Sensitivity Analysis of Hilbert Transform with Band- Pass FIR Filters for Robust Brain Computer Interface
|
|
- Rosamund Lee
- 5 years ago
- Views:
Transcription
1 Sensitivity Analysis of Hilbert Transform with Band- Pass FIR Filters for Robust Brain Computer Interface Jeffery Jonathan (Joshua) Davis (1,2) 1 Center for Large-Scale Integrated Optimization Networks University of Memphis, Memphis, TN 38152, USA 2 Embassy of Peace, Whitianga New Zealand Joshua_888@yahoo.com Robert Kozma Center for Large-Scale Integrated Optimization Networks Dept. Mathematical Sciences, University of Memphis Memphis TN USA rkozma@memphis.edu Abstract Transient cortical oscillations in the form of rapid synchronization-desynchronization transitions are key candidates of neural correlates of higher cognitive activity monitored by scalp EEG and intracranial ECoG arrays. The transition period is in the order of ms, and standard signal processing methodologies such as Fourier analysis are inadequate for proper characterization of the phenomenon. Hilbert transform-based (HT) analysis has shown great promise in detecting rapid changes in the synchronization properties of the cortex measured by highdensity EEG arrays. Therefore, HT is a primary candidate of operational principles of brain computer interfaces (BCI). Hilbert transform over narrow frequency bands has been applied successfully to develop robust BCI methods, but optimal filtering is a primary concern. Here we systematically evaluate the performance of FIR filters over various narrow frequency bands before applying Hilbert transforms. The conclusions are illustrated using rabbit ECoG data. The results are applicable for the analysis of scalp EEG data for advanced BCI devices. Keywords - Electrocorticogram, Hilbert Transform; Synchronization; Instantaneous Frequency; Analytic Amplitude; Analytic Phase; Cognition. I. INTRODUCTION Brains are the most complex substances in the known Universe. Neurophysiological processes underlying higher cognition and consciousness are intensively studied worldwide with many spectacular successes using fmri, ECoG, MEG, and EEG [1-5]. There is a need for more robust analysis of the filter properties on the outcome, in particular in BCI applications. The present study aims at better understanding of cortical processes and increasing the credibility and future dynamical development of this promising new research field. This work is a continuation of studies based on rabbit electrocorticogram (ECoG) experiments due to W. J. Freeman [1]. The present study helps to interpret the operation of brains without getting trapped into representational cognitivism as described by Dreyfus [6-7]. Instead, we work towards an embodied cognition model as a promising approach to deal with the complexities of cognition and consciousness [8-10]. This material is based upon work supported by the Science Foundation Program "Collaborative Research in Computational Neuroscience (CRCNS)" under Grant Number DMS Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation. In the present approach brains are viewed as nonlinear dynamical systems, which lead to successful characterization of learning in neural systems and to the interpretation of cognitive functions [11-17]. Dynamic brain models can provide the basis for adaptation and give an account of how the brain of an intentional animal can obtain meaningful information on the world [6, 18]. We are exploring paradigms that can shed light to the understanding of creativity, health, and peace in the context of behavioral responses and universal values for the progress of the individual and the society. In the dynamic brain approach, learning establishes an attractor landscape for each cortical area, in which the basins of attractions are shaped by experience. Each attractor in the landscape corresponds to a class of stimulus that the animals have learned to discriminate and each attractor is accessed by the arrival of a learned stimulus of that class. The mathematical and computational formulation is expressed in Freeman K (Katchalsky) sets [19-20]. In previous work our findings are based on experimental ECoG studies using intracranial electrode arrays implanted in rabbits [22-25]. Recently we presented results based on a comprehensive analysis of ECoG signals starting form 2Hz until 40Hz, covering delta, theta, alpha, beta, and gamma bands. This allowed us to move in the direction to overcome the limitations of previous works covering only specific frequency bands, such as gamma or theta band. In this work, we analyze the impact of changing filter order on the Analytic Amplitude (AA), the Analytic Phase (AP), and the Instantaneous Frequency (IF). We explore how much information is lost after applying FIR filters, and how filters of low order have an influence on phase displacements. This work extends on previous studies band-pass filter designs generating beating patterns and associated null spikes in the analytic signal domain [26]. The goal is to build a reliable foundation concerning the mathematical procedures we are applying as part of a methodology for further BCI research. We start with describing the rabbit ECoG experiments, followed by introducing the Hilbert transform-based signal processing approach. Next we describe the obtained results and discuss their relevance to the reliability of the mathematical procedures concerning FIR band pass filters. We
2 study the accuracy of the experimental parameters (both AA and IF) with the expected theoretical values derived from the center frequencies when applying the FIR band pass filters on both Rabbit and simulated data, to obtain a signal for each narrow band in order to verify that the filter is working properly in capturing the signal component of each particular band of interest. Our results point to the role of every band in the cognitive process. We point to some limitations in our analysis and outlines avenues to overcome them in the future research. II. DESCRIPTION OF ECOG DATA Local areas of sensory cortex of 1 cm in diameter generate broad-spectrum aperiodic waves of dendritic activity that have the same waveform. This spatial coherence is shown by the similarity of the waveforms of ECoGs that are recorded simultaneously from 8 8 epidural electrode arrays giving windows 6 6 mm 2 in width onto the olfactory, visual, auditory and somatomotory cortices [21]; see Fig. 1. In this work, only the experiments with visual cortex are presented. The spatial amplitude modulation (AM) of this coherent waveform in brief time segments gives a spatial pattern that is determined by the synaptic connectivity within each cortex. The connections and patterns change with the training of animals to identify significant stimuli. Evidence for the dynamic systems theory of chaotic self-organization in cortices comes from the results of classification of the spatial AM patterns. The ECoG segments coming from a sensory area give clusters of points, each of which corresponds to a response to a sample of the class of stimulus that the animal has learned to identify. The animals under classical conditioning learn discrimination, in which one stimulus is reinforced (CS+) and the other is not (CS ). III. ANALYTIC AMPLITUDE, PHASE ANALYSIS AND INSTANTANEOUS FREQUENCY OF EXPERIMENTAL DATA Here we expand on our findings regarding various frequency bands in multiple runs. As presented in other papers [28, 32], first we analyzed the 64-channel ECoG signals, performed preprocessing and filtering over them, and evaluated various statistical measures as specified below. Next, we evaluated the same statistical measures for the analytic signals calculated after Hilbert-transforming the preprocessed and filtered ECoG signals. The applied Hilbert transform methodology and the analytic signal construction followed the approach described in [26-27]. Informationtheoretic measures have been evaluated previously for the theta band using the same rabbit ECoG data [28]. In the present work we extend earlier studies toward a comprehensive study of a range of frequency bands. In all evaluations, we kept the frequency bandwidth constant at 2Hz, while shifting the center frequency of the filter stepwise at 2Hz increments. The considered frequency windows are summarized in Table I. In this case this was done for the 39 trials. TABLE I. FREQUENCY WINDOWS ANALYZED Frequency Band Windows (Hz) f Low f High Delta 2-4 Theta 4-6, 6-8 Alpha 8-10, Beta 12-14, 14-16, 16-18, 18-20, 20-22, 22-24, Low Gamma 26-28, 28-30, 30-32, 32-34, 34-36, 36-38, For this work we will summarize and present our results based on Narrow Band analysis within the broadband of Theta (4-8 Hz), Alpha (8-12 Hz), Beta (12-26 Hz), Gamma (26-40 Hz). The following quantities have been evaluated both for the band-passed ECoG and their analytic counterparts for the 39 trials: Signal amplitude (SA or AA): This could be either the amplitude (SA) of the ECoG signal or the analytic amplitude (AA) of the analytic signal. Standard Deviation in Space (STDx): The standard deviation calculated across the 64 channels for SA or AA, respectively. Instantaneous Frequency (IFx): Evaluated using the temporal derivative of the analytic phase (AP) across all 64 channels. This analysis requires a massive amount of data processing and visual exploration of each trial for each band. Here we present the results for the gamma band for 2 trials (first and last) as shown in Figure 1 (a, b) and Figure 2 (c, d) describing SA and AA characteristics respectively, to give you an initial impression of the work at hand. The instantaneous frequency has been calculated as the spatial average over all 64 individual channels. This means we have an average of the IF time series instead of 64 time series for each band. Note that in all figures the first half of the time series corresponds to the resting period prior the stimulus, which is administered in the form of a light flash at moment t=3s. The second part of the experiments after 3s represents the poststimulus response. The first and last 0.5s are omitted as those periods are used for the proper windowing. We have the following major observations based on Figs. 1-2: 1. SA and AA: There are oscillations during the whole time period, even during the resting period, but the oscillations show significant increase between 3-4s, i.e., immediately after the stimulus. In the case of SA, the increased amplitude is more visible at higher frequencies. For the AA, on the other hand, the increase is very prominent over all frequencies. 2. In the case of AA: We can observe a fine structure of the amplitude measure. Namely, there is an increase of AA between followed by a drop between seconds. In this last period we can observe a small increase between seconds, followed by a drop between seconds.
3 (a) accompanied by a drop in analytic frequency in the narrow band signals within Low Gamma, Beta, Alpha and Theta, something that can be observed in Figures 1 and 2 for Low Gamma. The onset and termination of these events is clearly observable in the trials, which vary within the window (3-4 s). At the same time, the two peaks in some cases are very close to each other, they may be almost merged; sometimes there are three peaks and occasionally four. However, based on the average, as well as other indicators like standard deviation, analytic phase and analytic frequency, we conjecture that the immediate post stimuli window (3-4 s) is reflecting a process for knowledge creation, consolidation and intentional behavior and that the process taking place in this window can be interpreted as a nonlinear, far from stability one, with four stages in it. These stages we call: Awe, Chaotic Exploration, Aha moment and Chaotic Integration and finally after the 4 th second a return to usual linear behavior. To make sure that our cognitive hypothesis rest on a sound mathematical foundation we feel compelled to test the reliability of the FIR band pass filtering procedure band by band, as well as, the limitation imposed by the Filter itself for the beginning and end of the filtered data. For these purpose we will present three initial tests: A test on a simple sinusoidal wave A test on a more complex sinusoidal wave A test on a theoretical complex sum of sinusoidal waves representing each frequency band in our analysis. (b) Figure 1. ECoG signals filtered over the Low Gamma (26Hz-40Hz). Temporal frequency band at constant 2Hz bandwidth segments; each plot displays the signal amplitude (SA), standard deviation of the signal, and the average frequency over the given narrow band in linear (omit reading log) coordinates; (a) trial #1, (b) trial # 39. Multiple curves on a given figure indicate the signals obtained over 7 bands of width 2Hz each within the Low Gamma. 3. STDx for SA and AA: Here the most important feature is that the overall STDx increases significantly between 3-4 seconds. This effect is clear in both SA and AA analysis. 4. IFx: We consider the absolute value of IF in linear and log10 coordinates. We observe a drop between seconds followed by an increase between seconds (with more dispersion). 5. AA and IFx relationship: As a general trend, we observe that wherever there is a drop in IFx there is an increase in AA and vice versa; this is in line with previous research [26-27]. Now we can confirm this as a general behavior over a broad frequency band. These graphs, together with the other 37 trials showed the presence of synchronization-desynchronization periods. We also observe that in the window just after stimuli (between 3-4 s) two periods of increased amplitude (peaks) appear, IV. HILBERT ANALYSIS OVER SEVERAL BANDS (Analytic Amplitude and Phase Relationship of ECoG Signals) We calculate the analytic signals V(t) after Hilberttransforming the 64-channel ECoG array data. The applied Hilbert transform methodology follows the approach described in [27]. The ECoG of each channel v j (t) (j=1,,64) is transformed to a time series of complex numbers, V j (t), with a real part, v j (t), and an imaginary part, u j (t), V j (t) = v j (t) + i u j (t), j = 1, 64, (1) Here the real part is the ECoG signal, while the imaginary part is the Hilbert transform of v j (t). We use MATLAB Hilbert function to produce u j (t). Sequences of steps give a trajectory of the complex vector V(t) composed of 64 complex values evolving in time. The vector length at each digitizing step, t, is the analytic amplitude:!!!!! =!!(!) +!!(!), (2) while the analytic phase is defined as the arctangent of the angle of the vector, also composed of 64 values evolving in time:
4 !! = 12 cos 2! 5! + 6 cos 2! 12!!! = 12 cos 2! 5! + 6 cos 2! 12! + 3 cos 2! 23! (c) Figure 3. Illustration shows the effect of the Band-Pass filter when applied to a simulated signal!! = 12 cos (2! 5!) with amplitude, A=12 and frequency, f=5, same as the center frequency of the FIR filter with parameters: FstopL = 3 Hz, FpassL = 4 Hz, FpassH = 6 Hz, and FstopH = 7 Hz and with the order of the filter N=281. (d) Figure 2. ECoG signals filtered over the Low Gamma (26Hz-40Hz). Temporal frequency band at constant 2Hz bandwidth segments; each plot displays the analytic amplitude (AA), standard deviation of the signal, and the average frequency over the given narrow band in linear (omit reading log) coordinates; (c) trial #1, (d) trial # 39. Multiple curves on a given figure indicate the signals obtained over 7 bands of width 2Hz each within the Low Gamma.!!! = tan!!!! (!)!! (!), (3) The Instantaneous Frequency vector, IF(t) general formula based on the analytic phase is as follows:!"!! = V.!!!!!!! =!!!!!!!!!!!!!, (4) INITIAL TESTS ON SINUSOIDAL WAVES USING THE FIR BAND PASS FILTER In this section we analyzed a set of generated sinusoidal signals, to test the FIR filters with data we know theoretically. Following we describe the different generated signals according to the general formula!! =!!!!!! cos (2!!!!):!! = 12 cos (2! 5!) Here, we show detailed results obtained for a band between 4-6 Hz. This helps to illustrate the most important features when applying FIR band pass filters before Hilbert analysis. In the next section we introduce results over a broad range of Narrow Band generated frequencies based both on the sinusoidal theoretical formula and the parameters of amplitude and frequency obtained with real Rabbit data. Band-pass filter is applied to the simulated signals on the theta band with parameters: FstopL = 3 Hz, FpassL = 4 Hz, FpassH = 6 Hz, and FstopH = 7 Hz. These filters are applied together with two different values for the order of the filter, N=281 and N=600, to show the effects of this parameter on the filtered data. Figure 3 shows a filtered simulated signal of the form,!! = 12 cos (2! 5!) where the order of the filter is N=281 and where we can observe two important features of this process: (1) the filter data FIR(xt) preserves the amplitude but is shifted or out of phase (almost antiphase), (2) the Analytic Amplitude is very distorted and unstable for the first and last half of a second respectively. We observe a drastic departure from A=12 to almost A=0.5 at the beginning of the first second. It is only after around 0.5s that it return to values around A=12 with slight oscillations. The last 0.5s displays a similar situation. These are clearly some of the expected effects of the FIR filter. Following we show how when we increase substantially the order of the FIR filter to N=600, we solve the issue of having the FIR(xt) out of phase with the original data (xt). However, this comes with a cost in a slight variation in amplitude (different than the oscillations observed in analytic amplitude) and longer periods of initial and final distortions and instabilities.
5 (a) (b) Figure 4. Illustration shows the effect of the Band-Pass filter when applied to the simulated signal,!! = 12 cos (2! 5!) + 6 cos (2! 12!) with compound amplitudes of, A1=12 and A2=6 and frequencies, f1=5 and f2=12, with FIR filter parameters: FstopL = 3 Hz, FpassL = 4 Hz, FpassH = 6 Hz, and FstopH = 7 Hz. (a) shows the results for N=281, (b) displays the results for N=600. Figure 4 shows a similar situation, however with a more complex signal containing the original component plus a new cosine wave,!! = 12!"# 2! 5! + 6 cos 2! 12! with two different orders for the filter N=281 and N=600. Figure 5. Illustration shows the effect of the Band-Pass filter when applied to,!! = 12 cos 2! 5! + 6 cos 2! 12! + 3 cos 2! 23!, the simulated signal with compound amplitudes of, A1=12, A2=6 and A3=3 and frequencies, f1=5, f2=12 and f3=23, with FIR filter parameters: FstopL = 3 Hz, FpassL = 4 Hz, FpassH = 6 Hz, and FstopH = 7 Hz and with the order of the filter N=600. We can also observe very marked oscillations in analytic amplitude values, between and around 6 and 18 due to the compound nature of the signal together with the effects of the selected parameters of the filter. These values are in the range of the sum of both amplitudes A1=12 and A2=6 of the different cosine components of the signal and the minimum which corresponds to A2=6. This behavior is preserved with the different orders of the FIR filter applied to the signal, both N=281 and N=600. However as we have stated before, it is clear that the initial conditions have a longer period of instability for N=600. The third signal we have analyzed for these section is an even more complex signal,!! = 12 cos 2! 5! + 6 cos 2! 12! + 3 cos 2! 23! with three cosine components. It is important to note that we just added again a new component with a lower amplitude and a higher frequency and consistently we applied the same filter with N=600 to show, in figure 5, that regardless of: (1) the added complexity, (2) some initial instabilities and (3) a slight variation in amplitude values of the original signal (xt), the FIR filter captures well the behavior of the frequency band of interest. In the next set of graphs we will show the analytic phase (wrapped and unwrapped), the instantaneous frequency (IF) and the histogram for the IF for each of the signals described above.
6 Figure 6. These Illustration shows four types of graphs: Wrapped Analytic Phase (top left), Unwrapped Analytic Phase (top right), Analytic or Instantaneous Frequency, IF (bottom left) and the Histogram of IF (bottom right). All of the above were calculated after Band-Pass filter has been applied to the simulated signal!! = 12 cos (2! 5!) with amplitude, A=12 and frequency, f=5, same as the center frequency of the FIR filter with parameters: FstopL = 3 Hz, FpassL = 4 Hz, FpassH = 6 Hz, and FstopH = 7 Hz and with the order of the filter N=281. This is to show with more clarity the reliability of the filter as well as its limitations. It is important to mention at this stage that a significant loss of precision in amplitude or a significant out of phase result when filtering data could alter dramatically our observations and conclusions when analyzing real data. Because for example if the onset of a stimuli is supposed to show at time t, the out of phase data would show it at time t+ε. Also if the analytic amplitude is our order parameter, then we should be very careful to preserve its values as close as possible after filtering. The two important things to mention in figure 6 are the instabilities observed at the beginning and at the end periods for the computed instantaneous frequency (IF) also reflected in the histogram, with an observed average frequency very close to five (5) as expected. Figure 7 (a) shows a similar behavior as Figure 6, however, the average IF is very close to five (5) as expected. We observe some small oscillatory departures from the expected frequency. These we conjecture is due to some of the limitations and sensibilities of the FIR filter, something we need to consider carefully when analyzing real data. In figure 7 (b), it is clear that the instabilities are much larger in magnitude for the initial period. The average computed IF is still near the expected value of five (5) and it is showing similar yet more attenuated oscillatory behavior than the one in figure 7 (a). This is showing us the trade offs when increasing N from N=281 to N=600. Figure 8 shows a similar behavior as already observed in the rest of the above analysis. Basically, the FIR band pass filter captures relatively well the behavior of the instantaneous frequency with some trade offs in terms of: initial and end instabilities and amplitude values when changing the order of the filter. (a) (b) Figure 7. These Illustration shows four types of graphs: Wrapped Analytic Phase (top left), Unwrapped Analytic Phase (top right), Analytic or Instantaneous Frequency, IF (bottom left) and the Histogram of IF (bottom right). All of the above were calculated after Band-Pass filter has been applied to the simulated signal,!! = 12 cos (2! 5!) + 6 cos (2! 12!), with compound amplitudes of, A1=12 and A2=6 and frequencies, f1=5 and f2=12, with FIR filter parameters: FstopL = 3 Hz, FpassL = 4 Hz, FpassH = 6 Hz, and FstopH = 7 Hz. (a) shows the results for N=281, (b) displays the results for N=600. However, the refinement of the filtering process can be improved as shown in the following graphs in Figure 9, where we have plotted both IF and Analytic Amplitude (AA) for the most complex signal,!! = 12 cos 2! 5! + 6 cos 2! 12! + 3 cos 2! 23!, under three different scenarios of filter parameters. The general results obtained are shown in the following table. TABLE II. PASS BAND SCENARIOS Scenario Pass Band Wide Middle Narrow FstopL FpassL FpassH FstopH Mean IF Mean AA
7 achieve a very close precision in relation to both the expected frequency and amplitude of f=5 and A=12. Finally, it is important to mention that to properly observe this oscillations we had to remove half a second from the beginning and the end of the process, and that it would had been even more accurate to remove a whole second in the beginning of the process which is about the time were we start to observe stable values for both IF and AA. Figure 8. Illustration of four types of graphs: Wrapped Analytic Phase (top left), Unwrapped Analytic Phase (top right), Analytic or Instantaneous Frequency, IF (bottom left) and the Histogram of IF (bottom right). All of the above were calculated after Band-Pass filter has been applied to,!! = 12 cos 2! 5! + 6 cos 2! 12! + 3 cos 2! 23!, the simulated signal with compound amplitudes of, A 1=12, A 2=6 and A 3=3 and frequencies, f 1=5, f 2=12 and f 3=23, with FIR filter parameters: FstopL = 3 Hz, FpassL = 4 Hz, FpassH = 6 Hz, and FstopH = 7 Hz and with the order of the filter N=600. Though both the means of IF and AA are very similar for the three different scenarios, they show very different results when we take in consideration the oscillatory patterns imposed by the filtering process together around the mean center frequency of f 1 =5 and its associated amplitude A 1 =12 for that band, as shown in figure 5. Figure 9. These Illustration shows two types of graphs: Instantaneous Frequency, IF (top), Analytic Amplitude, AA (bottom). All of the above were calculated after Band-Pass filter was applied to,!! = 12 cos 2! 5! + 6 cos 2! 12! + 3 cos 2! 23!, for N=600 and with FIR filter parameters: (a) Wide (dashed line), (b) Narrow (dotted line), (c) Middle (solid line) bandwidth as shown in table II. These three scenarios show that as we narrow the Band- Pass filter s band and we keep the centered frequency f=5 the oscillations diminish. Our graphs also show that we can VI. DISCUSSION AND CONCLUSIONS This work introduced the complexities associated with applying FIR filters of different orders with different bandwidth parameters on both real and simulated data as a means to give a warning about the sensitivities observed both in AA, AP and IF after Hilbert transforming the data. When we increase the order of the filter we observe a more accurate behavior of both the AA and the IF in relation to the expected theoretical values imposed on simulated data. However, this comes with the cost of loosing reliable data at the beginning and the end periods of both the computed AA and IF and the amount of data lost depends on the order of the filter. We also observe some oscillatory behaviors on the IF that can be solved by narrowing the Low and High, Stop and Pass parameters. All of this has to be taken in consideration when choosing a filter for real data and some calibration might be needed together with a proper understanding of the nature of the data and the objectives of the analysis. Our methodology is based on the Hilbert transform after FIR band pass filter for a broad-range of frequencies, including gamma, beta, alpha, and theta bands. A set of indexes and ratios using the AA and AP are employed to detect the onset of synchronization of neural activity across large cortical areas during high-level cognitive functions. In order to properly filter data before applying Hilbert transform, detailed analysis is required to fine tune to a greater degree the accuracy of the filters in terms of both order and bandwidth, together with a clear measurement of the trade-offs in terms of precision and loss of data. The results introduced in this work give a suitable approximation satisfying the needs of interpreting ECoG data. The understanding we derived here, give us insight into how to approach future analysis of scalp EEG data with potential for BCI applications. ACKNOWLEDGMENTS Experimental data have been recorded by Dr. John Barrie at the Freeman Neurophysiology Laboratory, Division of Neuroscience, Department of Molecular & Cell Biology, the University of California at Berkeley. These data are available upon request. This work has been supported in part by NSF CRCNS Grant Number DMS REFERENCES [1] Freeman, W. J., Consciousness, Intentionality and Causality, In: Reclaiming Cognition: The Primacy of Action, Intention and Emotion. Núñez, R., Freeman, W. J. (eds.) Bowling Green, OH. Imprint Academic
8 [2] Del Cul A.; Baillet S.; Dehaene S. Brain dynamics underlying the nonlinear threshold for access to consciousness, PLOS Biology, 5(10), , [3] Seth, A.K.; Dienes Z.; Cleeremans A. Measuring consciousness: relating behavioral and neurophysiological approaches, Trends. In Cogn. Sci. 12(8), , [4] He B.J.; Raichle M. E., The fmri signal, slow cortical potential and consciousness, Trends in Cogn. Sci. 13(7), , [5] Koch C.; Tononi G. A Test for Consciousness, Sci. American, 304(6), 44-47, [6] Dreyfus H. L, How Representational Cognitivism Failed and is Being Replaced by Body/World Coupling, in After Cognitivism: A Reassessment of Cognitive Science and Philosophy, Leidlmair K (ed), pp , Springer, [7] Dreyfus, H.L. Why Heideggerian AI failed and how fixing it would require making it more Heideggerian, Artificial Intelligence, 171 (18), , [8] Thompson, E., Varela, F. Radical Embodiment: Neural Dynamics and Consciousness, Trends in Cognitive Sciences, 5, , [9] Brooks, R. Flesh and Machine: How Robots Will Change Us, New York: Pantheon, [10] Barsalou, L.W. Grounded Cognition, Annual Rev. Psychol., 59: , [11] Freeman, W.J., Mass action in the nervous system. New York, NY: Academic Press, [12] Skarda, C., Freeman, W.J. How Brains make Chaos to Make Sense of the World, Behav. Brain Sci., 10(2), , [13] Nunez, P.L., Neocortical Dynamics and Human EEG Rhythms, Oxford University Press, Oxford, [14] Kelso JAS Dynamic Patterns: The Self Organization of Brain and Behavior. Cambridge MA: MIT Press, [15] Wright, J.J., D.J.T. Liley, Dynamics of the brain at global and microscopic scales: Neural networks and the EEG, Behav. Brain Sci. 19, , [16] Freeman, W.J., R. Kozma, Local-global interactions and the role of mesoscopic (intermediate-range) elements in brain dynamics, Behav. Brain Sci. 23(3 ), , [17] Werner G (2007) Metastability, criticality, and phase transitions in brains and its models, BioSystems, 90, , [18] Kozma, R., Huntsberger, T., Aghazarian, H., Freeman, W.J., Intentional Control for Planetary Rover SRR, Adv. Robotics, 22(12), , [19] Kozma, R., W.J. Freeman, Chaotic resonance: methods and applications for robust classification of noisy and variable patterns, Int. J. Bifur. Chaos 11 (6), , [20] Freeman WJ, Erwin Freeman K-set. Scholarpedia, 3(2): 3238, [21] Barrie, J.M., W.J. Freeman, M. Lenhart, Modulation by discriminative training of spatial patterns of gamma EEG amplitude and phase in neocortex of rabbits, J. Neurophysiol. 76, , [22] Freeman, W.J., J.M. Barrie, Analysis of spatial patterns of phase in neocortical gamma EEGs in rabbit, J. Neurophysiol 84, , [23] Kozma, R., W.J. Freeman, Classification of EEG Patterns Using Nonlinear Neurodynamics and Chaos, Neurocomputing, 44-46, , [24] Kozma R, Freeman WJ Intermittent spatio-temporal desynchronization and sequenced synchrony in ECoG signals, Chaos 18, [25] Kozma, R., Neural Correlates of Awareness in Brains and Man-Made devices, Proc. IEEE Int. Conf. Awareness Science and Technology (icast2011), IEEE Press, pp , [26] Freeman, W.J. "Deep analysis of perception through dynamic structures that emerge in cortical activity from self-regulated noise, Cogn Neurodyn (2009) 3: [27] Freeman WJ Origin, structure, and role of background EEG activity. Part 1. Analytic amplitude. Clin. Neurophysiol. 115: , [28] Davis, J.J. Joshua, Kozma, R. Analysis of Phase Relationships in ECoG Using Hilbert Transform and Information-Theoretic Measures, Proc. IEEE/INNS Int. Joint Conf. Neur. Networks, IJCNN2012/ WCCI2012, Brisbane, Australia, June 10-15, [29] Freeman, W.J. Definition of state variables and state space for brain-machine interface, Cognitive Neurodynamics, 1, 85-96, [30] Atmanspacher, H., Scheingraber, H. Pragmatic information and dynamical instabilities in a multimode countinous-wave dye laser, Can. J. Phys. 68, , [31] Freeman, W.J., Quian Quiroga, R. Imaging Brain Functions with EEG: Advanced Temporal and Spatial Analysis of Electrocorticographic Signals, Springer, [32] Davis, J.J.Joshua, Kozma, R. On the Invariance of Cortical Synchronization Measures Across a Broad Range of Frequencies, Proc. Int. Conf. Awareness Computing icast2012, IEEE Press, Seoul, Korea, August 21-24, 2012 (in press). [33] Davis, J.J.Joshua The Brain of Melchizedek, Thesis, Otago University, Dunedin, New Zealand, [34] Logothetis N.K. What we can do and what we cannot do with fmri, Nature 453: doi: /nature06976, 2008.
SIMULATING RESTING CORTICAL BACKGROUND ACTIVITY WITH FILTERED NOISE. Journal of Integrative Neuroscience 7(3):
SIMULATING RESTING CORTICAL BACKGROUND ACTIVITY WITH FILTERED NOISE Journal of Integrative Neuroscience 7(3): 337-344. WALTER J FREEMAN Department of Molecular and Cell Biology, Donner 101 University of
More informationStudy of Phase Relationships in ECoG Signals Using Hilbert-Huang Transforms
Study of Phase Relationships in ECoG Signals Using Hilbert-Huang Transforms Gahangir Hossain, Mark H. Myers, and Robert Kozma Center for Large-Scale Integrated Optimization and Networks (CLION) The University
More informationEE 791 EEG-5 Measures of EEG Dynamic Properties
EE 791 EEG-5 Measures of EEG Dynamic Properties Computer analysis of EEG EEG scientists must be especially wary of mathematics in search of applications after all the number of ways to transform data is
More informationPSYC696B: Analyzing Neural Time-series Data
PSYC696B: Analyzing Neural Time-series Data Spring, 2014 Tuesdays, 4:00-6:45 p.m. Room 338 Shantz Building Course Resources Online: jallen.faculty.arizona.edu Follow link to Courses Available from: Amazon:
More informationIntroduction to Computational Neuroscience
Introduction to Computational Neuroscience Lecture 4: Data analysis I Lesson Title 1 Introduction 2 Structure and Function of the NS 3 Windows to the Brain 4 Data analysis 5 Data analysis II 6 Single neuron
More informationMotor Imagery based Brain Computer Interface (BCI) using Artificial Neural Network Classifiers
Motor Imagery based Brain Computer Interface (BCI) using Artificial Neural Network Classifiers Maitreyee Wairagkar Brain Embodiment Lab, School of Systems Engineering, University of Reading, Reading, U.K.
More informationBeyond Blind Averaging Analyzing Event-Related Brain Dynamics
Beyond Blind Averaging Analyzing Event-Related Brain Dynamics Scott Makeig Swartz Center for Computational Neuroscience Institute for Neural Computation University of California San Diego La Jolla, CA
More information780. Biomedical signal identification and analysis
780. Biomedical signal identification and analysis Agata Nawrocka 1, Andrzej Kot 2, Marcin Nawrocki 3 1, 2 Department of Process Control, AGH University of Science and Technology, Poland 3 Department of
More informationClassifying the Brain's Motor Activity via Deep Learning
Final Report Classifying the Brain's Motor Activity via Deep Learning Tania Morimoto & Sean Sketch Motivation Over 50 million Americans suffer from mobility or dexterity impairments. Over the past few
More informationSpectro-Temporal Methods in Primary Auditory Cortex David Klein Didier Depireux Jonathan Simon Shihab Shamma
Spectro-Temporal Methods in Primary Auditory Cortex David Klein Didier Depireux Jonathan Simon Shihab Shamma & Department of Electrical Engineering Supported in part by a MURI grant from the Office of
More informationMicro-state analysis of EEG
Micro-state analysis of EEG Gilles Pourtois Psychopathology & Affective Neuroscience (PAN) Lab http://www.pan.ugent.be Stewart & Walsh, 2000 A shared opinion on EEG/ERP: excellent temporal resolution (ms
More informationDetecting spread spectrum pseudo random noise tags in EEG/MEG using a structure-based decomposition
Detecting spread spectrum pseudo random noise tags in EEG/MEG using a structure-based decomposition P Desain 1, J Farquhar 1,2, J Blankespoor 1, S Gielen 2 1 Music Mind Machine Nijmegen Inst for Cognition
More informationPhase Synchronization of Two Tremor-Related Neurons
Phase Synchronization of Two Tremor-Related Neurons Sunghan Kim Biomedical Signal Processing Laboratory Electrical and Computer Engineering Department Portland State University ELECTRICAL & COMPUTER Background
More informationCN510: Principles and Methods of Cognitive and Neural Modeling. Neural Oscillations. Lecture 24
CN510: Principles and Methods of Cognitive and Neural Modeling Neural Oscillations Lecture 24 Instructor: Anatoli Gorchetchnikov Teaching Fellow: Rob Law It Is Much
More informationNeurophysiology. The action potential. Why should we care? AP is the elemental until of nervous system communication
Neurophysiology Why should we care? AP is the elemental until of nervous system communication The action potential Time course, propagation velocity, and patterns all constrain hypotheses on how the brain
More information(Time )Frequency Analysis of EEG Waveforms
(Time )Frequency Analysis of EEG Waveforms Niko Busch Charité University Medicine Berlin; Berlin School of Mind and Brain niko.busch@charite.de niko.busch@charite.de 1 / 23 From ERP waveforms to waves
More informationBiomedical Signals. Signals and Images in Medicine Dr Nabeel Anwar
Biomedical Signals Signals and Images in Medicine Dr Nabeel Anwar Noise Removal: Time Domain Techniques 1. Synchronized Averaging (covered in lecture 1) 2. Moving Average Filters (today s topic) 3. Derivative
More informationEffects of Firing Synchrony on Signal Propagation in Layered Networks
Effects of Firing Synchrony on Signal Propagation in Layered Networks 141 Effects of Firing Synchrony on Signal Propagation in Layered Networks G. T. Kenyon,l E. E. Fetz,2 R. D. Puffl 1 Department of Physics
More informationNeural Coding of Multiple Stimulus Features in Auditory Cortex
Neural Coding of Multiple Stimulus Features in Auditory Cortex Jonathan Z. Simon Neuroscience and Cognitive Sciences Biology / Electrical & Computer Engineering University of Maryland, College Park Computational
More informationChapter 73. Two-Stroke Apparent Motion. George Mather
Chapter 73 Two-Stroke Apparent Motion George Mather The Effect One hundred years ago, the Gestalt psychologist Max Wertheimer published the first detailed study of the apparent visual movement seen when
More informationDetermination of human EEG alpha entrainment ERD/ERS using the continuous complex wavelet transform
Determination of human EEG alpha entrainment ERD/ERS using the continuous complex wavelet transform David B. Chorlian * a, Bernice Porjesz a, Henri Begleiter a a Neurodyanamics Laboratory, SUNY/HSCB, Brooklyn,
More informationNon-Sinusoidal Activity Can Produce Cross- Frequency Coupling in Cortical Signals in the Absence of Functional Interaction between Neural Sources
RESEARCH ARTICLE Non-Sinusoidal Activity Can Produce Cross- Frequency Coupling in Cortical Signals in the Absence of Functional Interaction between Neural Sources Edden M. Gerber 1 *, Boaz Sadeh 2, Andrew
More informationComplex Sounds. Reading: Yost Ch. 4
Complex Sounds Reading: Yost Ch. 4 Natural Sounds Most sounds in our everyday lives are not simple sinusoidal sounds, but are complex sounds, consisting of a sum of many sinusoids. The amplitude and frequency
More informationGeometric Neurodynamical Classifiers Applied to Breast Cancer Detection. Tijana T. Ivancevic
Geometric Neurodynamical Classifiers Applied to Breast Cancer Detection Tijana T. Ivancevic Thesis submitted for the Degree of Doctor of Philosophy in Applied Mathematics at The University of Adelaide
More informationREPORT ON THE RESEARCH WORK
REPORT ON THE RESEARCH WORK Influence exerted by AIRES electromagnetic anomalies neutralizer on changes of EEG parameters caused by exposure to the electromagnetic field of a mobile telephone Executors:
More informationSystem Identification and CDMA Communication
System Identification and CDMA Communication A (partial) sample report by Nathan A. Goodman Abstract This (sample) report describes theory and simulations associated with a class project on system identification
More informationChapter 2 Channel Equalization
Chapter 2 Channel Equalization 2.1 Introduction In wireless communication systems signal experiences distortion due to fading [17]. As signal propagates, it follows multiple paths between transmitter and
More informationA beamforming approach to phase-amplitude modulation analysis of multi-channel EEG
A beamforming approach to phase-amplitude modulation analysis of multi-channel EEG The MIT Faculty has made this article openly available. Please share how this access benefits you. Your story matters.
More informationChaotic-Based Processor for Communication and Multimedia Applications Fei Li
Chaotic-Based Processor for Communication and Multimedia Applications Fei Li 09212020027@fudan.edu.cn Chaos is a phenomenon that attracted much attention in the past ten years. In this paper, we analyze
More informationA chaotic lock-in amplifier
A chaotic lock-in amplifier Brian K. Spears Lawrence Livermore National Laboratory, 7000 East Avenue, Livermore CA 94550 Nicholas B. Tufillaro Measurement Research Lab, Agilent Laboratories, Agilent Technologies,
More informationfrom signals to sources asa-lab turnkey solution for ERP research
from signals to sources asa-lab turnkey solution for ERP research asa-lab : turnkey solution for ERP research Psychological research on the basis of event-related potentials is a key source of information
More informationPREDICTION OF FINGER FLEXION FROM ELECTROCORTICOGRAPHY DATA
University of Tartu Institute of Computer Science Course Introduction to Computational Neuroscience Roberts Mencis PREDICTION OF FINGER FLEXION FROM ELECTROCORTICOGRAPHY DATA Abstract This project aims
More informationCHAPTER 1 INTRODUCTION
1 CHAPTER 1 INTRODUCTION 1.1 BACKGROUND The increased use of non-linear loads and the occurrence of fault on the power system have resulted in deterioration in the quality of power supplied to the customers.
More informationLow-Frequency Transient Visual Oscillations in the Fly
Kate Denning Biophysics Laboratory, UCSD Spring 2004 Low-Frequency Transient Visual Oscillations in the Fly ABSTRACT Low-frequency oscillations were observed near the H1 cell in the fly. Using coherence
More informationDepartment of Electronic Engineering NED University of Engineering & Technology. LABORATORY WORKBOOK For the Course SIGNALS & SYSTEMS (TC-202)
Department of Electronic Engineering NED University of Engineering & Technology LABORATORY WORKBOOK For the Course SIGNALS & SYSTEMS (TC-202) Instructor Name: Student Name: Roll Number: Semester: Batch:
More informationLarge-scale cortical correlation structure of spontaneous oscillatory activity
Supplementary Information Large-scale cortical correlation structure of spontaneous oscillatory activity Joerg F. Hipp 1,2, David J. Hawellek 1, Maurizio Corbetta 3, Markus Siegel 2 & Andreas K. Engel
More informationDigitally controlled Active Noise Reduction with integrated Speech Communication
Digitally controlled Active Noise Reduction with integrated Speech Communication Herman J.M. Steeneken and Jan Verhave TNO Human Factors, Soesterberg, The Netherlands herman@steeneken.com ABSTRACT Active
More informationInstruction Manual for Concept Simulators. Signals and Systems. M. J. Roberts
Instruction Manual for Concept Simulators that accompany the book Signals and Systems by M. J. Roberts March 2004 - All Rights Reserved Table of Contents I. Loading and Running the Simulators II. Continuous-Time
More informationAUDL 4007 Auditory Perception. Week 1. The cochlea & auditory nerve: Obligatory stages of auditory processing
AUDL 4007 Auditory Perception Week 1 The cochlea & auditory nerve: Obligatory stages of auditory processing 1 Think of the ear as a collection of systems, transforming sounds to be sent to the brain 25
More informationNew ways in non-stationary, nonlinear EEG signal processing
MACRo 2013- International Conference on Recent Achievements in Mechatronics, Automation, Computer Science and Robotics New ways in non-stationary, nonlinear EEG signal processing László-Ferenc MÁRTON 1,
More informationLimulus eye: a filter cascade. Limulus 9/23/2011. Dynamic Response to Step Increase in Light Intensity
Crab cam (Barlow et al., 2001) self inhibition recurrent inhibition lateral inhibition - L17. Neural processing in Linear Systems 2: Spatial Filtering C. D. Hopkins Sept. 23, 2011 Limulus Limulus eye:
More information19 th INTERNATIONAL CONGRESS ON ACOUSTICS MADRID, 2-7 SEPTEMBER 2007
19 th INTERNATIONAL CONGRESS ON ACOUSTICS MADRID, 2-7 SEPTEMBER 2007 MODELING SPECTRAL AND TEMPORAL MASKING IN THE HUMAN AUDITORY SYSTEM PACS: 43.66.Ba, 43.66.Dc Dau, Torsten; Jepsen, Morten L.; Ewert,
More informationEXPERIMENTAL STUDY OF IMPULSIVE SYNCHRONIZATION OF CHAOTIC AND HYPERCHAOTIC CIRCUITS
International Journal of Bifurcation and Chaos, Vol. 9, No. 7 (1999) 1393 1424 c World Scientific Publishing Company EXPERIMENTAL STUDY OF IMPULSIVE SYNCHRONIZATION OF CHAOTIC AND HYPERCHAOTIC CIRCUITS
More informationApplication of Hilbert-Huang Transform in the Field of Power Quality Events Analysis Manish Kumar Saini 1 and Komal Dhamija 2 1,2
Application of Hilbert-Huang Transform in the Field of Power Quality Events Analysis Manish Kumar Saini 1 and Komal Dhamija 2 1,2 Department of Electrical Engineering, Deenbandhu Chhotu Ram University
More informationAHAPTIC interface is a kinesthetic link between a human
IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, VOL. 13, NO. 5, SEPTEMBER 2005 737 Time Domain Passivity Control With Reference Energy Following Jee-Hwan Ryu, Carsten Preusche, Blake Hannaford, and Gerd
More informationReal Robots Controlled by Brain Signals - A BMI Approach
International Journal of Advanced Intelligence Volume 2, Number 1, pp.25-35, July, 2010. c AIA International Advanced Information Institute Real Robots Controlled by Brain Signals - A BMI Approach Genci
More informationAuditory modelling for speech processing in the perceptual domain
ANZIAM J. 45 (E) ppc964 C980, 2004 C964 Auditory modelling for speech processing in the perceptual domain L. Lin E. Ambikairajah W. H. Holmes (Received 8 August 2003; revised 28 January 2004) Abstract
More informationFAULT DETECTION OF FLIGHT CRITICAL SYSTEMS
FAULT DETECTION OF FLIGHT CRITICAL SYSTEMS Jorge L. Aravena, Louisiana State University, Baton Rouge, LA Fahmida N. Chowdhury, University of Louisiana, Lafayette, LA Abstract This paper describes initial
More informationVisual Coding in the Blowfly H1 Neuron: Tuning Properties and Detection of Velocity Steps in a new Arena
Visual Coding in the Blowfly H1 Neuron: Tuning Properties and Detection of Velocity Steps in a new Arena Jeff Moore and Adam Calhoun TA: Erik Flister UCSD Imaging and Electrophysiology Course, Prof. David
More informationEvoked Potentials (EPs)
EVOKED POTENTIALS Evoked Potentials (EPs) Event-related brain activity where the stimulus is usually of sensory origin. Acquired with conventional EEG electrodes. Time-synchronized = time interval from
More informationMind Mirror 6 Data Analysis Healing Session September 2015 Susan Andrews and Frans Stiene
Mind Mirror 6 Data Analysis Healing Session September 2015 Susan Andrews and Frans Stiene Gamma brainwaves are intensely interesting to Awakened Mind Consciousness Trainers using the Mind Mirror EEG to
More information40 Hz Event Related Auditory Potential
40 Hz Event Related Auditory Potential Ivana Andjelkovic Advanced Biophysics Lab Class, 2012 Abstract Main focus of this paper is an EEG experiment on observing frequency of event related auditory potential
More informationShape Representation Robust to the Sketching Order Using Distance Map and Direction Histogram
Shape Representation Robust to the Sketching Order Using Distance Map and Direction Histogram Kiwon Yun, Junyeong Yang, and Hyeran Byun Dept. of Computer Science, Yonsei University, Seoul, Korea, 120-749
More informationTraining of EEG Signal Intensification for BCI System. Haesung Jeong*, Hyungi Jeong*, Kong Borasy*, Kyu-Sung Kim***, Sangmin Lee**, Jangwoo Kwon*
Training of EEG Signal Intensification for BCI System Haesung Jeong*, Hyungi Jeong*, Kong Borasy*, Kyu-Sung Kim***, Sangmin Lee**, Jangwoo Kwon* Department of Computer Engineering, Inha University, Korea*
More informationAnalysis of brain waves according to their frequency
Analysis of brain waves according to their frequency Z. Koudelková, M. Strmiska, R. Jašek Abstract The primary purpose of this article is to show and analyse the brain waves, which are activated during
More informationA Novel Detection and Classification Algorithm for Power Quality Disturbances using Wavelets
American Journal of Applied Sciences 3 (10): 2049-2053, 2006 ISSN 1546-9239 2006 Science Publications A Novel Detection and Classification Algorithm for Power Quality Disturbances using Wavelets 1 C. Sharmeela,
More informationNOISE ESTIMATION IN A SINGLE CHANNEL
SPEECH ENHANCEMENT FOR CROSS-TALK INTERFERENCE by Levent M. Arslan and John H.L. Hansen Robust Speech Processing Laboratory Department of Electrical Engineering Box 99 Duke University Durham, North Carolina
More informationBRAIN COMPUTER INTERFACE (BCI) RESEARCH CENTER AT SRM UNIVERSITY
BRAIN COMPUTER INTERFACE (BCI) RESEARCH CENTER AT SRM UNIVERSITY INTRODUCTION TO BCI Brain Computer Interfacing has been one of the growing fields of research and development in recent years. An Electroencephalograph
More informationSignal segmentation and waveform characterization. Biosignal processing, S Autumn 2012
Signal segmentation and waveform characterization Biosignal processing, 5173S Autumn 01 Short-time analysis of signals Signal statistics may vary in time: nonstationary how to compute signal characterizations?
More informationCommunication Engineering Prof. Surendra Prasad Department of Electrical Engineering Indian Institute of Technology, Delhi
Communication Engineering Prof. Surendra Prasad Department of Electrical Engineering Indian Institute of Technology, Delhi Lecture - 16 Angle Modulation (Contd.) We will continue our discussion on Angle
More informationA Vestibular Sensation: Probabilistic Approaches to Spatial Perception (II) Presented by Shunan Zhang
A Vestibular Sensation: Probabilistic Approaches to Spatial Perception (II) Presented by Shunan Zhang Vestibular Responses in Dorsal Visual Stream and Their Role in Heading Perception Recent experiments
More informationA Pilot Study: Introduction of Time-domain Segment to Intensity-based Perception Model of High-frequency Vibration
A Pilot Study: Introduction of Time-domain Segment to Intensity-based Perception Model of High-frequency Vibration Nan Cao, Hikaru Nagano, Masashi Konyo, Shogo Okamoto 2 and Satoshi Tadokoro Graduate School
More informationECE 476/ECE 501C/CS Wireless Communication Systems Winter Lecture 6: Fading
ECE 476/ECE 501C/CS 513 - Wireless Communication Systems Winter 2005 Lecture 6: Fading Last lecture: Large scale propagation properties of wireless systems - slowly varying properties that depend primarily
More informationTime-Frequency analysis of biophysical time series. Courtesy of Arnaud Delorme
Time-Frequency analysis of biophysical time series Courtesy of Arnaud Delorme 1 2 Why Frequency-domain Analysis For many signals, the signal's frequency content is of great importance. Beta Alpha Theta
More informationIntroduction. Chapter Time-Varying Signals
Chapter 1 1.1 Time-Varying Signals Time-varying signals are commonly observed in the laboratory as well as many other applied settings. Consider, for example, the voltage level that is present at a specific
More informationApplication Note 106 IP2 Measurements of Wideband Amplifiers v1.0
Application Note 06 v.0 Description Application Note 06 describes the theory and method used by to characterize the second order intercept point (IP 2 ) of its wideband amplifiers. offers a large selection
More informationAC : A CIRCUITS COURSE FOR MECHATRONICS ENGINEERING
AC 2010-2256: A CIRCUITS COURSE FOR MECHATRONICS ENGINEERING L. Brent Jenkins, Southern Polytechnic State University American Society for Engineering Education, 2010 Page 15.14.1 A Circuits Course for
More informationEfficient Learning in Cellular Simultaneous Recurrent Neural Networks - The Case of Maze Navigation Problem
Efficient Learning in Cellular Simultaneous Recurrent Neural Networks - The Case of Maze Navigation Problem Roman Ilin Department of Mathematical Sciences The University of Memphis Memphis, TN 38117 E-mail:
More informationThe Data: Multi-cell Recordings
The Data: Multi-cell Recordings What is real? How do you define real? If you re talking about your senses, what you feel, taste, smell, or see, then all you re talking about are electrical signals interpreted
More informationDistortion products and the perceived pitch of harmonic complex tones
Distortion products and the perceived pitch of harmonic complex tones D. Pressnitzer and R.D. Patterson Centre for the Neural Basis of Hearing, Dept. of Physiology, Downing street, Cambridge CB2 3EG, U.K.
More informationPlayware Research Methodological Considerations
Journal of Robotics, Networks and Artificial Life, Vol. 1, No. 1 (June 2014), 23-27 Playware Research Methodological Considerations Henrik Hautop Lund Centre for Playware, Technical University of Denmark,
More informationELT Receiver Architectures and Signal Processing Fall Mandatory homework exercises
ELT-44006 Receiver Architectures and Signal Processing Fall 2014 1 Mandatory homework exercises - Individual solutions to be returned to Markku Renfors by email or in paper format. - Solutions are expected
More informationCommunication using Synchronization of Chaos in Semiconductor Lasers with optoelectronic feedback
Communication using Synchronization of Chaos in Semiconductor Lasers with optoelectronic feedback S. Tang, L. Illing, J. M. Liu, H. D. I. barbanel and M. B. Kennel Department of Electrical Engineering,
More informationBiometric: EEG brainwaves
Biometric: EEG brainwaves Jeovane Honório Alves 1 1 Department of Computer Science Federal University of Parana Curitiba December 5, 2016 Jeovane Honório Alves (UFPR) Biometric: EEG brainwaves Curitiba
More informationJitter Analysis Techniques Using an Agilent Infiniium Oscilloscope
Jitter Analysis Techniques Using an Agilent Infiniium Oscilloscope Product Note Table of Contents Introduction........................ 1 Jitter Fundamentals................. 1 Jitter Measurement Techniques......
More informationAn Auditory Localization and Coordinate Transform Chip
An Auditory Localization and Coordinate Transform Chip Timothy K. Horiuchi timmer@cns.caltech.edu Computation and Neural Systems Program California Institute of Technology Pasadena, CA 91125 Abstract The
More informationAcoustics, signals & systems for audiology. Week 4. Signals through Systems
Acoustics, signals & systems for audiology Week 4 Signals through Systems Crucial ideas Any signal can be constructed as a sum of sine waves In a linear time-invariant (LTI) system, the response to a sinusoid
More informationA Robust Neural Robot Navigation Using a Combination of Deliberative and Reactive Control Architectures
A Robust Neural Robot Navigation Using a Combination of Deliberative and Reactive Control Architectures D.M. Rojas Castro, A. Revel and M. Ménard * Laboratory of Informatics, Image and Interaction (L3I)
More informationNeuronal correlates of pitch in the Inferior Colliculus
Neuronal correlates of pitch in the Inferior Colliculus Didier A. Depireux David J. Klein Jonathan Z. Simon Shihab A. Shamma Institute for Systems Research University of Maryland College Park, MD 20742-3311
More informationAccurate Delay Measurement of Coded Speech Signals with Subsample Resolution
PAGE 433 Accurate Delay Measurement of Coded Speech Signals with Subsample Resolution Wenliang Lu, D. Sen, and Shuai Wang School of Electrical Engineering & Telecommunications University of New South Wales,
More informationImage Enhancement in Spatial Domain
Image Enhancement in Spatial Domain 2 Image enhancement is a process, rather a preprocessing step, through which an original image is made suitable for a specific application. The application scenarios
More informationTNS Journal Club: Efficient coding of natural sounds, Lewicki, Nature Neurosceince, 2002
TNS Journal Club: Efficient coding of natural sounds, Lewicki, Nature Neurosceince, 2002 Rich Turner (turner@gatsby.ucl.ac.uk) Gatsby Unit, 18/02/2005 Introduction The filters of the auditory system have
More informationNon-Invasive Brain-Actuated Control of a Mobile Robot
Non-Invasive Brain-Actuated Control of a Mobile Robot Jose del R. Millan, Frederic Renkens, Josep Mourino, Wulfram Gerstner 5/3/06 Josh Storz CSE 599E BCI Introduction (paper perspective) BCIs BCI = Brain
More informationECE 476/ECE 501C/CS Wireless Communication Systems Winter Lecture 6: Fading
ECE 476/ECE 501C/CS 513 - Wireless Communication Systems Winter 2003 Lecture 6: Fading Last lecture: Large scale propagation properties of wireless systems - slowly varying properties that depend primarily
More informationEncoding of Naturalistic Stimuli by Local Field Potential Spectra in Networks of Excitatory and Inhibitory Neurons
Encoding of Naturalistic Stimuli by Local Field Potential Spectra in Networks of Excitatory and Inhibitory Neurons Alberto Mazzoni 1, Stefano Panzeri 2,3,1, Nikos K. Logothetis 4,5 and Nicolas Brunel 1,6,7
More informationNonuniform multi level crossing for signal reconstruction
6 Nonuniform multi level crossing for signal reconstruction 6.1 Introduction In recent years, there has been considerable interest in level crossing algorithms for sampling continuous time signals. Driven
More informationRESEARCH ON METHODS FOR ANALYZING AND PROCESSING SIGNALS USED BY INTERCEPTION SYSTEMS WITH SPECIAL APPLICATIONS
Abstract of Doctorate Thesis RESEARCH ON METHODS FOR ANALYZING AND PROCESSING SIGNALS USED BY INTERCEPTION SYSTEMS WITH SPECIAL APPLICATIONS PhD Coordinator: Prof. Dr. Eng. Radu MUNTEANU Author: Radu MITRAN
More informationOff-line EEG analysis of BCI experiments with MATLAB V1.07a. Copyright g.tec medical engineering GmbH
g.tec medical engineering GmbH Sierningstrasse 14, A-4521 Schiedlberg Austria - Europe Tel.: (43)-7251-22240-0 Fax: (43)-7251-22240-39 office@gtec.at, http://www.gtec.at Off-line EEG analysis of BCI experiments
More informationPosition Control of AC Servomotor Using Internal Model Control Strategy
Position Control of AC Servomotor Using Internal Model Control Strategy Ahmed S. Abd El-hamid and Ahmed H. Eissa Corresponding Author email: Ahmednrc64@gmail.com Abstract: This paper focuses on the design
More informationHigh-speed wavefront control using MEMS micromirrors T. G. Bifano and J. B. Stewart, Boston University [ ] Introduction
High-speed wavefront control using MEMS micromirrors T. G. Bifano and J. B. Stewart, Boston University [5895-27] Introduction Various deformable mirrors for high-speed wavefront control have been demonstrated
More informationBrain-Computer Interface for Control and Communication with Smart Mobile Applications
University of Telecommunications and Post Sofia, Bulgaria Brain-Computer Interface for Control and Communication with Smart Mobile Applications Prof. Svetla Radeva, DSc, PhD HUMAN - COMPUTER INTERACTION
More informationAPPENDIX MATHEMATICS OF DISTORTION PRODUCT OTOACOUSTIC EMISSION GENERATION: A TUTORIAL
In: Otoacoustic Emissions. Basic Science and Clinical Applications, Ed. Charles I. Berlin, Singular Publishing Group, San Diego CA, pp. 149-159. APPENDIX MATHEMATICS OF DISTORTION PRODUCT OTOACOUSTIC EMISSION
More informationIntroduction to Phase Noise
hapter Introduction to Phase Noise brief introduction into the subject of phase noise is given here. We first describe the conversion of the phase fluctuations into the noise sideband of the carrier. We
More informationLaboratory PID Tuning Based On Frequency Response Analysis. 2. be able to evaluate system performance for empirical tuning method;
Laboratory PID Tuning Based On Frequency Response Analysis Objectives: At the end, student should 1. appreciate a systematic way of tuning PID loop by the use of process frequency response analysis; 2.
More informationIntermittent Chaos in Switching Power Supplies Due to Unintended Coupling of Spurious Signals
Intermittent Chaos in Switching Power Supplies Due to Unintended Coupling of Spurious Signals C. K. Tse,Yufei Zhou,F.C.M.Lau and S. S. Qiu Dept. of Electronic & Information Engineering, Hong Kong Polytechnic
More information(i) Understanding the basic concepts of signal modeling, correlation, maximum likelihood estimation, least squares and iterative numerical methods
Tools and Applications Chapter Intended Learning Outcomes: (i) Understanding the basic concepts of signal modeling, correlation, maximum likelihood estimation, least squares and iterative numerical methods
More informationMachine recognition of speech trained on data from New Jersey Labs
Machine recognition of speech trained on data from New Jersey Labs Frequency response (peak around 5 Hz) Impulse response (effective length around 200 ms) 41 RASTA filter 10 attenuation [db] 40 1 10 modulation
More informationECE 476/ECE 501C/CS Wireless Communication Systems Winter Lecture 6: Fading
ECE 476/ECE 501C/CS 513 - Wireless Communication Systems Winter 2004 Lecture 6: Fading Last lecture: Large scale propagation properties of wireless systems - slowly varying properties that depend primarily
More informationThe Discrete Fourier Transform. Claudia Feregrino-Uribe, Alicia Morales-Reyes Original material: Dr. René Cumplido
The Discrete Fourier Transform Claudia Feregrino-Uribe, Alicia Morales-Reyes Original material: Dr. René Cumplido CCC-INAOE Autumn 2015 The Discrete Fourier Transform Fourier analysis is a family of mathematical
More informationKeysight Technologies Pulsed Antenna Measurements Using PNA Network Analyzers
Keysight Technologies Pulsed Antenna Measurements Using PNA Network Analyzers White Paper Abstract This paper presents advances in the instrumentation techniques that can be used for the measurement and
More information