A comparison of visual and auditory EEG interfaces for robot multi-stage task control.

Shared autonomy holds promise for assistive robotics, whereby physically-impaired people can direct robots to perform various tasks for them. However, a robot that is capable of many tasks also introduces many choices for the user, such as which object or location should be the target of interaction. In the context of non-invasive brain-computer interfaces for shared autonomy-most commonly electroencephalography-based-the two most common choices are to provide either auditory or visual stimuli to the user-each with their respective pros and cons.

Deep neural network-based classification of cardiotocograms outperformed conventional algorithms.

Cardiotocography records fetal heart rates and their temporal relationship to uterine contractions. To identify high risk fetuses, obstetricians inspect cardiotocograms (CTGs) by eye. Therefore, CTG traces are often interpreted differently among obstetricians, resulting in inappropriate interventions.

Simultaneous monitoring of mouse respiratory and cardiac rates through a single precordial electrode.

Normal respiratory and circulatory functions are crucial for survival. However, conventional methods of monitoring respiration, some of which use sensors inserted into the nasal cavity, may interfere with naïve respiratory rates. In this study, we conducted a single-point measurement of electrocardiograms (ECGs) from the pectoral muscles of anesthetized and waking mice and found low-frequency oscillations in the ECG baseline.

[Machine Learning-based Prediction of Seizure-inducing Action as an Adverse Drug Effect].

　During the preclinical research period of drug development, animal testing is widely used to help screen out a drug's dangerous side effects. However, it remains difficult to predict side effects within the central nervous system. Here, we introduce a machine learning-based in vitro system designed to detect seizure-inducing side effects before clinical trial.