Development and progress in sensors and technologies for human emotion recognition
S Pal, S Mukhopadhyay, N Suryadevara - Sensors, 2021 - mdpi.com
With the advancement of human-computer interaction, robotics, and especially humanoid
robots, there is an increasing trend for human-to-human communications over online …
robots, there is an increasing trend for human-to-human communications over online …
A review of algorithm & hardware design for AI-based biomedical applications
Y Wei, J Zhou, Y Wang, Y Liu, Q Liu… - IEEE transactions on …, 2020 - ieeexplore.ieee.org
This paper reviews the state of the arts and trends of the AI-Based biomedical processing
algorithms and hardware. The algorithms and hardware for different biomedical applications …
algorithms and hardware. The algorithms and hardware for different biomedical applications …
EEG signal processing and supervised machine learning to early diagnose Alzheimer's disease
D Pirrone, E Weitschek, P Di Paolo, S De Salvo… - Applied sciences, 2022 - mdpi.com
Electroencephalography (EEG) signal analysis is a fast, inexpensive, and accessible
technique to detect the early stages of dementia, such as Mild Cognitive Impairment (MCI) …
technique to detect the early stages of dementia, such as Mild Cognitive Impairment (MCI) …
Expression-EEG based collaborative multimodal emotion recognition using deep autoencoder
H Zhang - IEEE Access, 2020 - ieeexplore.ieee.org
Emotion recognition has shown many valuable roles in people's lives under the background
of artificial intelligence technology. However, most existing emotion recognition methods …
of artificial intelligence technology. However, most existing emotion recognition methods …
[HTML][HTML] Neural interface systems with on-device computing: machine learning and neuromorphic architectures
Highlights•Neural interfaces continue to improve in channel count and form factor.•Low-
power machine learning and neuromorphic processors can be integrated onto neural …
power machine learning and neuromorphic processors can be integrated onto neural …
Emotion recognition using heterogeneous convolutional neural networks combined with multimodal factorized bilinear pooling
Y Zhang, C Cheng, S Wang, T Xia - Biomedical Signal Processing and …, 2022 - Elsevier
Multimodal emotion recognition is one of the challenging topics in the field of knowledge-
based systems and many methods have been studied successfully. Nevertheless …
based systems and many methods have been studied successfully. Nevertheless …
Closed-loop neural prostheses with on-chip intelligence: A review and a low-latency machine learning model for brain state detection
The application of closed-loop approaches in systems neuroscience and therapeutic
stimulation holds great promise for revolutionizing our understanding of the brain and for …
stimulation holds great promise for revolutionizing our understanding of the brain and for …
RETRACTED ARTICLE: A review of Deep Learning based methods for Affect Analysis using Physiological Signals
Emotions are distinct reactions to internal or external events with implications for the
organism. Automatic emotion recognition is a demanding task for pattern recognition and a …
organism. Automatic emotion recognition is a demanding task for pattern recognition and a …
An on-chip processor for chronic neurological disorders assistance using negative affectivity classification
Chronic neurological disorders (CND's) are lifelong diseases and cannot be eradicated, but
their severe effects can be alleviated by early preemptive measures. CND's, such as …
their severe effects can be alleviated by early preemptive measures. CND's, such as …
Exploring convolutional neural network architectures for EEG feature extraction
The main purpose of this paper is to provide information on how to create a convolutional
neural network (CNN) for extracting features from EEG signals. Our task was to understand …
neural network (CNN) for extracting features from EEG signals. Our task was to understand …