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Passive vs. Active Electrode - Slew-Rate Problem New
It was brought to our attention that a rumour is buzzing around among EEG researchers that there may be an inherent noise problem with active electrodes. The source of this rumour is a paper published by Sarah Laszlo et al. in 2014 (see A direct comparison of active and passive ampliﬁcation electrodes in the same ampliﬁer system). In this paper, the authors compare the performance of one particular brand (not BioSemi) active electrodes with passive electrodes, both connected to a conventional EEG amplifier (also not BioSemi). Both active and passive electrodes setups were tested with low (2 kOhm) and high (50 kOhm) electrode-to-skin impedances.
The authors find that the total noise (250 Hz bandwidth) for passive electrodes is higher with high electrode impedance, whereas the total noise with the active electrodes is equal (and nearly as low as with passive electrodes with low impedance) for both low and high electrode impedances. However, they also determine that the number of sweeps needed to achieve a reliable ERP result is higher with high electrode impedances for both the active and passive setups. Note though, that the number of required sweeps did not show significant differences between active and passive setups.
The paper starts to derail when the authors attempt to explain why in the tests with high skin-to-electrode impedances, the number of sweeps required for reliable ERP is similar for active and passive setups, instead of a lower number of sweeps for the active case as they would expect on the basis of the better total noise results. So although the active electrodes do not perform any worse than passive electrodes, they basically wonder why the performance is not better than it is.
A logical first step in analyzing the above discrepancy would have been to analyze the noise spectra (for example a relatively small band of extra noise with frequencies around the ERP waveform would explain that the total noise number is hardly influenced whereas the number of required sweeps would certainly be affected). Instead, the authors choose to introduce a hypothesis without any support by measurements or references. The authors suggest that the amplifier in the used active electrodes has a slew rate that is limited to such an extent that it causes distortion of the ERP wave, leading to a higher required number of sweeps than would be expected on the basis of the total noise figures.
It is certainly true that slew rate limitations can lead to signal distortion. The effect usually plays a role only with large (several volts) signals at high (several MHz) frequencies. Suggesting slew rate limitations as a problem for EEG signals (amplitudes in the hundreds of uV range and frequencies below a few hundred Hz) sounds far fetched.
How extreme the hypothesis actually is, can be illustrated by some numbers. The paper shows ERP results with a bandwidth of 250 Hz and an amplitude of 6 uV. The required slew rate to acquire this waveform without distortion is approx. 0.01 V/s (min SR = 2*pi*f*Vpk-pk, see for example the Wikipedia page on Slew Rate). Now consider the standard BioSemi ActiveTwo EEG system with active electrodes. At 16 kHz sample rate, this system can acquire a 3 kHz, 0.5 Vpk-pk wave without significant distortion. The complete system (active electrodes, post-amplifier, analog-to-digital converter) therefore has a slew rate of at least 10,000 V/s (or 0.01 V/us in the more usual notation). The standard BioSemi active electrodes are also applied in a high-frequency version of the ActiveTwo system with 262 kHz sample rate. The setup accurately acquires a maximal sine wave of 80 kHz, 0.5 Vpk-pk, leading to a slew rate estimate for the standard BioSemi active electrodes of at least 0.2 V/us. This is a factor of 20 million faster than in the Laszlo hypothesis. Refer to the following result for a 20 kHz, 40 mVpk-pk input signal acquired with standard BioSemi electrodes and a 262 kHz sample rate version of the ActiveTwo system (the dots indicate sample points at 3.8 us intervals). Note the fast rise and fall times of the square wave edges indicating good slew rate performance (the plot shows rise/fall times of approx 10 us, this is a limitation for step responses of the decimation filter in the ADC).
Designers of EEG systems, with or without active electrodes, all make use of a relatively small pool of quite similar Op-amps (building blocks for amplifier circuits) and ADCs provided by a handful of semiconductor manufacturers. These components all offer slew rates far beyond what is minimally required for EEG signals. I am therefore quite sure that our competitors can present results similar as found for the BioSemi setup. This is supported by the input range and bandwidth figures published by various manufacturers. While I would not hesitate to spread doubts about performance aspects of products by BioSemi's competitors, the limited slew rate argument is so unlikely for any currently available EEG system (with or without active electrodes) that I would never consider using it.
In other words: it is doubtful that the particular (non BioSemi) active electrode evaluated in the paper has a slew rate with a factor of 20 million slower than the BioSemi active electrode. Anyway, such an extremely slow slew rate would have been immediately apparent in the waveforms generated by relatively large and fast artifacts in the EEG signal, such as eyeblinks. In addition, the phenomenon would have been straight forward to measure by connecting the active electrodes to a signal generator. The paper does not indicate that any attempt was made to verify the hypothesis with measurements. Given that the particular active electrodes as tested in the paper are quite widespread under EEG researchers, it is hard to imagine that a slew rate limitation of 0.01 V/s would have remained unnoticed for years among these researchers.
The authors make things worse by stating that slew rate limitations are more severe for amplifiers "at very low output impedance". They even refer to BioSemi to support this claim. In reality, BioSemi never has made such a statement and with good reason: it simply is not true. There are numerous amplifier designs with high slew rate and low output impedance, the BioSemi active electrode as presented above is just an example.
The last sentence of the paragraph is the most bizarre. While it is, of course, true that passive electrodes do not have the slew rate limitations associated with amplifier circuitry, the authors overlook that the input stage of an EEG amplifier for passive electrodes also has high input impedance, low output impedance and slew rate limitations. In terms of amplifier noise, input and output impedances and slew rate, the only principle difference between the active and the passive setup is the location of the first amplifier stage. The statement that "slew rate [..] is simply not an issue for passive electrodes" is therefore surprising. In reality, both active electrodes and input stages for passive electrodes have the same slew rate limitations, although as outlined above, it is unlikely that any EEG system (active or passive) using currently available electronic components will present a problem in this respect. Instead, the authors suggest that slew-rate limitation is an inherent problem encountered with all active electrodes (regardless of brand and design), whereas the effect is totally eliminated in all amplifiers for passive electrodes.
To summarize: the paper presents an unlikely hypothesis that is not verified and not supported by any valid reference. The authors fail to do the simple measurements or numerical evaluation that would have readily falsified the hypothesis. Nevertheless, they find themselves qualified to apply this questionable hypothesis not only to the particular active electrode evaluated in the paper but to the active electrode design in general. In effect, they disqualify all active electrodes, including the successful BioSemi design, and confuse the scientific discussion about the merits of the active electrode principle. It is peculiar to see that such an unjustified accusation, that may affect legitimate manufacturers who put a lot of effort in improving EEG acquisition systems, can make it through the review process of Journal of Neuroscience Methods.
This is an article published by Coen Metting van Rijn, PhD, director, BioSemi, 5-December 2019
FAQ: How shall I disinfect EEG caps and electrodes and what kind of disinfection solution shall I use after each recording sessions? New
Soap is a lipophilic substance, meaning that it has the ability to chemically bind to fatty/oily substances. The weakest link of the novel SARS-CoV-2 (a.k.a. COVID-19 virus) is the lipid (fatty) bilayer membrane. Due to this fact, soap will resolve the fatty outer layer of the virus and it becomes inactive. We advise cleaning the head-caps with warm water and soap. A shampoo optimized for delicate fabric is advised, such as Ivory Shampoo (which can be purchased from our online shop: https://shop.neurospec.com/ivory-electrode-cap-shampoo-750ml), or similar. When cleaning, use a soft brush to clean and degrease the inside of the electrode holders.
According to literature, alcohol (ethanol or isopropanol) makes the virus inactive within 30 seconds. The electrodes can be submerged in alcohol without damage. Hence, submerging the electrodes for five minutes in alcohol should be the most effective way to prevent COVID-19 infections. Furthermore, the U.S. Food and Drug Administration (FDA) recommends using alcohol-based disinfectant solutions with greater than 60% ethanol or 70% isopropanol.
ActiView Online Cap Maps New
The ActiView Online Cap Maps allow you to view the offsets of your online connected ActiveTwo through your browser on your desktop computer and even mobile devices. With coloured offsets, you will be able to easily identify the quality of your connected electrodes.
Simply download and install the latest version (current version 8.12) of ActiView, connect your ActiveTwo with plugged in electrode sets and navigate to Offset > Upload to Cloud in the ActiView software.
The offsets are streamed via a secured connection to the BioSemi servers. The offsets are then made available here.
After loading ActiView Online Cap Maps enter your device ID (found in About/Configure > Server ID) and select desired electrode layout (16, 32, 64 or 128 layout available). After saving these settings, offsets are displayed in different colours. Green indicates low offset values, purple high offset values and grey indicates that CMS/DRL is not connected.
If you wish to check multiple ActiveTwos simultaneously, simply open an additional private browser window and set the appropriate device ID and electrode layout.
How to run ActiView from BioSemi on macOS New
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Gems of the BESA.ini file New
BESA Research offers a myriad of hidden features and settings which cannot easily be changed through the user interface. These default settings are hidden in a text file which can be found in your Public Documents folder, in sub-folder ./BESA/Research_7_1. In that directory, there is a file called BESA.ini which holds several settings that can help boost usability and productivity. Before changing any of these settings, ensure to make a copy of the file in case you wish to restore the initial settings.
Below is an excerpt of useful settings - the full set of parameters is available in the BESA help file, in the section on Special Topics, or, alternatively on the Wiki page http://wiki.besa.de/index.php?title=The_Initialization_File:_BESA.ini.
- The section [Defaults] holds settings that will be applied when you open a continuous recording for the first time. Some things that you may want to adjust are:
- DisplayedTime=10 [time shown on the screen in seconds]
- ScpScale=50 [scaling of EEG signals in microvolts]
- ParallelComputing=On [defines if parallel computing during extensive computation should be used or not. Switch off if the computer overheats during parallel computing.]
- ColoredWaveforms=On [scalp waveforms are (not) colored according to region]
- NotchFilter=50 [notch filter center frequency [Hz] ]
- NotchFilterStatus=Off [notch filter is off, set=On if you want to use as default]
- The section [Montage] holds the default montage that will be applied the first time you press the button Rec, Vir, Src, or Usr in the control ribbon at the top. Note that pressing the button again will show a popup with all options. Values that can be adjusted are:
- Source=25s [apply source montage 25s when pressing the button Src]
- Recorded=Original Recording [apply original recording montage when pressing the button Rec]
- Virtual=Triple Banana [apply the virtual Triple Banana montage when pressing the button Vir]
- User=CA25 [apply the user montage CA25 when pressing the button Usr]
- The section [Mouse] is not part of the BESA.ini file by default. However, if you create it, you can change the way that the mouse wheel works during paging and cursor moving:
- WheelPagingDownRight=Off [this will page backward if you turn the mouse wheel towards you – by default, it will page forward upon turning the mouse wheel towards you]
- WheelCursorDownRight=Off [this will move the cursor position forward if you turn the mouse wheel towards you – by default, the cursor position will move backward upon turning the mouse wheel towards you]
Webinar: Combining Virtual Reality (VR) and Eye Tracking (ET) New
Take you Eye Tracking research to the next level with integrated Virtual Reality (VR)! With BIOPAC you can combine both worlds and add biosignal acquisition recording all into one complete system.
During this upcoming webinar, BIOPAC will present a complete and integrated solution that includes eye tracking analytics, the ability to link physiological data, and implementation of user interaction. A user-friendly interface combined with examples enables even novices to get started right away! Learn how eye tracking analytics tools can save considerable time and effort with data interpretation:
- How the system works
- How you can use eye tracking to add interactivity in VR
- How to perform a stimulus presentation paradigm in VR
- How to work with 360 videos and images
- Eye tracking data analysis—heatmaps, gaze replay, fixation times, gaze intersection, etc.
- Integration with physiological data and sensors with examples, measuring cognitive workload, stress, and more
Eliminate the hard choices associated with configuring software and hardware to get into VR and eye tracking. Join us on January 27 at 17:00 (CET) to learn how our unique solution can help anyone get started!
SOME OF OUR RANGE OF PRODUCTS
BIOSEMI - ActiveTwo
BIOSEMI - bringing EEG and ERP to a new level with the original Active Electrode and ActiveTwo EEG Amplifier.
BESA - Research 7.0
BESA - the leading innovators in digital EEG, MEG and MRI software for research and clinical applications.
SHIMADZU - LABNIRS
SHIMADZU - Next-Generation Optical Brain-Function Imaging with functional near-infrared spectroscopy (fNIRS)