convolutional neural networks

FACT-Net: a Frequency Adapter CNN with Temporal-periodicity Inception for Fast and Accurate MI-EEG Decoding
FACT-Net: a Frequency Adapter CNN with Temporal-periodicity Inception for Fast and Accurate MI-EEG Decoding 150 150 Transactions on Neural Systems and Rehabilitation Engineering (TNSRE)
Motor imagery brain-computer interface (MI-BCI) based on non-invasive electroencephalogram (EEG) signals is a typical paradigm of BCI. However, existing decoding methods face significant challenges in terms of signal decoding accuracy,… read more

Adapting Action Recognition Neural Networks for Automated Infantile Spasm Detection
Adapting Action Recognition Neural Networks for Automated Infantile Spasm Detection 150 150 Transactions on Neural Systems and Rehabilitation Engineering (TNSRE)
Infantile spasms are a severe epileptic syndrome characterized by short muscular contractions lasting from 0.5 to 2 seconds. They are often misdiagnosed due to their atypical presentation, and treatment is… read more

Early Detection of Parkinson’s Disease Using Deep NeuroEnhanceNet With Smartphone Walking Recordings
Early Detection of Parkinson’s Disease Using Deep NeuroEnhanceNet With Smartphone Walking Recordings 150 150 Transactions on Neural Systems and Rehabilitation Engineering (TNSRE)
With the development of digital medical technology, ubiquitous smartphones are emerging as valuable tools for the detection of complex and elusive diseases. This paper exploits smartphone walking recording for early… read more

A Combination Model of Shifting Joint Angle Changes With 3D-Deep Convolutional Neural Network to Recognize Human Activity
A Combination Model of Shifting Joint Angle Changes With 3D-Deep Convolutional Neural Network to Recognize Human Activity 150 150 Transactions on Neural Systems and Rehabilitation Engineering (TNSRE)
Research in the field of human activity recognition is very interesting due to its potential for various applications such as in the field of medical rehabilitation. The need to advance… read more
Learning Spatial–Spectral–Temporal EEG Features With Recurrent 3D Convolutional Neural Networks for Cross-Task Mental Workload Assessment

Learning Spatial–Spectral–Temporal EEG Features With Recurrent 3D Convolutional Neural Networks for Cross-Task Mental Workload Assessment
Author(s)3: Pengbo Zhang, Xue Wang, Weihang Zhang, Junfeng Chen
Learning Spatial–Spectral–Temporal EEG Features With Recurrent 3D Convolutional Neural Networks for Cross-Task Mental Workload Assessment 780 395 Transactions on Neural Systems and Rehabilitation Engineering (TNSRE)

    Mental workload assessment is essential for maintaining human health and preventing accidents. Most research on this issue is limited to a single task. However, cross-task assessment is indispensable for…

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