Active-TwoActiveTwo is a powerful high-resolution biosignal acquisition system that incorporates some revolutionary concepts.  Active electrode technology is just one of the significant innovations in the ActiveTwo system.  By placing active electronics within millimeters of the actual electrode contact, ActiveTwo virtually eliminates the need to prepare the scalp before applying electrodes.  This can cut measurement preparation time by an estimated 15 -30 minutes for most laboratories!

ActiveTwo can also be equipped with additional sensors for respiration, skin conductance, temperature, plethysmograph (pulse) and other parameters.  An optional isolated analog input box makes it possible to acquire almost any type of signal synchronously with the signals sampled by the ActiveTwo A/D box.

As an added benefit, ActiveTwo comes with powerful data acquisition software developed in National Instruments’ LabView.  We provide the compiled software so you do not need to own LabView, and you do not need to be a programmer to operate the system.  For those laboratories with programming resources, the source code is provided so that you can add any special features that you may need.

Each ActiveTwo system is built upon a basic set of items known as the Base System.

Base Components:

  • A/D interface box with no amplifier/converter modules (no channels installed)
  • USB 2.0 interface box with USB 2.0 cable
  • Rechargeable battery unit – 2 each
  • Battery charger with A/C adapter
  • ActiView Data Acquisition Software

Typical Additional Components:

  • Amplifier/Converter modules – up to 32 8-channel modules per A/D interface box
  • Active Electrodes – A/D interface can accommodate up to 256 active electrodes in sets of 32 on high-density connectors and/or up to 8 with individual leads and touch proof connectors
  • Head Caps – with electrode holders and position labels
  • Active Electrodes – with individual leads and touch-proof connectors
  • Trigger Interface Cable
  • Optional Sensors – for galvanic skin response, respiration, temperature, or pulse/plethysmograph

Other Options:

  • Custom-configured auxiliary inputs on the A/D interface box.  These inputs are appropriate for use with battery-powered or self-powered signal sources such as a condenser microphone or a photocell.
  • Analog input box with up to 16 bipolar / 32 monopolar channels and fiber-optic coupling to main A/D interface box

You May Also Need:

  • Consumable supplies
  • Post-processing software
  • Electrode position measurement hardware/software
  • Stimulus delivery software
  • Behavioral response measurement hardware
  • Installation and training

In addition, you will need computers, monitors and interface cables/devices to streamline operation between operator area and subject chamber.  We find that most customers prefer to source these items through their normal channels.  If you prefer to have us provide these items, we can do so if it is more convenient for you.


NeurOne EEG system (2)

Maybe.  There is no way the EEG system can harm the TMS system, but there are some TMS systems that are essentially useless with some EEG systems.  You need a a suitable combination of features in each to be successful at all in using TMS with EEG.

Desirable attributes:

  • Coils designed specifically for use with EEG will have a cable that exits the coil tangential to the head surface so that the coil does not pass close by EEG electrodes and cables.
  • A coil to be used with EEG should be passively cooled, since active cooling by means of a fan will induce electromagnetic interference in the EEG.
  • The TMS recharge mechanism should be designed to avoid inducing electromagnetic artifacts in the EEG.
  • The TMS system should be shielded so that no more than 3 milligauss of electromagnetic interference from the TMS system’s power supply reaches the electrodes and cables.
  • The TMS system should be able to produce a TMS pulse in response to an input trigger with a low and predictable latency.  Long, but especially unpredictable latency in responding to an input trigger will result in TMS artifacts that are difficult to impossible to remove from the EEG.

Aside from attributes of the TMS system, there are also important considerations regarding the EEG system in this relationship. See the other FAQ entry on that topic.

No.  There are a few special attributes that are required and others that are desirable.

Required attributes:

  • Inputs should be protected so that they are not damaged by the TMS pulse
  • Frequency response on all channels should be from DC on the low end, since AC coupled inputs will “ring” when presented with a large voltage transient like the TMS artifact

Desirable attributes:

  • Electrodes should have a low-profile so that the TMS coil can be placed as near the head as possible, maximizing penetration depth and current delivery
  • It is important for the EEG system to be able to trigger the TMS system at precisely the same phase with respect to the EEG samples each time a stimulus is delivered.  This will improve the performance of artifact removal algorithms

Aside from these attributes of the EEG system, there are also important considerations regarding the TMS system in this relationship. See the other FAQ entry on that topic.

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This is a sample list of some recent publications.  For more, visit Google Scholar.

Chobert, J., François, C., Velay, J.-L., & Besson, M. (2012). Twelve months of active musical training in 8-to 10-year-old children enhances the preattentive processing of syllabic duration and voice onset time. Cerebral Cortex, 24(4), 956–967.
Raczaszek-Leonardi, J. (2009). Symbols as constraints: the structuring role of dynamics and self-organization in natural language. Pragmatics & Cognition, 17(3), 653–676.
DaSalla, C. S., Kambara, H., Sato, M., & Koike, Y. (2009). Single-trial classification of vowel speech imagery using common spatial patterns. Neural Networks, 22(9), 1334–1339.
Muzik, J., & Hana, K. (2009). Real-time BSPM processing system. In 4th European Conference of the International Federation for Medical and Biological Engineering (pp. 377–380). Springer.
Igual, J., Llinares, R., Guillem, M., & Millet, J. (2006). Optimal localization of leads in atrial fibrillation episodes. In Acoustics, Speech and Signal Processing, 2006. ICASSP 2006 Proceedings. 2006 IEEE International Conference on (Vol. 2, pp. II–II). IEEE.
François, C., Chobert, J., Besson, M., Schön, D., Raczaszek-Leonardi, J., Moreno, S., … Ziegler, J. C. (201220092008). On-line orthographic influences on spoken language in a semantic task. Cerebral Cortex, 21(1), 169–179.
McCleery, J. P., Ceponiene, R., Burner, K. M., Townsend, J., Kinnear, M., & Schreibman, L. (2010). Neural correlates of verbal and nonverbal semantic integration in children with autism spectrum disorders. Journal of Child Psychology and Psychiatry, 51(3), 277–286.
François, C., Chobert, J., Besson, M., & Schön, D. (2012). Music training for the development of speech segmentation. Cerebral Cortex, 23(9), 2038–2043.
Rączaszek-Leonardi, J. (2010). Multiple time-scales of language dynamics: An example from psycholinguistics. Ecological Psychology, 22(4), 269–285.
Moreno, S., Marques, C., Santos, A., Santos, M., Castro, S. L., & Besson, M. (2008). Musical training influences linguistic abilities in 8-year-old children: more evidence for brain plasticity. Cerebral Cortex, 19(3), 712–723.
Jiang, Y., Farina, D., & Dössel, O. (2009). Localization of the origin of ventricular premature beats by reconstruction of electrical sources using spatio-temporal map-based regularization. In 4th European Conference of the International Federation for Medical and Biological Engineering (pp. 2511–2514). Springer.
Dossel, O., Bauer, W., Farina, D., Kaltwasser, C., & Skipa, O. (2006). Imaging of bioelectric sources in the heart using a cellular automaton model. In Engineering in Medicine and Biology Society, 2005. IEEE-EMBS 2005. 27th Annual International Conference of the (pp. 1067–1070). IEEE.
Zavala-Fernandez, H., Kania, M., Maniewski, R., & Janusek, D. (2012). Evaluation of blind source separation methods for noise reduction in BSPM recorded during exercise. In Computing in Cardiology (CinC), 2012 (pp. 593–596). IEEE.
Schulze, W. H. (2015). ECG imaging of ventricular activity in clinical applications (Vol. 22). KIT Scientific Publishing.
Bruns, H., Samol, A., Stolz, P., Schawe, T., Wenzelburger, F., Tjan, T., … Vahlhaus, C. (2005). Does body surface potential mapping (bspm) predict functional recovery in chronic ischemic myocardium after revascularization? In Computers in Cardiology, 2005 (pp. 207–210). IEEE.
Srivastava, V., & Prasad, D. (n.d.). CAPTURING ECG SIGNALS BY ICA.
Carr, K. W., White-Schwoch, T., Tierney, A. T., Strait, D. L., & Kraus, N. (2014). Beat synchronization predicts neural speech encoding and reading readiness in preschoolers. Proceedings of the National Academy of Sciences, 111(40), 14559–14564.
White-Schwoch, T., Carr, K. W., Thompson, E. C., Anderson, S., Nicol, T., Bradlow, A. R., … Kraus, N. (2015). Auditory processing in noise: A preschool biomarker for literacy. PLoS Biology, 13(7), e1002196.
Kornilov, S. A., Landi, N., Rakhlin, N., Fang, S.-Y., Grigorenko, E. L., & Magnuson, J. S. (2014). Attentional but not pre-attentive neural measures of auditory discrimination are atypical in children with developmental language disorder. Developmental Neuropsychology, 39(7), 543–567.
Zavala-Fernandez, H., Kania, M., Janusek, D., & Maniewski, R. (n.d.). Application of Independent Component Analysis for Rejection of Motion Artefact in BSPM Recorded During Exercise.
Snyder, A. C., & Foxe, J. J. (2010). Anticipatory attentional suppression of visual features indexed by oscillatory alpha-band power increases: a high-density electrical mapping study. Journal of Neuroscience, 30(11), 4024–4032.
Zhu, Y., Shayan, A., Zhang, W., Chen, T. L., Jung, T.-P., Duann, J.-R., … Cheng, C.-K. (2008). Analyzing high-density ECG signals using ICA. IEEE Transactions on Biomedical Engineering, 55(11), 2528–2537.
Ries, A. J., Touryan, J., Vettel, J., McDowell, K., & Hairston, W. D. (2014). A comparison of electroencephalography signals acquired from conventional and mobile systems. Journal of Neuroscience and Neuroengineering, 3(1), 10–20.
Kappenman, E. S., & Luck, S. J. (2010). The effects of electrode impedance on data quality and statistical significance in ERP recordings. Psychophysiology, 47(5), 888–904.
Hajcak, G., & Olvet, D. M. (2008). The persistence of attention to emotion: brain potentials during and after picture presentation. Emotion, 8(2), 250.
Wiswede, D., Koranyi, N., Müller, F., Langner, O., & Rothermund, K. (2012). Validating the truth of propositions: behavioral and ERP indicators of truth evaluation processes. Social Cognitive and Affective Neuroscience, 8(6), 647–653.
Dandekar, S., Privitera, C., Carney, T., & Klein, S. A. (2012). Neural saccadic response estimation during natural viewing. Journal of Neurophysiology, 107(6), 1776–1790.
Murray, M. M., Brunet, D., & Michel, C. M. (2008). Topographic ERP analyses: a step-by-step tutorial review. Brain Topography, 20(4), 249–264.
Nolan, H., Whelan, R., & Reilly, R. (2010). FASTER: fully automated statistical thresholding for EEG artifact rejection. Journal of Neuroscience Methods, 192(1), 152–162.
Maeoka, H., Matsuo, A., Hiyamizu, M., Morioka, S., & Ando, H. (2012). Influence of transcranial direct current stimulation of the dorsolateral prefrontal cortex on pain related emotions: a study using electroencephalographic power spectrum analysis. Neuroscience Letters, 512(1), 12–16.
Dandekar, S., Ding, J., Privitera, C., Carney, T., & Klein, S. A. (2012). The fixation and saccade p3. PloS One, 7(11), e48761.
Haas, A., Revil, A., Karaoulis, M., Frash, L., Hampton, J., Gutierrez, M., & Mooney, M. (2013). Electric potential source localization reveals a borehole leak during hydraulic fracturing. Geophysics, 78(2), D93–D113.
Hajcak, G., & Foti, D. (2008). Errors are aversive: Defensive motivation and the error-related negativity. Psychological Science, 19(2), 103–108.