EEG INTERFACE MODULE FOR COGNITIVE ASSESSMENT THROUGH NEUROPHYSIOLOGIC TESTS

Authors

  • Kundan Lal Verma Department of Electronics, DDU Gorakhpur University, Gorakhpur
  • Amit Kumar Jaiswal Cognitive Brain Lab, National Brain Research Centre, Gurgaon
  • Manish Mishra Department of Electronics, DDU Gorakhpur University, Gorakhpur
  • Anil Kumar Shukla Amity Institute of Telecom Engineering & Management, Amity University, Noida

Keywords:

Cognitive Assessment, EEG, Brain, Neurophysiologic Tests, EEG Device, Brain Computer Interface

Abstract

The cognitive signal processing is one of the important interdisciplinary field came from areas of life sciences, psychology, psychiatry, engi-neering, mathematics, physics, statistics and many other fields of research. Neurophysiologic tests are utilized to assess and treat brain injury, dementia, neurological conditions, and useful to investigate psychological and psychiatric disorders. This paper presents an ongoing research work on development of EEG interface device based on the principles of cognitive assessments and instrumentation. The method proposed engineering and science of cogni-tive signal processing in case of brain computer in-terface based neurophysiologic tests. The future scope of this study is to build a low cost EEG device for various clinical and pre-clinical applications with specific emphasis to measure the effect of cognitive action on human brain.

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References

Cortes, C., Vapnik, V. (1995).Support-vector network. Machine Learning, 20, 273-297. doi:10.1109/ISSNIP.2004.1417517

Dietrich A., Kanso R. (2010). A Review of EEG, ERP, and Neuroimaging Studies of Creativity and Insight. Psychological Bulletin 136 (5), 822–848.

Gualtieri, C.T., Johnson L.G. (2006). Reliability and validity of a computerized neurocognitive test battery, CNS Vital Signs. Archives of Clinical Neuropsychology, 21(7), 623–643.

Hamid, N.H.A., Sulaiman N., Aris S.A.M., Murat Z.H. (2010). Evaluation of human stress using EEG power spectrum. IEEE Int Coll Sig Proc Apps, 263-266.

Heffley, E., Foote B. (1985). PEARL II: Portable Laboratory Computer System for Psycho-physiological Assessment Using Event Related Brain Potentials. Neurobehavioral Toxicology and Teratology, 7, 399-407.

Kim, M-K. , Kim, M. (2013). A Review on the Computational Methods for Emotional State Estimation from the Human EEG. Computational and Mathematical Methods in Medicine, 2013, Article ID 573734, http://dx.doi.org/10.1155/2013/573734

Knoll, A., Wang, Y., Chen, F., Xi, J., Ruiz, N., Epps, J., Zarjam, P. (2011) Measuring Cognitive Workload with Low Cost Electroencephalogram. Human Computer Interaction-Interact 2011, lecture Notes in Computer Sciences, 6949, 568-571.

Ladner, R.E. (2008). Access and Empowerment: Commentary on Computers and People with Disabilities. ACM Transactions on Accessible Computing, 1, 1-5.

Lakshmi, M. R. (2014). Survey on EEG Signal Pro-cessing Methods. International Journal of Advanced Research in Computer Science and Software Engineering, 4(1), 84-91.

Lisetti, C., Nasoz, F. (2004). Using Noninvasive Wearable Computers to Recognize Human Emotions from Physiological Signals. EURA-SIP Journal on Applied Signal Processing, 11, 1672-1687.

Lotte, F., Congedo, M., Lécuyer, A., Lamarche, F., & Arnaldi, B. (2007). A review of classification algorithms for EEG-based brain–computer interfaces. Journal of neural engineering, 4.

Mohandas, K. P., Gerropati, M. (2003). Artificial Neural Networks for Classification of EEG signals for Brain Computer Interface. In Pro-ceedings of the FAE Symposium, 1-6.

Napoli, A., Barbe, M., Darvish, K., & Obeid, I. (2012). Assessing traumatic brain injuries using EEG power spectral analysis and instantaneous phase.In Engineering in Medicine and Biology Society (EMBC), 2012 Annual International Conference of the IEEE (pp. 4692-4695). IEEE.

Schuhfried, G. Vienna Test System, Psychological Assessment, retrieved from http://www.schuhfried.com

Sharma, N., Gobbert, M.K. (2010). A Comparative Evaluation of MATLAB, OCTAVE, FREEMAT, and SCILAB for Research and Teaching. Department of Mathematics and Statistics, University of Maryland, Baltimore County, Technical Report HPCF–2010–7.

Sulaiman, N. Taib, M. N., Lias, S., Murat, Z. H., Aris, S. A. M., Hamid, N. H. A. (2011). EEG-based Stress Features Using Spectral Centroids Technique and k-Nearest Neighbor Classifier. Paper presented at UkSim: 13th International Conference on Computer Modeling and Simulation, 69–74.

Tong, S., Bezerianos, A., Paul, J., Zhu Y., Thakor, N. (2002). Nonextensive entropy measure of EEG following brain injury from cardiac arrest. Physica A: Statistical Mechanics and its Applications 305(3–4), 619–628.

Verma, K.L., Jaiswal, A.K., Chandel, S.S.(2012). Sta-tistical Methods of Bio-Physiological Data Processing. ISBN 978-3-659-31141-3, Germany, Lambert Academic Publishing.

Yin, Y., Cao, J. (2011). Analyzing the EEG Energy of Quasi Brain Death using MEMD. APSIPA ASC 2011 Xi’an.

IJCRSEE

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Published

2014-12-20

How to Cite

Lal Verma, K., Kumar Jaiswal, A., Mishra, M., & Kumar Shukla, A. (2014). EEG INTERFACE MODULE FOR COGNITIVE ASSESSMENT THROUGH NEUROPHYSIOLOGIC TESTS. International Journal of Cognitive Research in Science, Engineering and Education (IJCRSEE), 2(2), 61–66. Retrieved from https://www.ijcrsee.com/index.php/ijcrsee/article/view/151

Issue

Vol. 2 No. 2 (2014): International Journal of Cognitive Research in Science, Engineering and Education (IJCRSEE)

Section

Original Research

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