Automatic Interpretation of EEGs: Bibliography

Automatic Interpretation of EEGs
An excellent starting point for becoming an expert on interpreting EEGs is the bibliography found at the Professional Testing Corporation web site. The references below are resources we found useful in developing a basic understanding of EEGs:

  • Strayhorn, D. (2014). The Atlas of Adult Electroencephalography. EEG Atlas Online. Retrieved January 18, 2014 (available online here).

  • Tapsell, S. (2014). EEGucation. A YouTube Channel. Retrieved January 18, 2014 (available online here).
  • Aminoff, M. (2012). Aminoff's Electrodiagnosis in Clinical Neurology (p. 890). Waltham, Massachusetts, USA: Elsevier Health Sciences (available online here).

  • Sanei, S., & Chambers, J. A. (2008). EEG signal processing. (p. 312). Hoboken, New Jersey, USA: Wiley-Interscience.

  • Stern, J. M., & Engel, J. (2005). Atlas of EEG patterns. Philadelphia, Pennsylvania, USA: Lippincott Williams & Wilkins (available online through any library that has a Wolters Kluwer/Ovid subscription).

  • Tatum, W., Husain, A., Benbadis, S., & Kaplan, P. (2007). Handbook of EEG Interpretation. (Kirsch, Ed.) (p. 276). New York City, New York, USA: Demos Medical Publishing (available online at Brainmasters Technologies Inc.).
  • American Clinical Neurophysiology Society. (2006). Guideline 6: A Proposal for Standard Montages to Be Used in Clinical EEG (pp. 1-7). Milwaukee, Wisconsin, USA. Retrieved from

  • Bao, F. S., Gao, J.-M., Hu, J., Lie, D. Y.-C., Zhang, Y., & Oommen, K. J. (2009). Automated epilepsy diagnosis using interictal scalp EEG. Proceedings of the International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 6603-6607). Minneapolis, Minnesota, USA.

  • Shoeb, A. H., & Guttag, J. V. (2010). Application of machine learning to epileptic seizure detection. Proceedings of the International Conference on Machine Learning (ICML) (pp. 975-982). Haifa, Israel. Retrieved from

  • Wulsin, D. F., Gupta, J. R., Mani, R., Blanco, J. A., & Litt, B. (2011). Modeling electroencephalography waveforms with semi-supervised deep belief nets: fast classification and anomaly measurement. Journal of Neural Engineering, 8(3), 36015.


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