This systematic review offers a world-first critical analysis of machine learning methods and
systems, along with future directions for the study of cybersickness induced by virtual reality …

Object detection is an essential task in computer vision and image processing. It has many
applications in various domains like medical diagnosis, civil military, video surveillance …

Emotion recognition still poses a challenge lying at the core of the rapidly growing area of
affective computing and is crucial for establishing a successful human–computer interaction …

Spiking neural network (SNN) based on sparse triggering and event-driven is a hardware-
friendly model. SNN can provide an ultra-low power alternative for the deep neural network …

Spiking neural networks (SNNs) are brain-inspired machine learning algorithms with merits
such as biological plausibility and unsupervised learning capability. Previous works have …

Spiking neural network (SNN) is a new generation of artificial neural networks (ANNs),
which is more analogous with the brain. It has been widely considered with neural …

In recent years, there has been a growing demand for miniaturization, low power
consumption, quick treatments, and non-invasive clinical strategies in the healthcare …

Using multimodal signals to solve the problem of emotion recognition is one of the emerging
trends in affective computing. Several studies have utilized state of the art deep learning …

Electroencephalogram (EEG) data are produced in quantity for measuring brain activity in
response to external stimuli. With the rapid development of brain-inspired intelligence …

The neuron model as a computational unit not only determines the performance of widely
used deep neural networks and emerging quantum neural networks, but in turn facilitates …