The Feeling of Time Passing Is Associated with Recurrent Sustained Activity and Theta Rhythms Across the Cortex.

Introduction: We are constantly estimating how much time has passed, and yet know little about the brain mechanisms through which this process occurs. In this pilot study, we evaluated so-called subjective time estimation with the temporal bisection task, while recording brain activity from electroencephalography (EEG). Methods: Nine adult participants were trained to distinguish between two durations of visual stimuli as either "short" (400 msec) or "long" (1600 msec). They were then presented with stimulus durations in between the long and short stimuli. EEG data from 128 electrodes were examined with a novel analytical method that identifies segments of sustained cortical activity during the task. Results: Participants tended to categorize intermediate durations as "long" more frequently than "short" and were thus experiencing time as moving faster while overestimating the amount of time passing. Their mean bisection point (during which frequency of selecting short vs. long is equal) was closer to the geometric mean of task stimuli (800 msec) rather than the arithmetic mean (1000 msec). In contrast, sustained brain activity occurred closer to the arithmetic mean. The recurrence rate of this activity was highly related to the bisection point, especially when analyzed within naturally occurring theta oscillations (4-8 Hz) (r = -0.90). Discussion: Sustained activity across the cortex within the theta range may reflect temporal durations, whereas its repeated appearance relates to the subjective feeling of time passing.

Source-Informed Segmentation: A Data-Driven Approach for the Temporal Segmentation of EEG.

GOAL: Understanding the dynamics of brain function through non-invasive monitoring techniques requires the development of computational methods that can deal with the non-stationary properties of recorded activities. As a solution to this problem, a new data-driven segmentation method for recordings obtained through electroencephalography (EEG) is presented. METHODS: The proposed method utilizes singular value decomposition (SVD) to identify the time intervals in the EEG recordings during which the spatial distribution of clusters of active cortical neurons remains quasi-stationary. Theoretical analysis shows that the spatial locality features of these clusters can be, asymptotically, captured by the most significant left singular subspace of the EEG data. A reference/sliding window approach is employed to dynamically extract this feature subspace, and the running projection error is monitored for significant changes using Kolmogorov-Smirnov test. RESULTS: Simulation results, for a wide range of possible scenarios regarding the spatial distribution of active cortical neurons, show that the algorithm is successful in accurately detecting the segmental structure of the simulated EEG data. The algorithm is also applied to experimental EEG recordings of a modified visual oddball task. Results identify a unique sequence of dynamic patterns in the event-related potential (ERP) response to each of the three involved stimuli. CONCLUSION: The proposed method, without using source localization methods or scalp topographical maps, is able to identify intervals of quasi-stationarity in the EEG recordings. SIGNIFICANCE: The proposed segmentation technique can offer new insights on the dynamics of functional organization of the brain in action.

On the Spatiotemporal Characteristics of Class-Discriminating Functional Networks.

We target the problem of identifying brain's functional networks that are discriminatory across classes of tasks, using data obtained through electroencephalography (EEG). A three-step framework is presented. First, the EEG data is segmented to identify the intervals during which cortical functional networks remain quasi-stationary. Second, these functional networks are spatially localized in the cortex. Finally, by employing the proposed discriminative Boolean matrix factorization (DBMF) algorithm, functional networks that are most recurrent in one class of tasks, but are least recurrent in the other are identified. The DBMF algorithm is capable of providing the spatial maps of the discriminative functional networks as well as information about their dynamic occurrence over time. The framework is applied to experimental EEG data, recorded during a motor task. The results show that the proposed framework identifies several parietal/motor functional networks as being the most discriminatory for motor execution trials from non-execution trials.

Global EEG segmentation using singular value decomposition.

In this paper, we propose a method based on singular value decomposition (SVD) for segmenting multichannel electroencephalography (EEG) data into temporal blocks during which the spatial distributions of the underlying active neuronal generators stay fixed. We locate segment boundaries by statistically comparing the residual error resulting from projecting the data under a reference window, on one hand, and a sliding window, on the other hand, onto a feature subspace. The basis of this subspace is the most significant left eigenvectors of the data block under the reference window. The statistical testing is performed using the Kolmogorov-Smirnov (K-S) test. To enhance the reliability of the K-S test, the consecutive K-S decisions are aggregated under a given decision window. Simulation results confirm that the proposed algorithm can successfully detect segment boundaries under a wide range of different conditions.