EEG-Oriented Self-Supervised Learning With Triple Information Pathways Network.

Recently, deep learning-based electroencephalogram (EEG) analysis and decoding have attracted widespread attention for monitoring the clinical condition of users and identifying their intention/emotion. Nevertheless, the existing methods generally model EEG signals with limited viewpoints or restricted concerns about the characteristics of the EEG signals, and thus represent complex spectro-/spatiotemporal patterns and suffer from high variability. In this work, we propose the novel EEG-oriented self-supervised learning methods and a novel deep architecture to learn rich representation, including information about the diverse spectral characteristics of neural oscillations, the spatial properties of electrode sensor distribution, and the temporal patterns of both the global and local viewpoints.

Gender-Related and Hemispheric Effects in Cortical Thickness-Based Hemispheric Brain Morphological Network.

Objective: The current study examined gender-related differences in hemispheric asymmetries of graph metrics, calculated from a cortical thickness-based brain structural covariance network named hemispheric morphological network.

Methods: Using the T1-weighted magnetic resonance imaging scans of 285 participants (150 females, 135 males) retrieved from the Human Connectome Project (HCP), hemispheric morphological networks were constructed per participant. In these hemispheric morphologic networks, the degree of similarity between two different brain regions in terms of the distributed patterns of cortical thickness values (the Jensen-Shannon divergence) was defined as weight of network edge that connects two different brain regions.

Dynamic activity classification based on automatic adaptation of postural orientation.

We propose a dynamic activity classification system with tri-axial accelerometer sensor using adaptation of user's postural orientation. In general, the sensor module is worn at a fixed position such as waist, head, wrist, thigh, and so on. However, in reality, the tilt of the attached sensor could be changed from time to time in actions such as sitting down, standing up, lying, walking or running.

An effective method for component activity classification supporting location awareness and user identification.

We present an effective method for component activity classification supporting location awareness and user identification at the same time. The system is comprised of three modules: Pressure Sensing Module (PSM), Activity Detecting Module (ADM), and Receiving Station (RS). The ADM having a unique id is a wearable module putting on one's waist-belt, which classifies component activity such as sitting chair, lying bed, sitting sofa, etc.

Toward real time detection of the basic living activity in home using a wearable sensor and smart home sensors.

As the elderly people living alone are enormously increasing recently, we need the system inferring activities of daily living (ADL) for maintaining healthy life and recognizing emergency. The system should be constructed with sensors, which are used to associate with people's living while remaining as non intrusive views as possible. To do this, the proposed system use a triaxial accelerometer sensor and environment sensors indicating contact with subject in home.

Semantic tagging for medical knowledge tracking.

We propose a semantic tagger that provides high level concept information for phrases in clinical documents, which enriches medical information tracking system that support decision making or quality assurance of medical treatment. In this paper, we have tried to deal with patient records written by doctors rather than well-formed documents such as Medline abstracts. In addition, annotating clinical text on phrases semantically rather than syntactically has been attempted, which are at higher level granularity than words that have been the target for most tagging work.