A. Keil

Publications (2)2.15 Total impact

• Article: Quantifying cognitive state from EEG using dependence measures.

  Bilal Fadlallah, Sohan Seth, Andreas Keil, José Principe
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  ABSTRACT: The exquisite human ability to perceive facial features has been explained by the activity of neurons particularly responsive to faces, found in the fusiform gyrus and the anterior part of the superior temporal sulcus. This study hypothesizes and demonstrates that it is possible to automatically discriminate face processing from processing of a simple control stimulus based on processed EEGs in an online fashion with high temporal resolution using measures of statistical dependence applied on steady-state visual evoked potentials. Correlation, mutual information, and a novel measure of association, referred to as generalized measure of association (GMA), were applied on filtered current source density data. Dependences between channel locations were assessed for two separate conditions elicited by distinct pictures (a face and a Gabor grating) flickering at a rate of 17.5 Hz. Filter settings were chosen to minimize the distortion produced by bandpassing parameters on dependence estimation. Statistical analysis was performed for automated stimulus classification using the Kolmogorov-Smirnov test. Results show active regions in the occipito-parietal part of the brain for both conditions with a greater dependence between occipital and inferotemporal sites for the face stimulus. GMA achieved a higher performance in discriminating the two conditions. Because no additional face-like stimuli were examined, this study established a basic difference between one particular face and one nonface stimulus. Future work may use additional stimuli and experimental manipulations to determine the specificity of the current connectivity results.
  IEEE transactions on bio-medical engineering 07/2012; 59(10):2773-81. · 2.15 Impact Factor
• Conference Proceeding: Estimation of instantaneous power in the EEG to assess brain connectivity with high temporal resolution

  JungPhil Kwon, S. Seth, A. Keil, J.C. Principe
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  ABSTRACT: This paper presents advanced signal processing algorithms to quantify brain connectivity with high temporal resolution using the electroencephalogram (EEG). The experimental paradigm exploits the visual cortex response to flickering images at a given frequency. The envelope of the EEG at the flickering frequency collected by 128 electrodes over the head quantifies the communication amongst the brain areas with high temporal resolution. This work proposes to use the empirical mode decomposition to find the flickering frequency and then use the Hilbert transform to estimate the instantaneous amplitude. A video of the topographical display of the instantaneous power over the array helps us visualize the exquisite communication that occurs during the stimulus presentation.
  Engineering in Medicine and Biology Society, 2009. EMBC 2009. Annual International Conference of the IEEE; 10/2009