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

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.

Temporal segmentation of EEG based on functional connectivity network structure

In the study of brain functional connectivity networks, it is assumed that a network is built from a data window in which activity is stationary. However, brain activity is non-stationary over sufficiently large time periods. Addressing the analysis electroencephalograph (EEG) data, we propose a data segmentation method based on functional connectivity network structure. The goal of segmentation is to ensure that within a window of analysis, there is similar network structure. We designed an intuitive and flexible graph distance measure to quantify the difference in network structure between two analysis windows. This measure is modular: a variety of node importance indices can be plugged into it. We use a reference window versus sliding window comparison approach to detect changes, as indicated by outliers in the distribution of graph distance values. Performance of our segmentation method was tested in simulated EEG data and real EEG data from a drone piloting experiment (using correlation or phase-locking value as the functional connectivity strength metric). We compared our method under various node importance measures and against matrix-based dissimilarity metrics that use singular value decomposition on the connectivity matrix. The results show the graph distance approach worked better than matrix-based approaches; graph distance based on partial node centrality was most sensitive to network structural changes, especially when connectivity matrix values change little. The proposed method provides EEG data segmentation tailored for detecting changes in terms of functional connectivity networks. Our study provides a new perspective on EEG segmentation, one that is based on functional connectivity network structure differences.

Analysis of task-related MEG functional brain networks using dynamic mode decomposition

Objective.Functional connectivity networks explain the different brain states during the diverse motor, cognitive, and sensory functions. Extracting connectivity network configurations and their temporal evolution is crucial for understanding brain function during diverse behavioral tasks.Approach.In this study, we introduce the use of dynamic mode decomposition (DMD) to extract the dynamics of brain networks. We compared DMD with principal component analysis (PCA) using real magnetoencephalography data during motor and memory tasks.Main results.The framework generates dominant connectivity brain networks and their time dynamics during simple tasks, such as button press and left-hand movement, as well as more complex tasks, such as picture naming and memory tasks. Our findings show that the proposed methodology with both the PCA-based and DMD-based approaches extracts similar dominant connectivity networks and their corresponding temporal dynamics.Significance.We believe that the proposed methodology with both the PCA and the DMD approaches has a very high potential for deciphering the spatiotemporal dynamics of electrophysiological brain network states during tasks.

Enabling effective breathing sound analysis for automated diagnosis of lung diseases

With the emergence of the COVID-19 pandemic, early diagnosis of lung diseases has attracted growing attention. Generally, monitoring the breathing sound is the traditional means for assessing the status of a patient's respiratory health through auscultation; for that a stethoscope is one of the clinical tools used by physicians for diagnosis of lung disease and anomalies. On the other hand, recent technological advances have made telehealth systems a practical and effective option for health status assessment and remote patient monitoring. The interest in telehealth solutions have further grown with the COVID-19 pandemic. These telehealth systems aim to provide increased safety and help to cope with the massive growth in healthcare demand. Particularly, employing acoustic sensors to collect breathing sound would enable real-time assessment and instantaneous detection of anomalies. However, existing work focuses on autonomous determination of respiratory rate which is not suitable for anomaly detection due to inability to deal with noisy data recording. This paper presents a novel approach for effective breathing sound analysis. We promote a new segmentation mechanism of the captured acoustic signals to identify breathing cycles in recorded sound signals. A scoring scheme is applied to qualify the segment based on the targeted respiratory illness by the overall breathing sound analysis. We demonstrate the effectiveness of our approach via experiments using published COPD datasets.

Early classification of motor tasks using dynamic functional connectivity graphs from EEG

OBJECTIVE

Classification of electroencephalography (EEG) signals with high accuracy using short recording intervals has been a challenging problem in developing brain computer interfaces (BCIs). This paper presents a novel feature extraction method for EEG recordings to tackle this problem.

APPROACH

The proposed approach is based on the concept that the brain functions in a dynamic manner, and utilizes dynamic functional connectivity graphs. The EEG data is first segmented into intervals during which functional networks sustain their connectivity. Functional connectivity networks for each identified segment are then localized, and graphs are constructed, which will be used as features. To take advantage of the dynamic nature of the generated graphs, a Long Short Term Memory (LSTM) classifier is employed for classification.

MAIN RESULTS

Features extracted from various durations of post-stimulus EEG data associated with motor execution and imagery tasks are used to test the performance of the classifier. Results show an average accuracy of 85.32% using features extracted from only 500 ms of the post-stimulus data.

SIGNIFICANCE

Our results demonstrate, for the first time, that using the proposed feature extraction method, it is possible to classify motor tasks from EEG recordings using a short interval of the data in the order of hundreds of milliseconds (e.g. 500 ms). This duration is considerably shorter than what has been reported before. These results will have significant implications for improving the effectiveness and the speed of BCIs, particularly for those used in assistive technologies.

Classification of EEG Data Based on the Spatio-Temporo-Rhythmic Characteristics of the Task-Discriminating Functional Sub-networks

We propose a new approach that utilizes the dynamic state of cortical functional connectivity for the classification of task-based electroencephalographic (EEG) data. We introduce a novel feature extraction framework that locates functional networks in the cortex as they convene at different time intervals across different frequency bands. The framework starts by applying the wavelet transform to isolate, then augment, EEG frequency bands. Next, the time intervals of stationary functional states, within the augmented data, are identified using the source-informed segmentation algorithm. Functional networks are localized in the brain, during each segment, using a singular value decomposition-based approach. For feature selection, we propose a discriminative-associative algorithm, and use it to find the sub-networks showing the highest recurrence rate differences across the target tasks. The sequences of augmented functional networks are projected onto the identified sub-networks, for the final sequences of features. A dynamic recurrent neural network classifier is then used for classification. The proposed approach is applied to experimental EEG data to classify motor execution and motor imagery tasks. Our results show that an accuracy of 90% can be achieved within the first 500 msec of the cued task-planning phase.

Recognizing Pain in Motor Imagery EEG Recordings Using Dynamic Functional Connectivity Graphs

The goal of this paper is to investigate whether motor imagery tasks, performed under pain-free versus pain conditions, can be discriminated from electroencephalography (EEG) recordings. Four motor imagery classes of right hand, left hand, foot, and tongue are considered. A functional connectivity-based feature extraction approach along with a long short-term memory (LSTM) classifier are employed for classifying pain-free versus under-pain classes. Moreover, classification is performed in different frequency bands to study the significance of each band in differentiating motor imagery data associated with pain-free and under-pain states. When considering all frequency bands, the average classification accuracy is in the range of 77:86-80:04%. Our frequency-specific analysis shows that the gamma band results in a notably higher accuracy than other bands, indicating the importance of this band in discriminating pain/no-pain conditions during the execution of motor imagery tasks. In contrast, functional connectivity graphs extracted from delta and theta bands do not seem to provide discriminatory information between pain-free and under-pain conditions. This is the first study demonstrating that motor imagery tasks executed under pain and without pain conditions can be discriminated from EEG recordings. Our findings can provide new insights for developing effective brain computer interface-based assistive technologies for patients who are in real need of them.