[TD]0.1119005189[/TD]
[TD]0.05488339021[/TD]
[TD]0.08935133245[/TD]
[TD]0.04588694811[/TD]
[/TR]
[TR]
[TD]delta[/TD]
[TD]0.1356725505[/TD]
[TD]0.1018875746[/TD]
[TD]0.1182703203[/TD]
[TD]0.07036324873[/TD]
[TD]0.1117089799[/TD]
[TD]0.04859686401[/TD]
[TD]0.1271154374[/TD]
[TD]0.1137159915[/TD]
[/TR]
[TR]
[TD]gamma[/TD]
[TD]0.05342617396[/TD]
[TD]0.02070444133[/TD]
[TD]0.105106289[/TD]
[TD]0.04142557336[/TD]
[TD]0.1771908942[/TD]
[TD]0.05536910234[/TD]
[TD]0.04623790657[/TD]
[TD]0.0216964541[/TD]
[/TR]
[TR]
[TD]gamma[/TD]
[TD]0.04330056623[/TD]
[TD]0.01848531279[/TD]
[TD]0.0886117039[/TD]
[TD]0.03197481233[/TD]
[TD]0.1504598348[/TD]
[TD]0.05721498369[/TD]
[TD]0.03919164056[/TD]
[TD]0.01607222636[/TD]
[/TR]
[TR]
[TD]gamma[/TD]
[TD]0.04545024978[/TD]
[TD]0.01738448116[/TD]
[TD]0.09882971373[/TD]
[TD]0.03162866316[/TD]
[TD]0.2155639335[/TD]
[TD]0.06960630355[/TD]
[TD]0.04659287154[/TD]
[TD]0.01960361894[/TD]
[/TR]
[TR]
[TD]theta[/TD]
[TD]0.1209384506[/TD]
[TD]0.05035070018[/TD]
[TD]0.1568734605[/TD]
[TD]0.04492048337[/TD]
[TD]0.1437592999[/TD]
[TD]0.04081044971[/TD]
[TD]0.118548976[/TD]
[TD]0.04547066162[/TD]
[/TR]
[TR]
[TD]theta[/TD]
[TD]0.119935241[/TD]
[TD]0.04463134201[/TD]
[TD]0.1558804437[/TD]
[TD]0.06264107688[/TD]
[TD]0.1567404072[/TD]
[TD]0.05319941449[/TD]
[TD]0.1429112546[/TD]
[TD]0.05618320364[/TD]
[/TR]
[TR]
[TD]theta[/TD]
[TD]0.1290941802[/TD]
[TD]0.05305600363[/TD]
[TD]0.1410177409[/TD]
[TD]0.04622597132[/TD]
[TD]0.1398873442[/TD]
[TD]0.05203699021[/TD]
[TD]0.1359943851[/TD]
[TD]0.05514591426[/TD]
[/TR]
[/TABLE]

At least in these runs, I’m seeing distinguishable differences in Alpha measured by the two sensors on the left. …less of a difference on the right. Some other minor differences in other wavelengths…

Is this a reasonable approach to take? Or are there better ways to distinguish the two states?

And how consistent are these measurements likely to be? Building a machine learning model on these could be interesting, but if they’re going to change drastically from day to day (or between sessions within a day), that’ll make these difficult to work with. The fact that the Calm app needs to calibrate before every session indicates that these measurements are likely to fluctuate a lot.


#5

[B]nekrodezynfekator [/B]

We only use EEG data in our algorithm.


#6

[B]paulb[/B], could you elaborate on how your algorithm works? That would greatly help those of us who are trying to build software to use the Muse.


#7

Thats our secret sauce, so I cant give you any info on that. But we are working hard to make it easier to program with the Muse and you will see new announcements about this when that stuff is ready. In the meantime, I’ve just posted some info that will get you started on creating brain-computer interfaces:


#8

Thanks for this, quite a bit of info here. And thanks for posting your data a well, kdeus.


#9

kdeus,
I am just wondering that how can I get the data like yours .
my data is like this
1413387395.000000, /muse/eeg, 820.8452, 834.005, 829.07007, 832.36005
1413387395.000000, /muse/eeg, 824.13513, 829.07007, 825.78015, 822.4902
1413387395.000000, /muse/eeg, 817.55524, 832.36005, 824.13513, 822.4902
1413387395.000000, /muse/drlref, 1641935.5, 1641935.5
1413387395.016000, /muse/acc, 66.40636, 980.4703, 195.3128
1413387395.036000, /muse/acc, 66.40636, 980.4703, 195.3128
1413387395.036000, /muse/acc, 66.40636, 980.4703, 195.3128
when I just followed muse-lab tutorial


#10

Hi goutami,

If you mean that you want to see those powerbands that shows the various states of mind (calm, active, high activity, sleep, etc.), like Alpha, Beta, Gamma, Delta, Theta …
that kdeus is working on, them you need to add another parameter to muse-io ( --dsp ), like bellow:

muse-io.exe --osc osc.udp://localhost:5000 --dsp

After that you can see these signals available to be plotted on muse-lab, and you can also see it also in the converted CSV files that you are doing with muse-player.

HTH, Eduardo.


#11

Hi Eduardo,

I tried like >
muse-io --osc osc.udp://localhost:5000 –osc –bp-path / muse/dsp/elements --dsp
i am getting the data like this

1415977686.418000, /muse/acc, -31.25005, 980.4703, 199.21906
1415977686.543000, /muse/acc, -27.343792, 980.4703, 199.21906
1415977686.543000, /muse/drlref, 1638709.6, 1641935.5
1415977686.543000, /muse/acc, -27.343792, 980.4703, 199.21906
1415977686.543000, /muse/eeg, 789.5906, 835.65, 807.68536, 815.9103
1415977686.543000, /muse/eeg/quantization, 1, 1, 1, 1
1415977686.543000, /muse/eeg, 807.68536, 840.58496, 804.3954, 824.13513
1415977686.543000, /muse/eeg, 810.9753, 850.45483, 802.7504, 819.2002
1415977686.543000, /muse/dsp/elements/low_freqs, 0.0, 0.0, 0.0, 0.0
1415977686.543000, /muse/dsp/elements/alpha, 0.3856804, 0.32899714, 0.39313152, 0.3658574
1415977686.543000, /muse/dsp/elements/beta, 0.20667319, 0.2165887, 0.12608473, 0.24632938
1415977686.543000, /muse/dsp/elements/delta, 0.198221, 0.24984759, 0.23442882, 0.14635968
1415977686.543000, /muse/dsp/elements/gamma, 0.113038704, 0.13072042, 0.09725045, 0.07373064
1415977686.543000, /muse/dsp/elements/theta, 0.096386716, 0.073846154, 0.14910448, 0.1677229
1415977686.543000, /muse/dsp/elements/horseshoe, 1.0, 1.0, 1.0, 1.0
1415977686.543000, /muse/dsp/elements/is_good, 1, 1, 1, 1
1415977686.543000, /muse/dsp/blink, 0
1415977686.543000, /muse/dsp/elements/jaw_clench, 0
1415977686.543000, /muse/dsp/elements/touching_forehead, 1
1415977686.543000, /muse/dsp/elements/raw_fft0, -1.1689781, -0.764995, -0.73513305, -0.9359783, -0.20842257, -0.89539284,

I tried what you mentioned, result is also almost same kind of data
I tried with different different signals> create
I wonder how i can get only the alpha, beta,delta,gamma and theta data.


#12

Hi goutami,

There isn’t an option for muse-io to only send out alpha/beta/theta etc. messages. With whatever program you are using to receive the OSC messages from muse-io, you should be able to separate out the different messages by their paths (the path being the /muse/dsp/whatever part of the message). It’s up to the programmer writing the application that receives the messages to decide what to do with them. So, even though you may be receiving some messages that you are not interested in, you can just ignore them and focus on the ones you do care about.

MuseLab does this, as you can see when you select the different OSC paths to graph in a visualizer, for instance. Furthermore, the various formats that MusePlayer can save Muse data in also make it easy to separate out different types of data by their OSC paths in your programming language of choice.

Here’s a really simple example. If you just wanted to quickly and simply record only OSC messages like that to a file, you could run muse-io with the --dsp flag and then do something like this in bash:

oscdump 5000 | grep 'alpha\|beta\|theta\|gamma\|delta' > my_file.txt

#13

Hi Tom,

 I filtered my data like below- but i am not sure that is this right ?

1415977686.543000, /muse/dsp/elements/alpha, 0.3856804, 0.32899714, 0.39313152, 0.3658574
1415977686.543000, /muse/dsp/elements/beta, 0.20667319, 0.2165887, 0.12608473, 0.24632938
1415977686.543000, /muse/dsp/elements/delta, 0.198221, 0.24984759, 0.23442882, 0.14635968
1415977686.543000, /muse/dsp/elements/gamma, 0.113038704, 0.13072042, 0.09725045, 0.07373064
1415977686.543000, /muse/dsp/elements/theta, 0.096386716, 0.073846154, 0.14910448, 0.1677229
1415977686.543000, /muse/dsp/elements/alpha, 0.3931312, 0.3144644, 0.34867996, 0.37140083

not by using grep,
because getting message
’grep’ not recognized as internal or external command

but i would like to know even after setting the time range there is no time in my data.


#14

Hi goutami,

You don’t need osdcump or grep to filter data. You are using muse-player to convert your recorded muse files to CSV, so you can use muse-player 's own parameter ( -i ) to filter any data you want, like this:

muse-player.py -f myrecfile.muse -C mytextfile.CSV -i alpha beta gamma delta theta

and your CSV file will have only lines with that words in the messages path.

And the first column is the timestamp of each line when the file was recorded (it is minutes since 01/01/1970 00:00:00, and microseconds within that minute).
Put your first value in a cell of a Excel worksheet and the formula bellow in the next cell - you can see that it was recorded about 2 hours before you posted your question above :slight_smile:

=DATE(1970;1;1)+(A2/86400) [TABLE=“align: left, border: 1, cellpadding: 0, cellspacing: 0, width: 268”]
[TR]
[TD]TIMESTAMP[/TD]
[TD]DATE & TIME[/TD]
[/TR]
[TR]
[TD=“align: right”]1415977686,543000[/TD]
[TD=“align: right”]11/14/2014 15:08:07[/TD]
[/TR]
[/TABLE]

Good luck !


#15

Hi Eduardo,

Thank you, i tried another way with my prog. but i will try this too.
I was wondering about how to coordinate this data with emotions.
As I saw everybody is getting the alpha, beta, delta, theta, and gamma data within 0.0… to 0.5…mcvolts(I guess)
How are they going to be compared with 0.5 Hz to 42Hz of emotions range.
Any clue?