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.


Consumable supplies (2)

The inside diameter of adhesive ring should be about the same size as, or larger than, the diameter of the electrode contact.  The inside diameter of the adhesive ring should not be larger than the outer diameter of the electrode housing.  The outside diameter minus the center hole determines how much surface area contacts the skin, thus determining how tightly the adhesive will adhere to the skin.  Also, the outside diameter will limit how close you can place electrodes to one another and to other features, such as the eyes.

Using ActiveTwo as an example, the electrode contact on a flat-type electrode has a diameter of about 4.5 mm.  An adhesive ring with 5 mm id (center hole) would be  idea, but the 5×13 adhesives are rather expensive because they are manufactured in Europe.  We recommend using a 4×19 or a 4×12 adhesive ring.  The 4×12 is a good choice when placing the electrodes close to one another or close to the eyes for startle measurements.  The 4×19 is a good choice when you have plenty of room and the primary concern is how well the electrodes stick to the skin.

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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.