Estimating the time and angle of arrivals in mobile communications

Dans ce projet, nous présentons une méthode nouvelle et précise d’estimation de la direction et des délais d’arrivée dans un environnement à trajets multiples, à des fins d’estimation de canal. Récemment, les méthodes de super-résolution ont été largement utilisées pour l’estimation à haute-résolution de la direction d’arrivée (DOA) ou de la différence de temps d’arrivée (TDOA). L’algorithme proposé dans ce travail est applicable à l’estimation d’un canal espace-temps pour des systèmes de traitement spatio-temporel qui emploient la technologie hybride DOA / TDOA. L’estimateur est basé sur l’algorithme MUSIC classique pour trouver la DOA et en profitant d’un simple corrélateur, il est possible de trouver le retard de chaque arrivée. Il est pertinent d’associer chaque angle à son propre retard pour être capable d’estimer les caractéristiques du canal quand nous ne connaissons pas la séquence transmise par l’émetteur. Pour ce faire, nous proposons une formation de faisceaux (voix) très simple et optimale par l’application du MVDR (Maximum Variance Distortion-less Response). Cette formation de faisceaux maximise le signal desiré par rapport aux autres signaux. Après détermination de l’angle d’arrivée par l’algorithme MUSIC, nous appliquons l’algorithme de formation de faisceaux MVDR pour obtenir le signal qui est reçu par le réseau d’antennes pour une direction. Ce signal est corrélé avec les autres signaux correspondants aux autres directions d’arrivée. Les pics dans les figures ainsi obtenues montrent le décalage temporel de chaque source par rapport à celle obtenue par la formation de faisceaux MVDR. La soustraction du plus petit décalage, correspondant au premier signal reçu à chaque décalage temporel, nous donne le temps d’arrivée de chaque source. Pour être plus précis, nous pouvons choisir la moyenne des vecteurs des délais estimés, chacun étant obtenu à partir d’une angle pour l’algorithme MVDR.In this project, we present a novel and precise way of estimating the direction and delay of arrivals in multipath environment for channel estimation purposes. Recently, super-resolution methods have been widely used for high resolution Direction Of Arrival (DOA) or Time Difference Of Arrival (TDOA) estimation. The proposed algorithm in this work is applicable to space-time channel estimation for space-time processing systems that employ hybrid DOA/TDOA technology. The estimator is based on the conventional MUSIC algorithm to find the DOA and by using a simple correlator it is possible to find the delay of each arrival. It is of interest to associate each angle to its proper delay to be able to estimate the characteristics of the channel when we have no knowledge about the transmitted sequence. To do this, we suggest a very simple and optimal beamforming method by performing Maximum Variance Distortion-less Response (MVDR). This beamforming maximizes the desired signal in the desired direction compare to the other signals that come from other directions. After finding the DOAs by MUSIC algorithm and selecting our desired direction, we obtain the signal from this direction by applying MVDR beamforming. Then, we perform a correlation between this signal and the others incoming signals from other directions. The peaks in the simulation figures illustrate the delay between each source with the obtained signal from MVDR. If we subtract the delay of the first arrival (the smallest delay in time), from the delays indicated in the figures, we can obtain the delay of each arrival. To be more precise, the mean of these estimated TOAs vector follows the exact TOA of each source

Ripple oscillations in the left temporal neocortex are associated with impaired verbal episodic memory encoding

Background: We sought to determine if ripple oscillations (80-120Hz), detected in intracranial EEG (iEEG) recordings of epilepsy patients, correlate with an enhancement or disruption of verbal episodic memory encoding. Methods: We defined ripple and spike events in depth iEEG recordings during list learning in 107 patients with focal epilepsy. We used logistic regression models (LRMs) to investigate the relationship between the occurrence of ripple and spike events during word presentation and the odds of successful word recall following a distractor epoch, and included the seizure onset zone (SOZ) as a covariate in the LRMs. Results: We detected events during 58,312 word presentation trials from 7,630 unique electrode sites. The probability of ripple on spike (RonS) events was increased in the seizure onset zone (SOZ, p<0.04). In the left temporal neocortex RonS events during word presentation corresponded with a decrease in the odds ratio (OR) of successful recall, however this effect only met significance in the SOZ (OR of word recall 0.71, 95% CI: 0.59-0.85, n=158 events, adaptive Hochberg p<0.01). Ripple on oscillation events (RonO) that occurred in the left temporal neocortex non-SOZ also correlated with decreased odds of successful recall (OR 0.52, 95% CI: 0.34-0.80, n=140, adaptive Hochberg , p<0.01). Spikes and RonS that occurred during word presentation in the left middle temporal gyrus during word presentation correlated with the most significant decrease in the odds of successful recall, irrespective of the location of the SOZ (adaptive Hochberg, p<0.01). Conclusion: Ripples and spikes generated in left temporal neocortex are associated with impaired verbal episodic memory encoding

Selection of stable features for modeling 4-D affective space from EEG recording

Recent advances in neuroscience made it possible to understand how the human brain processes emotions and affective states. However, the modeling of emotion remains elusive due to inherent ambiguity and complexity related to the perception of emotions, interpersonal variabilities, and context-specific interpretations. Here, we present a robust method of modeling 4-D continuous affective space (Valence, Arousal, Like, and Dominance). First, we determined the functional areas and frequency bands related to 4-D affective space. Second, we extracted and selected a set of stable features. For both steps, we used two different feature selection methods namely: Recursive Feature Elimination (RFE) and stability selection method. Moreover, compare their performances. For the RFE, we used Random Forest (RF), Support Vector Regression (SVR), Tree-based bagging, and for the stability selection, we used Randomized Lasso as an estimator. Empirical analysis on the DEAP data set shows that the stability selection method consistently provides relevant set of bands, electrode location and features over a range of model parameters. We also observed that only a small number of locations (40%-63%) and certain frequency bands specifically, gamma band frequency over Superior Temporal Gyrus, Supramarginal Gyrus, and Somatosensory Association Cortex were the highest ranked features across the affective dimensions. The selected features using the stability criteria were used to model 4-D affective space using SVR. Empirical analyses shows that the Root Mean Square Error (RMSE) for Valence, Arousal, Dominance, and Like are 2.13, 2.00, 2.07, and 2.11 respectively. In addition, we also compare the performance of this method with feature fusion and ensemble classification. It was observed that the SVR with selected features outperformed all other approaches. The predicted Valence-Arousal-Dominance were converted to categorical emotions for seamless interpretation

Identifying seizure onset zone from electrocorticographic recordings: A machine learning approach based on phase locking value

Purpose Using a novel technique based on phase locking value (PLV), we investigated the potential for features extracted from electrocorticographic (ECoG) recordings to serve as biomarkers to identify the seizure onset zone (SOZ). Methods We computed the PLV between the phase of the amplitude of high gamma activity (80–150 Hz) and the phase of lower frequency rhythms (4–30 Hz) from ECoG recordings obtained from 10 patients with epilepsy (21 seizures). We extracted five features from the PLV and used a machine learning approach based on logistic regression to build a model that classifies electrodes as SOZ or non-SOZ. Results More than 96% of electrodes identified as the SOZ by our algorithm were within the resected area in six seizure-free patients. In four non-seizure-free patients, more than 31% of the identified SOZ electrodes by our algorithm were outside the resected area. In addition, we observed that the seizure outcome in non-seizure-free patients correlated with the number of non-resected SOZ electrodes identified by our algorithm. Conclusion This machine learning approach, based on features extracted from the PLV, effectively identified electrodes within the SOZ. The approach has the potential to assist clinicians in surgical decision-making when pre-surgical intracranial recordings are utilized

Low-voltage fast seizures in humans begin with increased interneuron firing

Objective: Intracellular recordings from cells in entorhinal cortex tissue slices show that low-voltage fast (LVF) onset seizures are generated by inhibitory events. Here, we determined whether increased firing of interneurons occurs at the onset of spontaneous mesial–temporal LVF seizures recorded in patients. Methods: The seizure onset zone (SOZ) was identified using visual inspection of the intracranial electroencephalogram. We used wavelet clustering and temporal autocorrelations to characterize changes in single-unit activity during the onset of LVF seizures recorded from microelectrodes in mesial–temporal structures. Action potentials generated by principal neurons and interneurons (ie, putative excitatory and inhibitory neurons) were distinguished using waveform morphology and K-means clustering. Results: From a total of 200 implanted microelectrodes in 9 patients during 13 seizures, we isolated 202 single units; 140 (69.3%) of these units were located in the SOZ, and 40 (28.57%) of them were classified as inhibitory. The waveforms of both excitatory and inhibitory units remained stable during the LVF epoch (p \u3e \u3e 0.05). In the mesial–temporal SOZ, inhibitory interneurons increased their firing rate during LVF seizure onset (p \u3c 0.01). Excitatory neuron firing rates peaked 10 seconds after the inhibitory neurons (p \u3c 0.01). During LVF spread to the contralateral mesial temporal lobe, an increase in inhibitory neuron firing rate was also observed (p \u3c 0.01). Interpretation: Our results suggest that seizure generation and spread during spontaneous mesial–temporal LVF onset events in humans may result from increased inhibitory neuron firing that spawns a subsequent increase in excitatory neuron firing and seizure evolution. Ann Neurol 2018;84:588–600

Low-voltage fast seizures in humans begin with increased interneuron firing.

ObjectiveIntracellular recordings from cells in entorhinal cortex tissue slices show that low-voltage fast (LVF) onset seizures are generated by inhibitory events. Here, we determined whether increased firing of interneurons occurs at the onset of spontaneous mesial-temporal LVF seizures recorded in patients.MethodsThe seizure onset zone (SOZ) was identified using visual inspection of the intracranial electroencephalogram. We used wavelet clustering and temporal autocorrelations to characterize changes in single-unit activity during the onset of LVF seizures recorded from microelectrodes in mesial-temporal structures. Action potentials generated by principal neurons and interneurons (ie, putative excitatory and inhibitory neurons) were distinguished using waveform morphology and K-means clustering.ResultsFrom a total of 200 implanted microelectrodes in 9 patients during 13 seizures, we isolated 202 single units; 140 (69.3%) of these units were located in the SOZ, and 40 (28.57%) of them were classified as inhibitory. The waveforms of both excitatory and inhibitory units remained stable during the LVF epoch (p &gt; &gt; 0.05). In the mesial-temporal SOZ, inhibitory interneurons increased their firing rate during LVF seizure onset (p &lt; 0.01). Excitatory neuron firing rates peaked 10 seconds after the inhibitory neurons (p &lt; 0.01). During LVF spread to the contralateral mesial temporal lobe, an increase in inhibitory neuron firing rate was also observed (p &lt; 0.01).InterpretationOur results suggest that seizure generation and spread during spontaneous mesial-temporal LVF onset events in humans may result from increased inhibitory neuron firing that spawns a subsequent increase in excitatory neuron firing and seizure evolution. Ann Neurol 2018;84:588-600