Antonio Mauricio Ferreira Leite Miranda de Sá

Improving the detection of evoked responses to periodic stimulation by using multiple coherence—application to EEG during photic stimulation

The coherence between the stimulation signal and the electroencephalogram (EEG) has been used in the detection of evoked responses. However the detectors performance depends on both the signal-to-noise ratio (SNR) of the responses and the number of data segments (M) used in coherence estimation. In this work, a technique for detecting evoked responses was developed based on the extension to the multivariate case of this coherence. Thus, instead of using the EEG collected at a unique region, the estimation is proposed using two EEG derivations. As for the univariate case, this multiple coherence is independent of the stimulation signal. In addition, considering equal SNR in both signals, the detection rate with this multiple coherence is always greater than that one using only one signal. This was verified in Monte Carlo simulations, which also showed that a superior performance is still expected in practical situations, when a smaller SNR is found in the second signal. The results with EEG from 12 normal subjects during photic stimulation confirm this better performance. Since the proposed technique allows a higher detection rate without the need of increasing M, it permits evoked responses to be detected faster, which is very useful during monitored surgeries.

Multi-channel evoked response detection using only phase information.

The phase consistency of contiguous segments of the electroencephalogram (EEG) has been used in the detection of evoked responses to rhythmic stimulation. One of such techniques is the component synchrony measure (CSM), which is often used since the threshold for the detection task is easily obtained based on the estimates of asymptotic sample distribution. In this work we investigated the appropriateness of such thresholds for practical number of segments (M). The performance of CSM was next evaluated by Monte Carlo simulations with different signal-to-noise ratios (SNR) and values of M, and the results, compared with those for the magnitude-squared coherence. A way of improving the detection with CSM was also proposed, by suggesting the estimation taking into account the mean phase angle of a set of N signals. This multivariate detector was evaluated in simulations and an illustration of the technique was also given with the EEG of 14 subjects during photic stimulation. In simulated signals with equal SNR, the detection rate with this multivariate measure increased with N. The application to EEG data lead to similar results in 70% of the subjects, which suggests that improvements might be expected when more signals are available to detect evoked responses in EEG.

Assessing Time- and Phase-Locked Changes in the EEG during Sensory Stimulation by Means of Spectral Techniques

The Spectral F-Test (SFT) is a statistical test for assessing whether two sample spectra are from populations with statistically identical spectra. In this work SFT-based detectors are proposed for investigating spectral changes in the electroencephalogram (EEG) that may be either time- or phase-locked to an external stimulation. The sampling distributions of the detectors were obtained analytically, which allowed thresholds for the detection to be found. The technique was evaluated through simulations and an illustration is given with EEG acquired during stroboscopic stimulation.

Coherence estimate between a random and a periodic signal: Bias, variance, analytical critical values, and normalizing transforms

The present work deals with recent results on the sampling distribution of the magnitude-squared coherence (also called just coherence) estimate between a random (Gaussian) and a periodic signal, in order to obtain analytical critical values, alternative expressions for the probability density function (PDF) as well as the variance and bias of the estimate. A comparison with the more general case of coherence estimation when both signals are Gaussian was also provided. The results indicate that the smaller the true coherence (TC) values the closer both distributions become. The behaviour of variance and bias as a function of the number of data segments and the TC is similar for both coherence estimates. Additionally, the effect of a normalizing function (Fishers z transform) in the coherence estimated between a random and a periodic signal was also evaluated and normality has been nearly achieved. However, the variance was less equalized in comparison with coherence estimate between two Gaussian signals.

A coherence-based technique for separating phase-locked from non-phase-locked power spectrum estimates during intermittent stimulation

In addition to evoked responses, which are phase-locked to the stimuli, the stimulation may also change the ongoing EEG in a time-locked manner. This change has been investigated in event-related synchronization/desynchronization (ERS/ERD) studies by comparing the spectra before and during stimulation or alternatively by using the intertrial variance method (IVM). In the present work, a technique based on the coherence estimate (kappa(y)(2)(f)) between the stimulation signal and the EEG is proposed for separating the ongoing EEG activity spectrum from that of the evoked responses. Furthermore, a statistical criterion is applied to reduce spurious spectral peaks. The performance of this procedure was assessed through simulation and illustrated with EEG during photic stimulation. For simulated data (signal-to-noise-ratio of 0.995 within 10-12.5 Hz) kappa(y)(2)(f) led to a non-phase-locked spectrum estimate with an average normalized error of 12.4%, which is reduced to only 0.2% after applying the statistical criterion. The methodology proposed is asymptotically equivalent to the IVM but it does not require previous filtering the EEG data. Kappa(y)(2)(f) together with the statistical correction criterion allows investigating the entrainment within a narrow-band range, particularly in frequencies close to that of the alpha peak. Hence it is useful in ERS/ERD studies. Moreover, it can be also used for characterizing frequencies within the gamma band.

Spectral F-Test power evaluation in the EEG during intermittent photic stimulaton

Intermittent photic stimulation (IPS) is an important functional test, which can induce the photic driving in the electroencephalogram (EEG). It is capable of enhancing latent oscillations manifestations not present in the resting EEG. However, for adequate quantitative evaluation of the photic driving, these changes should be assessed on a statistical basis. With this aim, the sampling distribution of spectral F test was investigated. On this basis, confidence limits of the SFT-estimate could be obtained for different practical situations, in which the signal-to-noise ratio and the number of epochs used in the estimation may vary. The technique was applied to the EEG of 10 normal subjects during IPS, and allowed detecting responses not only at the fundamental IPS frequency but also at higher harmonics. It also permitted to assess the strength of the photic driving responses and to compare them in different derivations and in different subjects.

A multiple coherence-based detector for evoked responses in the EEG during sensory stimulation

The sampling distribution of the multiple coherence estimate between a periodic signal and a set of filtered versions of evoked responses embedded in additive noise signals is derived for the zero-coherence case. For a fixed number of signals used in the estimation, the probability density function varies with the number of data segments. Analytical expressions for both bias and variance of the estimate were derived and together with the critical values constitute the statistical apparatus for the detector based on this multiple coherence estimate. An illustration of the technique as applied to detect evoked responses in the Electroencephalogram during sensory stimulation is also provided.

Frequency-Domain Objective Response Detection Techniques Applied to Evoked Potentials: A Review

The Electroencephalogram (EEG) is the biological signal collected at the scalp as a summation result of ionic currents generated from the post-synaptic potentials of the brain neurons. Differently from some other biosignals such as the electrocardiogram, which presents a visual identifiable pattern – particularly the QRS complex, the EEG exhibits a very large random variability. It is indeed quite often assumed to be a white Gaussian noise, and this stochastic behavior turns the analysis of EEG signals by visual inspection a very difficult task. In spite of this, the EEG is known to be correlated with sensorial information processing, and it is widely used for neurophysiologic assessment and neuropathies diagnosis.

A spatial approach of magnitude-squared coherence applied to selective attention detection

BACKGROUND Auditory selective attention is the human ability of actively focusing in a certain sound stimulus while avoiding all other ones. This ability can be used, for example, in behavioral studies and brain-machine interface. NEW METHOD In this work we developed an objective method - called Spatial Coherence - to detect the side where a subject is focusing attention to. This method takes into consideration the Magnitude Squared Coherence and the topographic distribution of responses among electroencephalogram electrodes. The individuals were stimulated with amplitude-modulated tones binaurally and were oriented to focus attention to only one of the stimuli. RESULTS The results indicate a contralateral modulation of ASSR in the attention condition and are in agreement with prior studies. Furthermore, the best combination of electrodes led to a hit rate of 82% for 5.03 commands per minute. COMPARISON WITH EXISTING METHOD(S) Using a similar paradigm, in a recent work, a maximum hit rate of 84.33% was achieved, but with a greater a classification time (20s, i.e. 3 commands per minute). CONCLUSIONS It seems that Spatial Coherence is a useful technique for detecting focus of auditory selective attention.

Recovery coefficients determination for partial volume effect correction in oncological PET/CT images considering the effect of activity outside the field of view

ObjectivesThe partial volume effect (PVE) has a great impact in quantitative PET/CT imaging. Correction methods have been recently proposed by many authors to make the image quantification more accurate. This work presents a methodology for determining the recovery coefficients (RCs) for PVE correction in PET/CT images. It was taken into account the radioactivity outside the field of view (FOV), which is expected in a patient image acquisition.MethodsThe NEMA image quality phantom and the NEMA scatter phantom were used. The phantoms were filled with 18F-FDG for different sphere-to-background ratios. The RCs have been determined from image acquisitions in a Siemens Biograph 16 Hi-Rez PET/CT scanner with and without the scatter phantom.ResultsThe RC values that ranged from 0.38 to 1.00 without the scatter phantom exhibited a wider variation when this latter was taken into account (from 0.27 to 1.02). This more realistic estimation must be considered if one takes into account that an incorrect SUV measure in tumors leads to errors in the evaluation of the response to therapy based on PET/CT images.ConclusionsThe activity outside the FOV should be considered in RCs determination to improve the RC-based PVE correction method.