International Journal of Applied Engineering Research

Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

Brain-Computer Interface: Electroencephalography as an Emerging Tool for BCI Applications


Affiliations
1 Symbiosis Institute of Technology, Symbiosis International University, Pune, Maharashtra, India
     

   Subscribe/Renew Journal


Electroencephalography (EEG) is a diagnostic tool by which electrical potentials are measured with electrodes placed on the surface of the scalp. Analysis of brain waves gives better understanding of the processes in the human brain. It not only helps to diagnose brain disorders, monitor the depth of anesthesia, locate the tumor in the brain, epilepsy seizers etc but also gives important information about the cognitive process in the human brain. The electroencephalogram is one of the useful biosignals to detect the human behavior and human emotions. Establishing a new communication channel for physically immobilized people to interact with the outside world with the help of brain waves is attracting the attention of the researchers and scientists world over. This paper discusses the fundamentals of Electroencephalography, measurement of brainwaves, analysis, feature extraction techniques, classifiers and various applications and future research areas in human psychology and cognitive development using EEG.

Keywords

Electroencephalography, Brain-Computer Interface, Feature Extraction, Classifier, Epilepsy
User
Notifications
Font Size


  • Canli, T., and Amin, Z. Neuroimaging of emotion and personality: Scientific evidence and ethical considerations. Brain and Cognition (50: 414-431, 2002.

  • Ray Adams, Gisela Susanne Bahr and Benigno Moreno, Brain Computer Interfaces: Psychology and Pragmatic Perspectives for the Future, HMI Seminar on BCI, 2009.

  • J.R. Woplow, N. Birbaumer, D.J. McFarland, G. Pfurtscheller, T.M. Vaughan. Braiin-computer interfaces for communication and control. Clinical Neurophysiology, 113(6):767-791, 2002.

  • Freeman W J, Holmes M D, Burke B C and Vanhatalo S Spatial spectra of scalp EEG and EMG from awake humans Clin. Neurophysiol. 114 1053–68, 2003.

  • Srinivasan R, Nunez P L and Silberstein R B., Spatial filtering and neocortical dynamics: estimates of EEG coherence IEEE Trans. Biomed. Eng. 45 814–26, 1998.

  • Creutzfeldt O, Houchin J. Neuronal basis of EEG waves. In: Remond A, ed. Handbook of Electroencephalography and clinical Neurophysiology. Vol 2. Amsterdum: Elsevier; 71-79, 1974.

  • Joseph I. Sirven, John M. Stern, Atlas of Video-EEG Monitoring, Tata McGraw Hill, dition; page 3, 2011.

  • M. Teplan, Fundamentals of EEG Measurement, Measurement Science Review, Volume 2, Section 2, 2002.

  • G.V. Kondraske. Neurophysiological measurements. In: J.D. Bronzino ed. Biomedical Engineering and Instrumentation, pp. 138-179, PWS Publishing, Boston, 1986.

  • J. D. Bronzino.. Principles of Electroencephalography. In: J.D. Bronzino ed. The Biomedical Engeneering Handbook, pp. 201-212, CRC Press, Florida, 1995.

  • T.W. Picton et. al. Guidelines for using event-related potentials to study cognition: Recording standards and publication criteria. Psychophysiology 37, 127-152, 2000.

  • H.H. Jasper. The ten-twenty electrode system of the International Federation.

  • Electroencephalography and Clinical Neurophysiology, 371-375, 1958.

  • H.N. Nagel.. Biopotential Amplifiers. In: J.D. Bronzino ed. The Biomedical Engeneering Handbook, pp. 1185-1195, CRC Press, Florida, 1995.

  • D. Brunet, G. Young et al. Electroencephalography, Guidelines for Clinical Practice and Facility Standards, College of Physicians and Surgeons of Ontario, Canada, 2000.

  • Esteller, R., Echauz, J., Tcheng, T., Litt, B., Pless, B. Line length: an efficient feature for seizure onset detection. Proc 23rd Intl Conf Engr Med Biol Soc; 2: 1707-1710, 2001.

  • Carrie JR. A hybrid computer techniquefor detecting sharp EEG transients. Electroencephalogr Clin Neurophysiol. 33(3): 336-338, 1972.

  • Staba, R.J., Wilson, C.L., Bragin, A., Fried, I., Engel, J., Jr., Quantitative analysis of high frequency oscillations (80-500Hz) recorded in human epileptic hippocampus and entorhinal cortex. J Neurophysiol.; 88(4): 1743-1752, 2002.

  • Adeli H, Zhou Z, Dadmehr N. Analysis of EEG records in an epileptic patient using wavelet transform. J Neurosci Methods.; 123(1): 69-87, 2003.

  • R.D. Bickford.. Electroencephalography. In: Adelman G. ed. Encyclopedia of Neuroscience, Birkhauser, Cambridge (USA), 371-373, 1987.


Abstract Views: 736

PDF Views: 0




  • Brain-Computer Interface: Electroencephalography as an Emerging Tool for BCI Applications

Abstract Views: 736  |  PDF Views: 0

Authors

Meena Laad
Symbiosis Institute of Technology, Symbiosis International University, Pune, Maharashtra, India

Abstract


Electroencephalography (EEG) is a diagnostic tool by which electrical potentials are measured with electrodes placed on the surface of the scalp. Analysis of brain waves gives better understanding of the processes in the human brain. It not only helps to diagnose brain disorders, monitor the depth of anesthesia, locate the tumor in the brain, epilepsy seizers etc but also gives important information about the cognitive process in the human brain. The electroencephalogram is one of the useful biosignals to detect the human behavior and human emotions. Establishing a new communication channel for physically immobilized people to interact with the outside world with the help of brain waves is attracting the attention of the researchers and scientists world over. This paper discusses the fundamentals of Electroencephalography, measurement of brainwaves, analysis, feature extraction techniques, classifiers and various applications and future research areas in human psychology and cognitive development using EEG.

Keywords


Electroencephalography, Brain-Computer Interface, Feature Extraction, Classifier, Epilepsy

References