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Analysis of EEG Data EEG data has been studied
for over 130 years. However, in recent years, the sensing technology has
improved and has become much less expensive. The Digital Signal Processing
and Machine Learning algorithms have improved and the expectations from EEG data
analysis are nothing less but full interpretation of thoughts. Brain Machine
Interface (BMI) is expected to rely more on EEG rather than invasive sensing
for applications such as artificial limbs and certainly for monitoring of
pilots, drivers and other tasks which require intense concentration. We have developed
tools for robust modeling and analysis of EEG data and are applying it to
various clinical and research applications. This work is done in
collaboration with the Functional Brain Imaging Lab at the Souraski Medical Center headed by Prof. Talma Hendler. Recent Publications ·
A. Zhdanov, T. Hendler, L. Ungerleider, and N. Intrator Inferring functional brain states using temporal evolution of
regularized classifiers Computational Intelligence and
Neuroscience vol. 2007, Article ID 52609, 8 pages. ·
A. Zhdanov, T. Hendler, L. Ungerleider, and N. Intrator Machine Learning Framework for Inferring Cognitive State From Magnetoencephalographic (MEG) Signals Proceedings
of the International Conference on Cognitive Neurodynamics.
ICCN pp. 393-397, (2007) ·
Y. Hasson-Meir, A. Zhdanov, T. Hendler
and N. Intrator Inference of Brain Mental States from Spatio-temporal
Analysis of EEG Single Trials BIOSIGNALS, 2011 |
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