Development of a miniaturized and modular probe for fNIRS instrument.

Functional near-infrared spectroscopy (fNIRS) is a non-invasive and promising method for continuously monitoring hemodynamic and metabolic changes in tissues. However, the existing fNIRS equipment uses optical fiber, which is bulky, expensive, and time-consuming. We present a miniaturized, modular, novel silicon photomultiplier (SiPM) detector and develop a fNIRS instrument aimed at investigating the cerebral hemodynamic response for patients with epilepsy. Light emitting probe is a circle with a diameter of 5 mm. Independent and modular light source and detector are more flexible in placement. The system can be expanded to high-density measurement with 16 light sources, 16 detectors, and 52 channels. The sampling rate of each channel is 25 Hz. Instrument performance was evaluated using brain tissue phantom and in vivo experiments. High signal-to-noise ratio (60 dB) in source detector separation (SDS) of 30 mm, good stability (0.1%), noise equivalent power (0.89 pW), and system drift (0.56%) were achieved in the phantom experiment. Forearm blood-flow occlusion experiments were performed on the forearm of three healthy volunteers to demonstrate the ability to track rapid hemodynamic changes. Breath holding experiments on the forehead of healthy volunteers demonstrated the system can well detect brain function activity. The computer software was developed to display the original light signal intensity and the concentration changes of oxygenated hemoglobin (HbO2) and deoxygenated hemoglobin (HbR) in real time. This system paves the way for our further diagnosis of epilepsy.

Application of Electroencephalography-Based Machine Learning in Emotion Recognition: A Review.

Emotion recognition has become increasingly prominent in the medical field and human-computer interaction. When people's emotions change under external stimuli, various physiological signals of the human body will fluctuate. Electroencephalography (EEG) is closely related to brain activity, making it possible to judge the subject's emotional changes through EEG signals. Meanwhile, machine learning algorithms, which are good at digging out data features from a statistical perspective and making judgments, have developed by leaps and bounds. Therefore, using machine learning to extract feature vectors related to emotional states from EEG signals and constructing a classifier to separate emotions into discrete states to realize emotion recognition has a broad development prospect. This paper introduces the acquisition, preprocessing, feature extraction, and classification of EEG signals in sequence following the progress of EEG-based machine learning algorithms for emotion recognition. And it may help beginners who will use EEG-based machine learning algorithms for emotion recognition to understand the development status of this field. The journals we selected are all retrieved from the Web of Science retrieval platform. And the publication dates of most of the selected articles are concentrated in 2016-2021.

Minireview of Epilepsy Detection Techniques Based on Electroencephalogram Signals.

Epilepsy is one of the most common neurological disorders typically characterized by recurrent and uncontrollable seizures, which seriously affects the quality of life of epilepsy patients. The effective tool utilized in the clinical diagnosis of epilepsy is the Electroencephalogram (EEG). The emergence of machine learning promotes the development of automated epilepsy detection techniques. New algorithms are continuously introduced to shorten the detection time and improve classification accuracy. This minireview summarized the latest research of epilepsy detection techniques that focused on acquiring, preprocessing, feature extraction, and classification of epileptic EEG signals. The application of seizure prediction and localization based on EEG signals in the diagnosis of epilepsy was also introduced. And then, the future development trend of epilepsy detection technology has prospected at the end of the article.