16 research outputs found
A privacy-preserving data storage and service framework based on deep learning and blockchain for construction workers' wearable IoT sensors
Classifying brain signals collected by wearable Internet of Things (IoT)
sensors, especially brain-computer interfaces (BCIs), is one of the
fastest-growing areas of research. However, research has mostly ignored the
secure storage and privacy protection issues of collected personal
neurophysiological data. Therefore, in this article, we try to bridge this gap
and propose a secure privacy-preserving protocol for implementing BCI
applications. We first transformed brain signals into images and used
generative adversarial network to generate synthetic signals to protect data
privacy. Subsequently, we applied the paradigm of transfer learning for signal
classification. The proposed method was evaluated by a case study and results
indicate that real electroencephalogram data augmented with artificially
generated samples provide superior classification performance. In addition, we
proposed a blockchain-based scheme and developed a prototype on Ethereum, which
aims to make storing, querying and sharing personal neurophysiological data and
analysis reports secure and privacy-aware. The rights of three main transaction
bodies - construction workers, BCI service providers and project managers - are
described and the advantages of the proposed system are discussed. We believe
this paper provides a well-rounded solution to safeguard private data against
cyber-attacks, level the playing field for BCI application developers, and to
the end improve professional well-being in the industry
Decoding Neural Activity to Assess Individual Latent State in Ecologically Valid Contexts
There exist very few ways to isolate cognitive processes, historically
defined via highly controlled laboratory studies, in more ecologically valid
contexts. Specifically, it remains unclear as to what extent patterns of neural
activity observed under such constraints actually manifest outside the
laboratory in a manner that can be used to make an accurate inference about the
latent state, associated cognitive process, or proximal behavior of the
individual. Improving our understanding of when and how specific patterns of
neural activity manifest in ecologically valid scenarios would provide
validation for laboratory-based approaches that study similar neural phenomena
in isolation and meaningful insight into the latent states that occur during
complex tasks. We argue that domain generalization methods from the
brain-computer interface community have the potential to address this
challenge. We previously used such an approach to decode phasic neural
responses associated with visual target discrimination. Here, we extend that
work to more tonic phenomena such as internal latent states. We use data from
two highly controlled laboratory paradigms to train two separate
domain-generalized models. We apply the trained models to an ecologically valid
paradigm in which participants performed multiple, concurrent driving-related
tasks. Using the pretrained models, we derive estimates of the underlying
latent state and associated patterns of neural activity. Importantly, as the
patterns of neural activity change along the axis defined by the original
training data, we find changes in behavior and task performance consistent with
the observations from the original, laboratory paradigms. We argue that these
results lend ecological validity to those experimental designs and provide a
methodology for understanding the relationship between observed neural activity
and behavior during complex tasks
Signal processing approaches to minimize or suppress calibration time in oscillatory activity-based Brain-Computer Interfaces
International audienceOne of the major limitations of Brain-Computer Interfaces (BCI) is their long calibration time, which limits their use in practice, both by patients and healthy users alike. Such long calibration times are due to the large between-user variability and thus to the need to collect numerous training electroencephalography (EEG) trials for the machine learning algorithms used in BCI design. In this paper, we first survey existing approaches to reduce or suppress calibration time, these approaches being notably based on regularization, user-to-user transfer, semi-supervised learning and a-priori physiological information. We then propose new tools to reduce BCI calibration time. In particular, we propose to generate artificial EEG trials from the few EEG trials initially available, in order to augment the training set size. These artificial EEG trials are obtained by relevant combinations and distortions of the original trials available. We propose 3 different methods to do so. We also propose a new, fast and simple approach to perform user-to-user transfer for BCI. Finally, we study and compare offline different approaches, both old and new ones, on the data of 50 users from 3 different BCI data sets. This enables us to identify guidelines about how to reduce or suppress calibration time for BCI
EEG-based neuroergonomics for 3D user interfaces: opportunities and challenges
International audience3D user interfaces (3DUI) are increasingly used in a number of applications, spanning from entertainment to industrial design. However, 3D interaction tasks are generally more complex for users since interacting with a 3D environment is more cognitively demanding than perceiving and interacting with a 2D one. As such, it is essential that we could evaluate finely user experience, in order to propose seamless interfaces. To do so, a promising research direction is to measure users' inner-state based on brain signals acquired during interaction, by following a neuroergonomics approach. Combined with existing methods, such tool can be used to strengthen the understanding of user experience. In this paper, we review the work being undergone in this area; what has been achieved and the new challenges that arise. We describe how a mobile brain imaging technique such as electroencephalography (EEG) brings continuous and non-disruptive measures. EEG-based evaluation of users can give insights about multiple dimensions of the user experience, with realistic interaction tasks or novel interfaces. We investigate four constructs: workload, attention, error recognition and visual comfort. Ultimately, these metrics could help to alleviate users when they interact with computers
Electroencephalogram-Based Single-Trial Detection of Language Expectation Violations in Listening to Speech
We propose an approach for the detection of language expectation violations that occur in communication. We examined semantic and syntactic violations from electroencephalogram (EEG) when participants listened to spoken sentences. Previous studies have shown that such event-related potential (ERP) components as N400 and the late positivity (P600) are evoked in the auditory where semantic and syntactic anomalies occur. We used this knowledge to detect language expectation violation from single-trial EEGs by machine learning techniques. We recorded the brain activity of 18 participants while they listened to sentences that contained semantic and syntactic anomalies and identified the significant main effects of these anomalies in the ERP components. We also found that a multilayer perceptron achieved 59.5% (semantic) and 57.7% (syntactic) accuracies