A Multiresolution Analysis in .NET

Integration QR Code 2d barcode in .NET A Multiresolution Analysis
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A Multiresolution Analysis
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Figure 19.9. Lower delta activity for all (8) subjects in response to known and unknown stimuli for
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Trial 2 Just Faces. ERPs are detected on electrode O2 and wavelet decomposition is computed using a Daubechies order 17 (db17) wavelet.
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the trials continue and as noise is removed, as in the shift from Trial 4 ( Noise02 ) to Trial 5 ( Noise03 ), the CWT plots in response to known stimuli regain a form much like that found in Trial 2 ( Just Faces ). The plots obtained in this section follow a logical progression: As face discernibility decreases, the shape of the CWT plot changes and resembles that of the response to unknown stimuli, then as the face becomes more discernible, the CWT plot takes a form close to that of the original. Based on the results in this section, the three-dimensional CWTs used have shown the potential to highlight differences in familiarity and may be used as a stand-alone tool or in conjunction with other methods to determine familiarity. Finally, the superimposed lower delta activity among the responses to known stimuli shows strong phase coherence along the N400f and P600f peaks. The lower delta activity among the responses to unknown stimuli is very apparently out of phase. The observation of N400f and P600f peaks in the lower delta activity of the responses to known stimuli supports the assertions that the peaks are associated with familiar face recognition as stated in Eimer [6] and Bentin and Deouell [3]. Furthermore, the observation of these peaks in the lower delta activity in responses to known stimuli and not in responses to unknown stimuli supports the assertion in this study that the DWT is suitable to determine familiar face recognition.
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CONCLUSION
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Multiple methods were used to investigate familiar face recognition in the time-scale domain. Trends were found using discrete and continuous wavelet transform analysis. In the case of the DWT, the lower delta activity displays marked phase coherence
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References
in response to known stimuli, while the lower delta activity for the responses to unknown stimuli are out of phase. Another encouraging trend is that shown in the three-dimensional CWT plots. As face discernibility decreases, the shape of the CWT plot changes and resembles that of the response to unknown stimuli, then as the face becomes more discernible, the CWT plot takes a form close to that of the original. The same trend of discernibility is seen based on the ratio of the CWT magnitude in response to known stimuli compared to those in response to unknown stimuli. The trends described above could potentially be applied to many real-world situations, not the least being security and law enforcement. A system can be visualized that would utilize the methods in concert to determine familiar face recognition. A subject is tted with an electrode cap and ERPs are calculated, an eight-level DWT is performed on the ERPs, and the lower delta activity phase coherence is determined. Next, the CWT is taken for the responses to known and unknown stimuli, and the ratio of the maximum magnitudes between 700 and 800 ms is calculated. The DWT and CWT output could then be analyzed separately by an investigator or could be used as input to a trained neural network that would then output a determination of known or unknown.
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REFERENCES
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1. M. Small, Asymmetrical evoked potentials in response to face stimuli, Cortex 19, 441 450, 1983. 2. S. Bentin, T. Allison, E. Perez, A. Puce, and G. McCarthy, Electrophysiological studies of face perception in humans, J. Cognitive Neurosci. 8, 551 565, 1996. 3. S. Bentin and L. Y. Deouell, Structural encoding and identi cation in face processing: ERP evidence for separate mechanisms, Cognitive Neuropsychol. 17(1/2/3):35 54, 2000. 4. B. Rossion, I. Gauthier, M. J. Tarr, P. Despland, R. Bruyer, S. Linotte, and M. Crommelinck, The N170 occipito-temporal component is delayed and enhanced to inverted faces but not to inverted objects: An electrophysiological account of face-speci c processes in the human brain, Neuroreport 11(1):69 74, 2000. 5. M. J. Herrmann, M. C. Ehlis, H. Ellgring, and A. J. Fallgatter, Early stages (P100) of face perception in humans as measured with event-related potentials (ERPs), J. Neural Transmission 112(8):1073 1081, 2005. 6. M. Eimer, Event-related brain potentials distinguish processing stages involved in face perception and recognition, Clin. Neurophysiol. 111:694 705, 2000. 7. O. A. Rosso, M. T. Martin, A. Figliola, K. Keller, and A. Plastino, EEG analysis using wavelet-based information tools, J. Neurosci. Methods 153: 163 182, 2006. 8. V. J. Samar, A. Bopardikar, R. Rao, and K. Swartz, Wavelet analysis of neuroelectric waveforms: A conceptual tutorial, Brain and Language 66:7 60, 1999. 9. V. J. Samar, K. P. Swartz, and M. R. Raghuveer, Multiresolution analysis of event-related potentials by wavelet decomposition, Brain and Cognition 27: 398 438, 1995. 10. B. Boashash, H. Carson, and M. Mesbah, Detection of seizures in newborns using time-frequency analysis of EEG signals, in Proceedings of the IEEE Workshop on Statistical Signal and Array Processing, Pocono Manor, PA, 2000, pp. 564 568. 11. H. E. Hanrahan, Extraction of features in auditory brainstem response (ABR) signals, COMSIG 90, Proceedings of the third South African Conference on Communications and Signal Processing, IEEE catalog number 90TH0314-5/90, 1990, pp. 61 66. 12. E. A. Bartnik, K. J. Blinowski, and P. J. Durka, Single evoked potential reconstruction by means of wavelet transform, Biolo. Cybern. 67: 175 181, 1991.
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