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Article ; Online: Estimating distribution shifts for predicting cross-subject generalization in electroencephalography-based mental workload assessment.

Albuquerque, Isabela / Monteiro, João / Rosanne, Olivier / Falk, Tiago H

Frontiers in artificial intelligence

2022 Volume 5, Page(s) 992732

Abstract: Assessment of mental workload in real-world conditions is key to ensuring the performance of workers executing tasks that demand sustained attention. Previous literature has employed electroencephalography (EEG) to this end despite having observed that ... ...

Abstract	Assessment of mental workload in real-world conditions is key to ensuring the performance of workers executing tasks that demand sustained attention. Previous literature has employed electroencephalography (EEG) to this end despite having observed that EEG correlates of mental workload vary across subjects and physical strain, thus making it difficult to devise models capable of simultaneously presenting reliable performance across users. Domain adaptation consists of a set of strategies that aim at allowing for improving machine learning systems performance on unseen data at training time. Such methods, however, might rely on assumptions over the considered data distributions, which typically do not hold for applications of EEG data. Motivated by this observation, in this work we propose a strategy to estimate two types of discrepancies between multiple data distributions, namely marginal and conditional shifts, observed on data collected from different subjects. Besides shedding light on the assumptions that hold for a particular dataset, the estimates of statistical shifts obtained with the proposed approach can be used for investigating other aspects of a machine learning pipeline, such as quantitatively assessing the effectiveness of domain adaptation strategies. In particular, we consider EEG data collected from individuals performing mental tasks while running on a treadmill and pedaling on a stationary bike and explore the effects of different normalization strategies commonly used to mitigate cross-subject variability. We show the effects that different normalization schemes have on statistical shifts and their relationship with the accuracy of mental workload prediction as assessed on unseen participants at training time.
Language	English
Publishing date	2022-10-04
Publishing country	Switzerland
Document type	Journal Article
ISSN	2624-8212
ISSN (online)	2624-8212
DOI	10.3389/frai.2022.992732
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Book ; Online: Discovering Bugs in Vision Models using Off-the-shelf Image Generation and Captioning

Wiles, Olivia / Albuquerque, Isabela / Gowal, Sven

2022

Abstract: Automatically discovering failures in vision models under real-world settings remains an open challenge. This work demonstrates how off-the-shelf, large-scale, image-to-text and text-to-image models, trained on vast amounts of data, can be leveraged to ... ...

Abstract	Automatically discovering failures in vision models under real-world settings remains an open challenge. This work demonstrates how off-the-shelf, large-scale, image-to-text and text-to-image models, trained on vast amounts of data, can be leveraged to automatically find such failures. In essence, a conditional text-to-image generative model is used to generate large amounts of synthetic, yet realistic, inputs given a ground-truth label. Misclassified inputs are clustered and a captioning model is used to describe each cluster. Each cluster's description is used in turn to generate more inputs and assess whether specific clusters induce more failures than expected. We use this pipeline to demonstrate that we can effectively interrogate classifiers trained on ImageNet to find specific failure cases and discover spurious correlations. We also show that we can scale the approach to generate adversarial datasets targeting specific classifier architectures. This work serves as a proof-of-concept demonstrating the utility of large-scale generative models to automatically discover bugs in vision models in an open-ended manner. We also describe a number of limitations and pitfalls related to this approach.
Keywords	Computer Science - Computer Vision and Pattern Recognition ; Computer Science - Machine Learning ; Statistics - Machine Learning
Subject code	006 ; 004
Publishing date	2022-08-18
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Generative models improve fairness of medical classifiers under distribution shifts.

Ktena, Ira / Wiles, Olivia / Albuquerque, Isabela / Rebuffi, Sylvestre-Alvise / Tanno, Ryutaro / Roy, Abhijit Guha / Azizi, Shekoofeh / Belgrave, Danielle / Kohli, Pushmeet / Cemgil, Taylan / Karthikesalingam, Alan / Gowal, Sven

Nature medicine

2024 Volume 30, Issue 4, Page(s) 1166–1173

Abstract: Domain generalization is a ubiquitous challenge for machine learning in healthcare. Model performance in real-world conditions might be lower than expected because of discrepancies between the data encountered during deployment and development. ... ...

Abstract	Domain generalization is a ubiquitous challenge for machine learning in healthcare. Model performance in real-world conditions might be lower than expected because of discrepancies between the data encountered during deployment and development. Underrepresentation of some groups or conditions during model development is a common cause of this phenomenon. This challenge is often not readily addressed by targeted data acquisition and 'labeling' by expert clinicians, which can be prohibitively expensive or practically impossible because of the rarity of conditions or the available clinical expertise. We hypothesize that advances in generative artificial intelligence can help mitigate this unmet need in a steerable fashion, enriching our training dataset with synthetic examples that address shortfalls of underrepresented conditions or subgroups. We show that diffusion models can automatically learn realistic augmentations from data in a label-efficient manner. We demonstrate that learned augmentations make models more robust and statistically fair in-distribution and out of distribution. To evaluate the generality of our approach, we studied three distinct medical imaging contexts of varying difficulty: (1) histopathology, (2) chest X-ray and (3) dermatology images. Complementing real samples with synthetic ones improved the robustness of models in all three medical tasks and increased fairness by improving the accuracy of clinical diagnosis within underrepresented groups, especially out of distribution.
MeSH term(s)	Artificial Intelligence ; Machine Learning
Language	English
Publishing date	2024-04-10
Publishing country	United States
Document type	Journal Article
ZDB-ID	1220066-9
ISSN	1546-170X ; 1078-8956
ISSN (online)	1546-170X
ISSN	1078-8956
DOI	10.1038/s41591-024-02838-6
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Article: Adaptive Filtering for Improved EEG-Based Mental Workload Assessment of Ambulant Users.

Rosanne, Olivier / Albuquerque, Isabela / Cassani, Raymundo / Gagnon, Jean-François / Tremblay, Sebastien / Falk, Tiago H

Frontiers in neuroscience

2021 Volume 15, Page(s) 611962

Abstract: Recently, due to the emergence of mobile electroencephalography (EEG) devices, assessment of mental workload in highly ecological settings has gained popularity. In such settings, however, motion and other common artifacts have been shown to severely ... ...

Abstract	Recently, due to the emergence of mobile electroencephalography (EEG) devices, assessment of mental workload in highly ecological settings has gained popularity. In such settings, however, motion and other common artifacts have been shown to severely hamper signal quality and to degrade mental workload assessment performance. Here, we show that classical EEG enhancement algorithms, conventionally developed to remove ocular and muscle artifacts, are not optimal in settings where participant movement (e.g., walking or running) is expected. As such, an adaptive filter is proposed that relies on an accelerometer-based referential signal. We show that when combined with classical algorithms, accurate mental workload assessment is achieved. To test the proposed algorithm, data from 48 participants was collected as they performed the Revised Multi-Attribute Task Battery-II (MATB-II) under a low and a high workload setting, either while walking/jogging on a treadmill, or using a stationary exercise bicycle. Accuracy as high as 95% could be achieved with a random forest based mental workload classifier with ambulant users. Moreover, an increase in gamma activity was found in the parietal cortex, suggesting a connection between sensorimotor integration, attention, and workload in ambulant users.
Language	English
Publishing date	2021-04-07
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2411902-7
ISSN	1662-453X ; 1662-4548
ISSN (online)	1662-453X
ISSN	1662-4548
DOI	10.3389/fnins.2021.611962
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Book ; Online: Learning to navigate image manifolds induced by generative adversarial networks for unsupervised video generation

Albuquerque, Isabela / Monteiro, João / Falk, Tiago H.

2019

Abstract: In this work, we introduce a two-step framework for generative modeling of temporal data. Specifically, the generative adversarial networks (GANs) setting is employed to generate synthetic scenes of moving objects. To do so, we propose a two-step ... ...

Abstract	In this work, we introduce a two-step framework for generative modeling of temporal data. Specifically, the generative adversarial networks (GANs) setting is employed to generate synthetic scenes of moving objects. To do so, we propose a two-step training scheme within which: a generator of static frames is trained first. Afterwards, a recurrent model is trained with the goal of providing a sequence of inputs to the previously trained frames generator, thus yielding scenes which look natural. The adversarial setting is employed in both training steps. However, with the aim of avoiding known training instabilities in GANs, a multiple discriminator approach is used to train both models. Results in the studied video dataset indicate that, by employing such an approach, the recurrent part is able to learn how to coherently navigate the image manifold induced by the frames generator, thus yielding more natural-looking scenes.
Keywords	Computer Science - Computer Vision and Pattern Recognition ; Computer Science - Machine Learning ; Statistics - Machine Learning
Subject code	004 ; 006
Publishing date	2019-01-23
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Improving out-of-distribution generalization via multi-task self-supervised pretraining

Albuquerque, Isabela / Naik, Nikhil / Li, Junnan / Keskar, Nitish / Socher, Richard

2020

Abstract: Self-supervised feature representations have been shown to be useful for supervised classification, few-shot learning, and adversarial robustness. We show that features obtained using self-supervised learning are comparable to, or better than, supervised ...

Abstract	Self-supervised feature representations have been shown to be useful for supervised classification, few-shot learning, and adversarial robustness. We show that features obtained using self-supervised learning are comparable to, or better than, supervised learning for domain generalization in computer vision. We introduce a new self-supervised pretext task of predicting responses to Gabor filter banks and demonstrate that multi-task learning of compatible pretext tasks improves domain generalization performance as compared to training individual tasks alone. Features learnt through self-supervision obtain better generalization to unseen domains when compared to their supervised counterpart when there is a larger domain shift between training and test distributions and even show better localization ability for objects of interest. Self-supervised feature representations can also be combined with other domain generalization methods to further boost performance.
Keywords	Computer Science - Computer Vision and Pattern Recognition ; Computer Science - Machine Learning
Subject code	006
Publishing date	2020-03-30
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Deep learning-based electroencephalography analysis: a systematic review.

Roy, Yannick / Banville, Hubert / Albuquerque, Isabela / Gramfort, Alexandre / Falk, Tiago H / Faubert, Jocelyn

Journal of neural engineering

2019 Volume 16, Issue 5, Page(s) 51001

Abstract: Context: Electroencephalography (EEG) is a complex signal and can require several years of training, as well as advanced signal processing and feature extraction methodologies to be correctly interpreted. Recently, deep learning (DL) has shown great ... ...

Abstract	Context: Electroencephalography (EEG) is a complex signal and can require several years of training, as well as advanced signal processing and feature extraction methodologies to be correctly interpreted. Recently, deep learning (DL) has shown great promise in helping make sense of EEG signals due to its capacity to learn good feature representations from raw data. Whether DL truly presents advantages as compared to more traditional EEG processing approaches, however, remains an open question. Objective: In this work, we review 154 papers that apply DL to EEG, published between January 2010 and July 2018, and spanning different application domains such as epilepsy, sleep, brain-computer interfacing, and cognitive and affective monitoring. We extract trends and highlight interesting approaches from this large body of literature in order to inform future research and formulate recommendations. Methods: Major databases spanning the fields of science and engineering were queried to identify relevant studies published in scientific journals, conferences, and electronic preprint repositories. Various data items were extracted for each study pertaining to (1) the data, (2) the preprocessing methodology, (3) the DL design choices, (4) the results, and (5) the reproducibility of the experiments. These items were then analyzed one by one to uncover trends. Results: Our analysis reveals that the amount of EEG data used across studies varies from less than ten minutes to thousands of hours, while the number of samples seen during training by a network varies from a few dozens to several millions, depending on how epochs are extracted. Interestingly, we saw that more than half the studies used publicly available data and that there has also been a clear shift from intra-subject to inter-subject approaches over the last few years. About [Formula: see text] of the studies used convolutional neural networks (CNNs), while [Formula: see text] used recurrent neural networks (RNNs), most often with a total of 3-10 layers. Moreover, almost one-half of the studies trained their models on raw or preprocessed EEG time series. Finally, the median gain in accuracy of DL approaches over traditional baselines was [Formula: see text] across all relevant studies. More importantly, however, we noticed studies often suffer from poor reproducibility: a majority of papers would be hard or impossible to reproduce given the unavailability of their data and code. Significance: To help the community progress and share work more effectively, we provide a list of recommendations for future studies and emphasize the need for more reproducible research. We also make our summary table of DL and EEG papers available and invite authors of published work to contribute to it directly. A planned follow-up to this work will be an online public benchmarking portal listing reproducible results.
MeSH term(s)	Brain/physiology ; Brain-Computer Interfaces/trends ; Databases, Factual/trends ; Deep Learning/trends ; Electroencephalography/methods ; Electroencephalography/trends ; Humans
Language	English
Publishing date	2019-08-14
Publishing country	England
Document type	Journal Article ; Systematic Review
ZDB-ID	2170901-4
ISSN	1741-2552 ; 1741-2560
ISSN (online)	1741-2552
ISSN	1741-2560
DOI	10.1088/1741-2552/ab260c
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Article: PASS: A Multimodal Database of Physical Activity and Stress for Mobile Passive Body/ Brain-Computer Interface Research.

Parent, Mark / Albuquerque, Isabela / Tiwari, Abhishek / Cassani, Raymundo / Gagnon, Jean-François / Lafond, Daniel / Tremblay, Sébastien / Falk, Tiago H

Frontiers in neuroscience

2020 Volume 14, Page(s) 542934

Abstract: With the burgeoning of wearable devices and passive body/brain-computer interfaces (B/BCIs), automated stress monitoring in everyday settings has gained significant attention recently, with applications ranging from serious games to clinical monitoring. ... ...

Abstract	With the burgeoning of wearable devices and passive body/brain-computer interfaces (B/BCIs), automated stress monitoring in everyday settings has gained significant attention recently, with applications ranging from serious games to clinical monitoring. With mobile users, however, challenges arise due to other overlapping (and potentially confounding) physiological responses (e.g., due to physical activity) that may mask the effects of stress, as well as movement artifacts that can be introduced in the measured signals. For example, the classical increase in heart rate can no longer be attributed solely to stress and could be caused by the activity itself. This makes the development of mobile passive B/BCIs challenging. In this paper, we introduce PASS, a multimodal database of Physical Activity and StresS collected from 48 participants. Participants performed tasks of varying stress levels at three different activity levels and provided quantitative ratings of their perceived stress and fatigue levels. To manipulate stress, two video games (i.e., a calm exploration game and a survival game) were used. Peripheral physical activity (electrocardiography, electrodermal activity, breathing, skin temperature) as well as cerebral activity (electroencephalography) were measured throughout the experiment. A complete description of the experimental protocol is provided and preliminary analyses are performed to investigate the physiological reactions to stress in the presence of physical activity. The PASS database, including raw data and subjective ratings has been made available to the research community at http://musaelab.ca/pass-database/. It is hoped that this database will help advance mobile passive B/BCIs for use in everyday settings.
Language	English
Publishing date	2020-12-08
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2411902-7
ISSN	1662-453X ; 1662-4548
ISSN (online)	1662-453X
ISSN	1662-4548
DOI	10.3389/fnins.2020.542934
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Article: WAUC: A Multi-Modal Database for Mental Workload Assessment Under Physical Activity.

Albuquerque, Isabela / Tiwari, Abhishek / Parent, Mark / Cassani, Raymundo / Gagnon, Jean-François / Lafond, Daniel / Tremblay, Sébastien / Falk, Tiago H

Frontiers in neuroscience

2020 Volume 14, Page(s) 549524

Abstract: Assessment of mental workload is crucial for applications that require sustained attention and where conditions such as mental fatigue and drowsiness must be avoided. Previous work that attempted to devise objective methods to model mental workload were ... ...

Abstract	Assessment of mental workload is crucial for applications that require sustained attention and where conditions such as mental fatigue and drowsiness must be avoided. Previous work that attempted to devise objective methods to model mental workload were mainly based on neurological or physiological data collected when the participants performed tasks that did not involve physical activity. While such models may be useful for scenarios that involve static operators, they may not apply in real-world situations where operators are performing tasks under varying levels of physical activity, such as those faced by first responders, firefighters, and police officers. Here, we describe WAUC, a multimodal database of mental Workload Assessment Under physical aCtivity. The study involved 48 participants who performed the NASA Revised Multi-Attribute Task Battery II under three different activity level conditions. Physical activity was manipulated by changing the speed of a stationary bike or a treadmill. During data collection, six neural and physiological modalities were recorded, namely: electroencephalography, electrocardiography, breathing rate, skin temperature, galvanic skin response, and blood volume pulse, in addition to 3-axis accelerometry. Moreover, participants were asked to answer the NASA Task Load Index questionnaire after each experimental section, as well as rate their physical fatigue level on the Borg fatigue scale. In order to bring our experimental setup closer to real-world situations, all signals were monitored using wearable, off-the-shelf devices. In this paper, we describe the adopted experimental protocol, as well as validate the subjective, neural, and physiological data collected. The WAUC database, including the raw data and features, subjective ratings, and scripts to reproduce the experiments reported herein will be made available at: http://musaelab.ca/resources/.
Language	English
Publishing date	2020-12-01
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2411902-7
ISSN	1662-453X ; 1662-4548
ISSN (online)	1662-453X
ISSN	1662-4548
DOI	10.3389/fnins.2020.549524
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Book ; Online: An end-to-end approach for the verification problem

Monteiro, Joao / Albuquerque, Isabela / Alam, Jahangir / Hjelm, R Devon / Falk, Tiago

learning the right distance

2020

Abstract: In this contribution, we augment the metric learning setting by introducing a parametric pseudo-distance, trained jointly with the encoder. Several interpretations are thus drawn for the learned distance-like model's output. We first show it approximates ...

Abstract	In this contribution, we augment the metric learning setting by introducing a parametric pseudo-distance, trained jointly with the encoder. Several interpretations are thus drawn for the learned distance-like model's output. We first show it approximates a likelihood ratio which can be used for hypothesis tests, and that it further induces a large divergence across the joint distributions of pairs of examples from the same and from different classes. Evaluation is performed under the verification setting consisting of determining whether sets of examples belong to the same class, even if such classes are novel and were never presented to the model during training. Empirical evaluation shows such method defines an end-to-end approach for the verification problem, able to attain better performance than simple scorers such as those based on cosine similarity and further outperforming widely used downstream classifiers. We further observe training is much simplified under the proposed approach compared to metric learning with actual distances, requiring no complex scheme to harvest pairs of examples. Comment: ICML 2020 final camera ready. Code is available at: https://github.com/joaomonteirof/e2e_verification
Keywords	Computer Science - Machine Learning ; Statistics - Machine Learning
Subject code	006
Publishing date	2020-02-21
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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