[Eeglablist] nonstationarity issue

[Kathleen Van Benthem]

Hello List,
I have looked for a BCILAB forum but couldn't find one, so I have posted
here.

We have recently had a paper rejected where we classified low and high
mental workload states (a match to sample :easy vs hard task) using *BCILAB*,
the CSP (between 7 and 30 hz) and LDA .  We analyzed epochs based on the
onset of standard and deviant tones- also labelled as per the low and high
workload task conditions (participants wore earbuds and were told to ignore
the tones while completing the task).

We used a 128 channel system and decided to use all the channels in the
classification analysis.  We achieved a sig. better than chance
classification rate for all four different classification schemes (based on
what tone type and epoch segment we used).  We counterbalanced between
subjects regarding whether they started with the easy or hard condition of
the task.

The rejection was based on nonstationarity issues.  Apparently the dynamics
of the brain are so wildly fluctuating that you cannot draw conclusions any
time you introduce a time series.  My thought is that I should demonstrate
that the mean and variance of some other features of the EEG signal were
*not* significantly different between conditions- eg. eye artifacts.  Thus
when I show differences between my workload conditions for other EEG
features I can make a case that it was the task manipulation that likely
resulted in the differences in EEG signals, and our classification rates
were not simply a result of nonlinear and nonstationary EEG.

Questions;
Has anyone else run across this problem with nonstationarity in repeated
measures?  and how did you solve it?   Are there methods within *BCILAB* to
deal with time series issues?

ps. we could not alternate between easy hard easy hard easy hard etc...
within subjects or you would lose the stable mental workload state you were
trying to induce.  For example, in real life if you were engaged in a task
that went from easy to hard repeatedly in a short time frame, IMHO, you
would essentially be in a high workload state the entire time.

Any and all suggestions are welcome!


​
Kathleen Van Benthem Ph.D., AGE-WELL HQP
​Instructor FYSM 1607C​
Postdoctoral Fellow, ACE​ Lab
VSIM Building, Carleton University
kathy.vanbenthem at carleton.ca
Phone:  (613) 355-2515
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