Interpretable Cross-Subject EEG-Based Emotion Recognition Using Channel-Wise Features.

Electroencephalogram (EEG)-based emotion recognition is receiving significant attention in research on brain-computer interfaces (BCI) and health care. To recognize cross-subject emotion from EEG data accurately, a technique capable of finding an effective representation robust to the subject-specific variability associated with EEG data collection processes is necessary. In this paper, a new method to predict cross-subject emotion using time-series analysis and spatial correlation is proposed. To represent the spatial connectivity between brain regions, a channel-wise feature is proposed, which can effectively handle the correlation between all channels. The channel-wise feature is defined by a symmetric matrix, the elements of which are calculated by the Pearson correlation coefficient between two-pair channels capable of complementarily handling subject-specific variability. The channel-wise features are then fed to two-layer stacked long short-term memory (LSTM), which can extract temporal features and learn an emotional model. Extensive experiments on two publicly available datasets, the Dataset for Emotion Analysis using Physiological Signals (DEAP) and the SJTU (Shanghai Jiao Tong University) Emotion EEG Dataset (SEED), demonstrate the effectiveness of the combined use of channel-wise features and LSTM. Experimental results achieve state-of-the-art classification rates of 98.93% and 99.10% during the two-class classification of valence and arousal in DEAP, respectively, with an accuracy of 99.63% during three-class classification in SEED.

A rare case of gastric-type mucinous adenocarcinoma in a woman with Peutz-Jeghers syndrome.

Adenocarcinoma of the cervix is less common than squamous cell carcinoma. Minimal deviation adenocarcinoma (adenoma malignum) is considered an extremely well-differentiated variant of GAS. An association exists between GAS and Peutz-Jeghers syndrome, which is a rare autosomal dominant disorder characterized by mucocutaneous pigmentation and multiple hamartomatous polyps in the gastrointestinal tracts. The incidence of GAS in patients with Peutz-Jeghers syndrome is estimated to be 11-17%. We present a rare case of adenoma malignum, diagnosed using colposcopic biopsy in a woman with Peutz-Jeghers syndrome, which was histopathologically confirmed to be GAS after surgery.

Prediction of vascular access stenosis: Blood temperature monitoring with the Twister versus static intra-access pressure ratio.

BACKGROUND: The Kidney Disease Outcomes Quality Initiative (KDOQI) guidelines recommend intra-access flow (Qa) measurement as the preferred vascular access surveillance method over static intra-access pressure ratio (SIAPR). Recently, it has become possible to perform Qa measurement during hemodialysis using thermodilution method called blood temperature monitoring (BTM) with the Twister device. The aim of this study was to investigate the correlation between Qa by BTM and SIAPR and to compare the performance of two tests in prediction of vascular access stenosis. METHODS: The study was performed from January 2016 to November 2017 and included 97 patients with arteriovenous fistulas (AVF). Qa by BTM and SIAPR were simultaneously measured every 1~3 months with a total of 449 measurements during study period. RESULTS: In our study population, mean age was 59.9±10.0 years and 61.9% were diabetes. The mean Qa obtained by BTM was 1186±588 mL/min. There was no correlation between Qa by BTM and venous SIAPR (r = 0.061, P = 0.196). Angiography identified 36 stenotic AVFs (37.1%) among the study subjects. They included 13 cases with only inflow stenosis, 6 with only outflow stenosis, and 17 with stenosis on both sides. Receiver-operating characteristic (ROC) curve analysis showed that Qa by BTM had higher discriminative ability to diagnose vascular access stenosis compared to SIAPR (P <0.001). The Qa less than 583 mL/min showed the highest diagnostic accuracy in vascular stenosis prediction. CONCLUSION: Intradialytic measurement of Qa by BTM showed better diagnostic power over venous SIAPR in prediction of vascular access stenosis.

A Vision-Based Wayfinding System for Visually Impaired People Using Situation Awareness and Activity-Based Instructions.

A significant challenge faced by visually impaired people is 'wayfinding', which is the ability to find one's way to a destination in an unfamiliar environment. This study develops a novel wayfinding system for smartphones that can automatically recognize the situation and scene objects in real time. Through analyzing streaming images, the proposed system first classifies the current situation of a user in terms of their location. Next, based on the current situation, only the necessary context objects are found and interpreted using computer vision techniques. It estimates the motions of the user with two inertial sensors and records the trajectories of the user toward the destination, which are also used as a guide for the return route after reaching the destination. To efficiently convey the recognized results using an auditory interface, activity-based instructions are generated that guide the user in a series of movements along a route. To assess the effectiveness of the proposed system, experiments were conducted in several indoor environments: the sit in which the situation awareness accuracy was 90% and the object detection false alarm rate was 0.016. In addition, our field test results demonstrate that users can locate their paths with an accuracy of 97%.

Wheelchair Navigation System for Disabled and Elderly People.

An intelligent wheelchair (IW) system is developed in order to support safe mobility for disabled or elderly people with various impairments. The proposed IW offers two main functions: obstacle detection and avoidance, and situation recognition. First, through a combination of a vision sensor and eight ultrasonic ones, it detects diverse obstacles and produces occupancy grid maps (OGMs) that describe environmental information, including the positions and sizes of obstacles, which is then given to the learning-based algorithm. By learning the common patterns among OGMs assigned to the same directions, the IW can automatically find paths to prevent collisions with obstacles. Second, it distinguishes a situation whereby the user is standing on a sidewalk, traffic intersection, or roadway through analyzing the texture and shape of the images, which aids in preventing any accidents that would result in fatal injuries to the user, such as collisions with vehicles. From the experiments that were performed in various environments, we can prove the following: (1) the proposed system can recognize different types of outdoor places with 98.3% accuracy; and (2) it can produce paths that avoid obstacles with 92.0% accuracy.

3D Markov Process for Traffic Flow Prediction in Real-Time.

Recently, the correct estimation of traffic flow has begun to be considered an essential component in intelligent transportation systems. In this paper, a new statistical method to predict traffic flows using time series analyses and geometric correlations is proposed. The novelty of the proposed method is two-fold: (1) a 3D heat map is designed to describe the traffic conditions between roads, which can effectively represent the correlations between spatially- and temporally-adjacent traffic states; and (2) the relationship between the adjacent roads on the spatiotemporal domain is represented by cliques in MRF and the clique parameters are obtained by example-based learning. In order to assess the validity of the proposed method, it is tested using data from expressway traffic that are provided by the Korean Expressway Corporation, and the performance of the proposed method is compared with existing approaches. The results demonstrate that the proposed method can predict traffic conditions with an accuracy of 85%, and this accuracy can be improved further.

Vision based interface system for hands free control of an Intelligent Wheelchair.

BACKGROUND: Due to the shift of the age structure in today's populations, the necessities for developing the devices or technologies to support them have been increasing. Traditionally, the wheelchair, including powered and manual ones, is the most popular and important rehabilitation/assistive device for the disabled and the elderly. However, it is still highly restricted especially for severely disabled. As a solution to this, the Intelligent Wheelchairs (IWs) have received considerable attention as mobility aids. The purpose of this work is to develop the IW interface for providing more convenient and efficient interface to the people the disability in their limbs. METHODS: This paper proposes an intelligent wheelchair (IW) control system for the people with various disabilities. To facilitate a wide variety of user abilities, the proposed system involves the use of face-inclination and mouth-shape information, where the direction of an IW is determined by the inclination of the user's face, while proceeding and stopping are determined by the shapes of the user's mouth. Our system is composed of electric powered wheelchair, data acquisition board, ultrasonic/infra-red sensors, a PC camera, and vision system. Then the vision system to analyze user's gestures is performed by three stages: detector, recognizer, and converter. In the detector, the facial region of the intended user is first obtained using Adaboost, thereafter the mouth region is detected based on edge information. The extracted features are sent to the recognizer, which recognizes the face inclination and mouth shape using statistical analysis and K-means clustering, respectively. These recognition results are then delivered to the converter to control the wheelchair. RESULT & CONCLUSION: The advantages of the proposed system include 1) accurate recognition of user's intention with minimal user motion and 2) robustness to a cluttered background and the time-varying illumination. To prove these advantages, the proposed system was tested with 34 users in indoor and outdoor environments and the results were compared with those of other systems, then the results showed that the proposed system has superior performance to other systems in terms of speed and accuracy. Therefore, it is proved that proposed system provided a friendly and convenient interface to the severely disabled people.