Study uses CNNs to detect sleep arousals more accurately.
problem Accurate detection of sleep arousals for better sleep quality assessment.
method Investigated five 1D CNN models on EEG signals from PhysioNet/Computing in Cardiology Challenge 2018.
result Best 1D CNN model achieved high precision and recall metrics.
This research improves EEG-based MI-BCI systems to be more resilient to emotional arousal.
problem Lack of robustness in EEG-based MI-BCI systems due to emotional arousal.
method Subjects were exposed to VR environments to induce high and low arousal states. Machine learning models were trained on proxy subjects instead of arousal states. MI models were trained for each subject.
result MI-BCI systems are made more resilient to emotional perturbations.
Project uses GANs to recognize facial expressions and emotions from-the-wild with dual model approach.
problem Facial expression and emotion recognition in real-world scenarios.
method Created a dual GAN model architecture for Action Units and Valence Arousal annotations.
result Dual GAN model achieved better results than single model for emotion recognition.
Study develops algorithm to detect subtle respiratory events from PSG recordings.
problem Detecting non-apneic and non-hypopneic arousals in polysomnography recordings.
method Bidirectional LSTM classifier with 465 multi-domain features.
result 0.50 AUPRC on hidden test dataset, tied for second-best score in 2018 PhysioNet challenge.
Aff-Wild database expands facial expression recognition to real-world conditions.
problem Lack of spontaneous facial expression databases in real-world conditions.
method Collects spontaneous facial expressions from YouTube, annotates with valence and arousal, uses deep learning techniques.
result Developed an end-to-end DNN model achieving 0.555 CCC for valence and 0.499 CCC for arousal.
Deep learning detects sleep events like arousals and leg movements.
problem Detecting arousals and leg movements in polysomnogram for sleep disorders.
method Deep learning model trained on 1,485 subjects, tested on 1,000 recordings.
result Optimal detection achieved with dynamic event window for arousals and static window for leg movements.
Sleep disorders are implicated in a growing number of health problems. In this paper, we present a signal-processing/machine learning approach to detecting arousals in the multi-channel polysomnographic recordings of the Physionet/CinC Challenge2018 dataset. Methods: Our network architecture consists of two components.…
SleepNet detects sleep disorders using neural networks trained on PSG data.
problem Automated detection of sleep disorders like apnea and hypopnea.
method Dense Convolutional Neural Network (DRCNN) trained on Polysomnography (PSG) data.
result SleepNet achieved the first place in a sleep arousal detection challenge.
StarGAN model generates and recognizes emotions from facial expressions.
problem Emotion recognition and generation from facial expressions.
method Used StarGAN model to train on a new emotion dataset of 4K videos.
result Trained StarGAN model can generate and recognize emotions based on valence arousal scores.
Deep transfer learning boosts single-EEG arousal detection accuracy.
problem Challenges in machine learning due to channel mismatch across different EEG datasets.
method Transfer learning strategy to adapt a multivariate model to single-channel EEG data.
result Fine-tuned model achieves similar performance to baseline model and significantly outperforms a single-channel model.
Project creates new dataset for 'in the wild' emotions recognition.
problem Recognition of emotions in real-life unpredictable situations.
method Design and implement a new dataset and deep learning model.
result Demonstrates effectiveness of the new deep learning model for real-life emotions recognition.
Music FaderNets learns high-level musical qualities from low-level attributes.
problem Learning high-level musical qualities from limited data and subjective labels.
method Model low-level attributes through feature disentanglement and latent regularization; infer high-level features from low-level representations using GM-VAEs.
result Model successfully learns intrinsic relationships between high-level features and low-level attributes with minimal labeled data.
We consider the task of multimodal music mood prediction based on the audio signal and the lyrics of a track. We reproduce the implementation of traditional feature engineering based approaches and propose a new model based on deep learning. We compare the performance of both approaches on a database containing 18,000 …
We prove that a graph G is asymptotically isomorphic to the ray if and only if G is uniformly spherically bounded and is of bounded local degrees. This problem arouse in combinatorics and was posed in [3] (Problem 10.1).
New model recognizes emotions with missing modalities, improving accuracy.
problem Handling missing modalities in emotion recognition.
method Transformer-based architecture with cross-attention and self-attention mechanisms.
result Improvement of 37% in predicting arousal values and 30% in valence values compared to baseline.
Project extends emotion recognition database and trains neural networks for categorical and dimensional emotions.
problem Understanding and recognizing emotions for human-computer interaction, safety, and medical research.
method Training CNN + RNN models with emotion category and valence/arousal representations, comparing three model types.
result Categorical emotion recognition performance improves with combined model training.
Emotion recognition system using EEG signals achieves high accuracy.
problem Developing an accurate emotion recognition system using EEG signals.
method EEG signals were decomposed into frequency bands using DWT, features were extracted, PCA was applied, and SVM, KNN, and ANN were used for classification.
result Cross-validated SVM with RBF kernel achieved 91.3% accuracy for arousal and 91.1% for valence in the beta frequency band.
This short paper describes our solution to the 2018 IEEE World Congress on Computational Intelligence One-Minute Gradual-Emotional Behavior Challenge, whose goal was to estimate continuous arousal and valence values from short videos. We designed four base regression models using visual and audio features, and then use…
Over the past few years many research efforts have been devoted to the field of affect analysis. Various approaches have been proposed for: i) discrete emotion recognition in terms of the primary facial expressions; ii) emotion analysis in terms of facial Action Units (AUs), assuming a fixed expression intensity; iii) …
This paper describes audEERING's submissions as well as additional evaluations for the One-Minute-Gradual (OMG) emotion recognition challenge. We provide the results for audio and video processing on subject (in)dependent evaluations. On the provided Development set, we achieved 0.343 Concordance Correlation Coefficien…
A fusion approach combines audio and video features for emotion recognition.
problem Continuous emotion recognition using both visual and auditory modalities.
method Pre-trained CNN features from video frames and minimalistic auditory descriptors. Fusion at feature or prediction level. SVR for prediction.
result Improves CCCs of 0.749 and 0.565 for arousal and valence respectively.
This research improves emotion detection from speech, enhancing CCC by 30%.
problem Improving emotion detection from speech for categorical emotions.
method Used LSTM and TC-LSTM networks, trained with multiple datasets and robust features.
result Improved CCC for valence by 30% compared to baseline.
First ABAW 2020 Competition analyzes affective behavior tasks.
problem Automatic analysis of valence-arousal, basic expressions, and action units in real-world scenarios.
method Provided Aff-Wild2 database, described Challenges, evaluation metrics, and top-performing systems.
result Demonstrated the feasibility of automatic affective behavior analysis in real-world settings.
Deep learning framework detects emotions from EEG data.
problem Detecting emotions from EEG signals.
method Temporal and spatial convolutional layers learn discriminative representations.
result TSception achieves 86.03% classification accuracy, significantly outperforming other methods.
Model improves emotion recognition using multiple physiological signals.
problem Single physiological signal is insufficient for accurate emotion recognition.
method Fused multiple modal physiological signals (EEG, EMG, EOG) for emotion classification.
result Best classification accuracy of 94.42% on arousal and 94.02% on valence in two-class tasks.
1-D CNNs classify pupil size variations in scotopic conditions.
problem Handling wide inter-subjects variability in pupil analysis.
method 1-D Convolutional Neural Networks applied directly to raw pupil size data.
result 1-D CNNs provide high accuracy in classifying short-range pupil size sequences.
A deep neural network detects sleep events in polysomnograms with high accuracy.
problem Manual scoring of sleep events in clinical analysis is inconsistent and time-consuming.
method A single deep neural network architecture trained on 1653 recordings for joint detection of arousals, leg movements, and sleep disordered breathing.
result Joint detection of sleep events yields higher accuracy compared to separate models, and correlates well with manual annotations.
We found a way to code meanders and show they are idempotent.
problem Understanding and coding meandric permutations.
method We established a bijection between meanders and Gauss diagrams, and used this to construct matrices that are idempotent.
result Meandric permutations are idempotent over the field GF(2).
Background: Electroencephalography (EEG) monitors brain activity during sleep and is used to identify sleep disorders. In sleep medicine, clinicians interpret raw EEG signals in so-called sleep stages, which are assigned by experts to every 30s window of signal. For diagnosis, they also rely on shorter prototypical mic…
Nonconvex optimization problems arise in different research fields and arouse lots of attention in signal processing, statistics and machine learning. In this work, we explore the accelerated proximal gradient method and some of its variants which have been shown to converge under nonconvex context recently. We show th…
Automatic understanding of human affect using visual signals is of great importance in everyday human-machine interactions. Appraising human emotional states, behaviors and reactions displayed in real-world settings, can be accomplished using latent continuous dimensions (e.g., the circumplex model of affect). Valence …
Recently, a unified model for image-to-image translation tasks within adversarial learning framework has aroused widespread research interests in computer vision practitioners. Their reported empirical success however lacks solid theoretical interpretations for its inherent mechanism. In this paper, we reformulate thei…
This paper presents our approach to the One-Minute Gradual-Emotion Recognition (OMG-Emotion) Challenge, focusing on dimensional emotion recognition through visual analysis of the provided emotion videos. The approach is based on a Convolutional and Recurrent (CNN-RNN) deep neural architecture we have developed for the …
Deep-AER system uses lightweight CNN models for EEG-based emotion recognition.
problem Challenging task of emotion recognition using EEG signals.
method Two-level ensemble of lightweight 1D-CNN models trained on a small dataset.
result Deep-AER achieved high accuracies (98.43% and 97.65%) for emotion detection.
Proposes a new method to improve regression models with reweighted samples.
problem Improves regression models' performance under low sample sizes and covariate perturbations.
method Reparametrizes sample weights using a doubly non-negative matrix and solves the reweighted estimate efficiently.
result Adversarial reweighting strategy delivers promising results on various datasets.
Generative model evaluates text emotion intensity, outperforming classification.
problem Limitations of discrete emotion classification in applied domains.
method Fine-tuning generative language models to output continuous emotion intensity scores.
result Generative model outperforms classification baselines and reveals generalization capabilities.
Deep learning has aroused extensive attention due to its great empirical success. The efficiency of the block coordinate descent (BCD) methods has been recently demonstrated in deep neural network (DNN) training. However, theoretical studies on their convergence properties are limited due to the highly nonconvex nature…
Study Gram determinants in knot theory, focusing on a Möbius band determinant.
problem Closed formula for the Gram determinant of type (Mb)1. method Survey of Gram determinants, focusing on a Möbius band determinant.
result Speculation on closed formula for (Mb)1 Gram determinant. This work introduces a fair learning method for diverse sensitive attributes.
problem Fairness in supervised learning with complex sensitive attributes.
method Neural network with a simple random sampler for fairness penalties.
result The method improves fairness and utility on benchmark data.
Graph few-shot learning improves node classification with prior knowledge transfer.
problem Challenging semi-supervised node classification with few labeled nodes.
method Incorporates prior knowledge from auxiliary graphs into a transferable metric space.
result Improves classification accuracy on target graphs.
The paper studies quaternionic Monge-Ampère equations in weighted energy classes.
problem Characterizing the finite energy range of quaternionic Monge-Ampère operator.
method Proving well-definedness and fine property of the operator in weighted energy classes.
result Explicit characterization of the finite energy range of quaternionic Monge-Ampère operator.
Acquisition of labeled training samples for affective computing is usually costly and time-consuming, as affects are intrinsically subjective, subtle and uncertain, and hence multiple human assessors are needed to evaluate each affective sample. Particularly, for affect estimation in the 3D space of valence, arousal an…
Data stream clustering tackles real-time data processing challenges.
problem Real-time processing of data streams with less prior information.
method Review of data stream clustering algorithms and their characteristics.
result Comparison and analysis of data stream clustering algorithms.
Study uses simulation-based inference to decode brain activity from synthetic stimuli.
problem Reversing the process of brain activity emulation to recover stimuli or their properties.
method Pairing brain emulator with LLMs to learn a probabilistic mapping from brain maps to stimulus parameters.
result LLMs can serve as controllable stimulus generators and parameters can be recovered from brain maps.
Introduces new metrics to measure performance of deep Bayesian neural networks.
problem Lack of specific criteria to measure performance of deep Bayesian neural networks.
method Proposes several metrics including model calibration, data rejection ability, and uncertainty divergence.
result Introduces more specific criteria for measuring deep Bayesian neural network performance.
Generative model combines multi-dimensional annotations for more accurate ground truth estimation.
problem Inaccurate ground truth estimation from naive annotators' multi-dimensional annotations.
method Proposes a joint multi-dimensional model for global and time-series annotation fusion using Expectation-Maximization algorithm.
result More accurate ground truth estimates through joint modeling of multiple dimensions.
Improved emotion prediction using autoencoder codebooks.
problem Continuous emotion prediction from audio data.
method Bag-of-Words model based on autoencoder codebook.
result Improved CCC scores for emotion dimensions.
Neural responses are highly variable, and some portion of this variability arises from fluctuations in modulatory factors that alter their gain, such as adaptation, attention, arousal, expected or actual reward, emotion, and local metabolic resource availability. Regardless of their origin, fluctuations in these signal…