[PDF][PDF] Deep unsupervised domain adaptation: A review of recent advances and perspectives
Deep learning has become the method of choice to tackle real-world problems in different
domains, partly because of its ability to learn from data and achieve impressive performance …
domains, partly because of its ability to learn from data and achieve impressive performance …
Beyond supervised learning for pervasive healthcare
The integration of machine/deep learning and sensing technologies is transforming
healthcare and medical practice. However, inherent limitations in healthcare data, namely …
healthcare and medical practice. However, inherent limitations in healthcare data, namely …
Deep unsupervised domain adaptation with time series sensor data: A survey
Sensors are devices that output signals for sensing physical phenomena and are widely
used in all aspects of our social production activities. The continuous recording of physical …
used in all aspects of our social production activities. The continuous recording of physical …
A survey on deep learning-based short/zero-calibration approaches for EEG-based brain–computer interfaces
Brain–computer interfaces (BCIs) utilizing machine learning techniques are an emerging
technology that enables a communication pathway between a user and an external system …
technology that enables a communication pathway between a user and an external system …
Single-trial EEG classification with EEGNet and neural structured learning for improving BCI performance
Research and development of new machine learning techniques to augment the
performance of Brain-computer Interfaces (BCI) have always been an open area of interest …
performance of Brain-computer Interfaces (BCI) have always been an open area of interest …
Deep learning based prediction of EEG motor imagery of stroke patients' for neuro-rehabilitation application
H Raza, A Chowdhury… - 2020 International Joint …, 2020 - ieeexplore.ieee.org
Due to the non-stationary nature of electroencephalography (EEG) signals, a Brain-
computer Interfacing (BCI) system requires frequent calibration. This leads to inter session …
computer Interfacing (BCI) system requires frequent calibration. This leads to inter session …
The effects of individual differences, non-stationarity, and the importance of data partitioning decisions for training and testing of EEG cross-participant models
A Kamrud, B Borghetti, C Schubert Kabban - Sensors, 2021 - mdpi.com
EEG-based deep learning models have trended toward models that are designed to perform
classification on any individual (cross-participant models). However, because EEG varies …
classification on any individual (cross-participant models). However, because EEG varies …
Attentive adversarial network for large-scale sleep staging
S Nasiri, GD Clifford - Machine Learning for Healthcare …, 2020 - proceedings.mlr.press
Current approaches to developing a generalized automated sleep staging method rely on
constructing a large labeled training and test corpora by leveraging electroencephalograms …
constructing a large labeled training and test corpora by leveraging electroencephalograms …
Location-Aware Encoding for Lesion Detection in Ga-DOTATATE Positron Emission Tomography Images
Objective: Lesion detection with positron emission tomography (PET) imaging is critical for
tumor staging, treatment planning, and advancing novel therapies to improve patient …
tumor staging, treatment planning, and advancing novel therapies to improve patient …
Unsupervised Detection of Covariate Shift Due to Changes in EEG Headset Position: Towards an Effective Out-of-Lab Use of Passive Brain–Computer Interface
In the field of passive Brain–computer Interfaces (BCI), the need to develop systems that
require rapid setup, suitable for use outside of laboratories is a fundamental challenge …
require rapid setup, suitable for use outside of laboratories is a fundamental challenge …