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Found 18 papers, 6 papers with code
Combining EEG and NLP Features for Predicting Students' Lecture Comprehension using Ensemble Classification
In this work, we propose a classification framework for predicting students' lecture comprehension in two tasks: (i) students' confusion after listening to the simulated lecture and (ii) the correctness of students' responses to the post-lecture assessment.
PseudoCell: Hard Negative Mining as Pseudo Labeling for Deep Learning-Based Centroblast Cell Detection
Depending on the confidence threshold, PseudoCell can eliminate 58. 18-99. 35% of non-centroblasts tissue areas on WSI.
ApSense: Data-driven Algorithm in PPG-based Sleep Apnea Sensing
This paper contributes to developing fingertip PPG-based obstructive sleep apnea (OSA) event onset recognition.
PACMAN: a framework for pulse oximeter digit detection and reading in a low-resource setting
In light of the COVID-19 pandemic, patients were required to manually input their daily oxygen saturation (SpO2) and pulse rate (PR) values into a health monitoring system-unfortunately, such a process trend to be an error in typing.
ANet: Autoencoder-Based Local Field Potential Feature Extractor for Evaluating An Antidepressant Effect in Mice after Administering Kratom Leaf Extracts
Here, we adopted an autoencoder (AE)-based anomaly detector called ANet to measure the similarity of mice's local field potential (LFP) features that responded to KT leave extracts and AD flu.
RRWaveNet: A Compact End-to-End Multi-Scale Residual CNN for Robust PPG Respiratory Rate Estimation
This study proposes a method for continuously estimating RR, RRWaveNet.
EEG-BBNet: a Hybrid Framework for Brain Biometric using Graph Connectivity
The benefit of the CNN in automatic feature extraction and the capability of GCNN in learning connectivity between EEG electrodes through graph representation are jointly exploited.
OCTAve: 2D en face Optical Coherence Tomography Angiography Vessel Segmentation in Weakly-Supervised Learning with Locality Augmentation
In this work, we propose the application of the scribble-base weakly-supervised learning method to automate the pixel-level annotation.
Ranked #1 on
Retinal Vessel Segmentation
on ROSE-2
Deep Reinforcement Learning Models Predict Visual Responses in the Brain: A Preliminary Result
Supervised deep convolutional neural networks (DCNNs) are currently one of the best computational models that can explain how the primate ventral visual stream solves object recognition.
A Pilot Study on Visually Stimulated Cognitive Tasks for EEG-Based Dementia Recognition
The efficiencies of EEG signals from various cognitive tasks, for dementia classification, have yet to be thoroughly investigated.
MIN2Net: End-to-End Multi-Task Learning for Subject-Independent Motor Imagery EEG Classification
We integrate deep metric learning into a multi-task autoencoder to learn a compact and discriminative latent representation from EEG and perform classification simultaneously.
SleepPoseNet: Multi-View Learning for Sleep Postural Transition Recognition Using UWB
Recognizing movements during sleep is crucial for the monitoring of patients with sleep disorders, and the utilization of ultra-wideband (UWB) radar for the classification of human sleep postures has not been explored widely.
MetaSleepLearner: A Pilot Study on Fast Adaptation of Bio-signals-Based Sleep Stage Classifier to New Individual Subject Using Meta-Learning
This is the first work that investigated a non-conventional pre-training method, MAML, resulting in a possibility for human-machine collaboration in sleep stage classification and easing the burden of the clinicians in labelling the sleep stages through only several epochs rather than an entire recording.
Decoding EEG Rhythms During Action Observation, Motor Imagery, and Execution for Standing and Sitting
Event-related desynchronization and synchronization (ERD/S) and movement-related cortical potential (MRCP) play an important role in brain-computer interfaces (BCI) for lower limb rehabilitation, particularly in standing and sitting.
Human-Computer Interaction Signal Processing
Parkinson's Disease Recognition Using SPECT Image and Interpretable AI: A Tutorial
Even though there are multiple interpretation methods available for the DL model, there is no evidence of which method is suitable for PD recognition application.
Towards Asynchronous Motor Imagery-Based Brain-Computer Interfaces: a joint training scheme using deep learning
The focus is mainly on three types of brain responses: non-imagery EEG (\textit{background EEG}), (\textit{pure imagery}) EEG, and EEG during the transitional period between background EEG and pure imagery (\textit{transitional imagery}).
Automatic Lung Cancer Prediction from Chest X-ray Images Using Deep Learning Approach
al., along with the transfer learning scheme was explored as a means to classify lung cancer using chest X-ray images.
Affective EEG-Based Person Identification Using the Deep Learning Approach
\textcolor{red}{We proposed a cascade of deep learning using a combination of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs)}.
