[Eeglablist] which cap electrode is the best?

[Philip Michael Zeman]

Hi Ki-Young Jung


I have had experience with 3 styles of caps

(1) the standard 10-20 system EEG cap available for the last 20 years. Put the cap on the head, secure the chin strap, put electrode paste in the holes in the cap, attach the electrodes.

(2) a geodesics quick-cap design available for approximately the last 5 years. The cap is an expandable wire grid of electrodes.  Easy set-up is advertised.

(3) Biosemi Active-II electrode cap system available for approximately the last 5 years.  Put the cap on the head, secure the chin strap, put electrode paste in the holes in the cap, attach *ACTIVE LOW NOISE* electrodes.


I highly recommend either option (1) or option (3).  My experience with option (2) is that while attaching the electrodes to a study participants head is fast, the electrode contacts are generally not particularly good.  The result is that the data are quite noisy.

Since it sounds like you already have your EEG system, you will not be able to use option (3) so that leaves option (2).  This my opinion colored by my experience.  I suggest you also consider the opinions of other researchers, in addition to mine, before you make your final decision.


Regards,

Philip Michael Zeman





  ----- Original Message ----- 
  From: Jung Ki-Young 
  To: eeglablist at sccn.ucsd.edu 
  Sent: Tuesday, August 25, 2009 9:54 PM
  Subject: [Eeglablist] which cap electrode is the best?


  Dear EEGLABer

  I am going to use cap electrode for 64 channel EEG system (Grass Telefactor).

  There are several models of cap electrode such as, easy cap, quick cap, electro-cap etc.

  I'd like to know which cap electrode is the best in terms of impedence value, easy accessibility, and durability something.

  Thanks a lot in advance for your help.

   

  Ki-Young Jung

  ---------------------------------------------------------------------------
  Jung, Ki-Young, MD, PhD, Associate Professor
  Department of Neurology, Korea University Medical Center, 
  Korea University College of Medicine 

  KU Computational Neuroscience Research Lab (http://eeg.re.kr)
  ---------------------------------------------------------------------------

   



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