Adversarial Attacks and Defenses in Physiological Computing: A Systematic Review

Physiological computing uses human physiological data as system inputs in real time. It includes, or significantly overlaps with, brain-computer interfaces, affective computing, adaptive automation, health informatics, and physiological signal based biometrics. Physiological computing increases the communication bandwidth from the user to the computer, but is also subject to various types of adversarial attacks, in which the attacker deliberately manipulates the training and/or test examples to hijack the machine learning algorithm output, leading to possibly user confusion, frustration, injury, or even death. However, the vulnerability of physiological computing systems has not been paid enough attention to, and there does not exist a comprehensive review on adversarial attacks to it. This paper fills this gap, by providing a systematic review on the main research areas of physiological computing, different types of adversarial attacks and their applications to physiological computing, and the corresponding defense strategies. We hope this review will attract more research interests on the vulnerability of physiological computing systems, and more importantly, defense strategies to make them more secure.

FCM-RDpA: TSK Fuzzy Regression Model Construction Using Fuzzy C-Means Clustering, Regularization, DropRule, and Powerball AdaBelief

To effectively optimize Takagi-Sugeno-Kang (TSK) fuzzy systems for regression problems, a mini-batch gradient descent with regularization, DropRule, and AdaBound (MBGD-RDA) algorithm was recently proposed. This paper further proposes FCM-RDpA, which improves MBGD-RDA by replacing the grid partition approach in rule initialization by fuzzy c-means clustering, and AdaBound by Powerball AdaBelief, which integrates recently proposed Powerball gradient and AdaBelief to further expedite and stabilize parameter optimization. Extensive experiments on 22 regression datasets with various sizes and dimensionalities validated the superiority of FCM-RDpA over MBGD-RDA, especially when the feature dimensionality is higher. We also propose an additional approach, FCM-RDpAx, that further improves FCM-RDpA by using augmented features in both the antecedents and consequents of the rules.

EEG-Based Brain-Computer Interfaces Are Vulnerable to Backdoor Attacks

Research and development of electroencephalogram (EEG) based brain-computer interfaces (BCIs) have advanced rapidly, partly due to the wide adoption of sophisticated machine learning approaches for decoding the EEG signals. However, recent studies have shown that machine learning algorithms are vulnerable to adversarial attacks, e.g., the attacker can add tiny adversarial perturbations to a test sample to fool the model, or poison the training data to insert a secret backdoor. Previous research has shown that adversarial attacks are also possible for EEG-based BCIs. However, only adversarial perturbations have been considered, and the approaches are theoretically sound but very difficult to implement in practice. This article proposes to use narrow period pulse for poisoning attack of EEG-based BCIs, which is more feasible in practice and has never been considered before. One can create dangerous backdoors in the machine learning model by injecting poisoning samples into the training set. Test samples with the backdoor key will then be classified into the target class specified by the attacker. What most distinguishes our approach from previous ones is that the backdoor key does not need to be synchronized with the EEG trials, making it very easy to implement. The effectiveness and robustness of the backdoor attack approach is demonstrated, highlighting a critical security concern for EEG-based BCIs.

Overcoming Negative Transfer: A Survey

Transfer learning aims to help the target task with little or no training data by leveraging knowledge from one or multi-related auxiliary tasks. In practice, the success of transfer learning is not always guaranteed, negative transfer is a long-standing problem in transfer learning literature, which has been well recognized within the transfer learning community. How to overcome negative transfer has been studied for a long time and has raised increasing attention in recent years. Thus, it is both necessary and challenging to comprehensively review the relevant researches. This survey attempts to analyze the factors related to negative transfer and summarizes the theories and advances of overcoming negative transfer from four crucial aspects: source data quality, target data quality, domain divergence and generic algorithms, which may provide the readers an insight into the current research status and ideas. Additionally, we provided some general guidelines on how to detect and overcome negative transfer on real data, including the negative transfer detection, datasets, baselines, and general routines. The survey provides researchers a framework for better understanding and identifying the research status, fundamental questions, open challenges and future directions of the field.

Transfer Learning for Brain-Computer Interfaces: A Complete Pipeline

Transfer learning (TL) has been widely used in electroencephalogram (EEG) based brain-computer interfaces (BCIs) to reduce the calibration effort for a new subject, and demonstrated promising performance. After EEG signal acquisition, a closed-loop EEG-based BCI system also includes signal processing, feature engineering, and classification/regression blocks before sending out the control signal, whereas previous approaches only considered TL in one or two such components. This paper proposes that TL could be considered in all three components (signal processing, feature engineering, and classification/regression). Furthermore, it is also very important to specifically add a data alignment component before signal processing to make the data from different subjects more consistent, and hence to facilitate subsequential TL. Offline calibration experiments on two MI datasets verified our proposal. Especially, integrating data alignment and sophisticated TL approaches can significantly improve the classification performance, and hence greatly reduce the calibration effort.

Transfer Learning for EEG-Based Brain-Computer Interfaces: A Review of Progress Made Since 2016

A brain-computer interface (BCI) enables a user to communicate with a computer directly using brain signals. Electroencephalograms (EEGs) used in BCIs are weak, easily contaminated by interference and noise, non-stationary for the same subject, and varying across different subjects and sessions. Therefore, it is difficult to build a generic pattern recognition model in an EEG-based BCI system that is optimal for different subjects, during different sessions, for different devices and tasks. Usually, a calibration session is needed to collect some training data for a new subject, which is time consuming and user unfriendly. Transfer learning (TL), which utilizes data or knowledge from similar or relevant subjects/sessions/devices/tasks to facilitate learning for a new subject/session/device/task, is frequently used to reduce the amount of calibration effort. This paper reviews journal publications on TL approaches in EEG-based BCIs in the last few years, i.e., since 2016. Six paradigms and applications -- motor imagery, event-related potentials, steady-state visual evoked potentials, affective BCIs, regression problems, and adversarial attacks -- are considered. For each paradigm/application, we group the TL approaches into cross-subject/session, cross-device, and cross-task settings and review them separately. Observations and conclusions are made at the end of the paper, which may point to future research directions.

Rethink the Connections among Generalization, Memorization and the Spectral Bias of DNNs

Over-parameterized deep neural networks (DNNs) with sufficient capacity to memorize random noise can achieve excellent generalization performance on normal datasets, challenging the bias-variance trade-off in classical learning theory. Recent studies claimed that DNNs first learn simple patterns and then memorize noise; some other works showed that DNNs have a spectral bias to learn target functions from low to high frequencies during training. These suggest some connections among generalization, memorization and the spectral bias of DNNs: the low-frequency components in the input space represent the \emph{patterns} which can generalize, whereas the high-frequency components represent the \emph{noise} which needs to be memorized. However, we show that it is not true: under the experimental setup of deep double descent, the high-frequency components of DNNs begin to diminish in the second descent, whereas the examples with random labels are still being memorized. Moreover, we find that the spectrum of DNNs can be applied to monitoring the test behavior, e.g., it can indicate when the second descent of the test error starts, even though the spectrum is calculated from the training set only.

Transfer Learning for EEG-Based Brain-Computer Interfaces: A Review of Progresses Since 2016

A brain-computer interface (BCI) enables a user to communicate directly with a computer using the brain signals. Electroencephalogram (EEG) is the most frequently used input signal in BCIs. However, EEG signals are weak, easily contaminated by interferences and noise, non-stationary for the same subject, and varying among different subjects. So, it is difficult to build a generic pattern recognition model in an EEG-based BCI system that is optimal for different subjects, in different sessions, for different devices and tasks. Usually a calibration session is needed to collect some subject-specific data for a new subject, which is time-consuming and user-unfriendly. Transfer learning (TL), which can utilize data or knowledge from similar or relevant subjects/sessions/devices/tasks to facilitate the learning for a new subject/session/device/task, is frequently used to alleviate this calibration requirement. This paper reviews journal publications on TL approaches in EEG-based BCIs in the last few years, i.e., since 2016. Six paradigms and applications -- motor imagery (MI), event related potentials (ERP), steady-state visual evoked potentials (SSVEP), affective BCIs (aBCI), regression problems, and adversarial attacks -- are considered. For each paradigm/application, we group the TL approaches into cross-subject/session, cross-device, and cross-task settings and review them separately. Observations and conclusions are made at the end of the paper, which may point to future research directions.