99 research outputs found
Online Mental Fatigue Monitoring via Indirect Brain Dynamics Evaluation
Driver mental fatigue leads to thousands of traffic accidents. The increasing quality and availability of low-cost electroencephalogram (EEG) systems offer possibilities for practical fatigue monitoring. However, non-data-driven methods, designed for practical, complex situations, usually rely on handcrafted data statistics of EEG signals. To reduce human involvement, we introduce a data-driven methodology for online mental fatigue detection: self-weight ordinal regression (SWORE). Reaction time (RT), referring to the length of time people take to react to an emergency, is widely considered an objective behavioral measure for mental fatigue state. Since regression methods are sensitive to extreme RTs, we propose an indirect RT estimation based on preferences to explore the relationship between EEG and RT, which generalizes to any scenario when an objective fatigue indicator is available. In particular, SWORE evaluates the noisy EEG signals from multiple channels in terms of two states: shaking state and steady state. Modeling the shaking state can discriminate the reliable channels from the uninformative ones, while modeling the steady state can suppress the task-nonrelevant fluctuation within each channel. In addition, an online generalized Bayesian moment matching (online GBMM) algorithm is proposed to online-calibrate SWORE efficiently per participant. Experimental results with 40 participants show that SWORE can maximally achieve consistent with RT, demonstrating the feasibility and adaptability of our proposed framework in practical mental fatigue estimation
Sensors and Systems for Monitoring Mental Fatigue: A systematic review
Mental fatigue is a leading cause of motor vehicle accidents, medical errors,
loss of workplace productivity, and student disengagements in e-learning
environment. Development of sensors and systems that can reliably track mental
fatigue can prevent accidents, reduce errors, and help increase workplace
productivity. This review provides a critical summary of theoretical models of
mental fatigue, a description of key enabling sensor technologies, and a
systematic review of recent studies using biosensor-based systems for tracking
mental fatigue in humans. We conducted a systematic search and review of recent
literature which focused on detection and tracking of mental fatigue in humans.
The search yielded 57 studies (N=1082), majority of which used
electroencephalography (EEG) based sensors for tracking mental fatigue. We
found that EEG-based sensors can provide a moderate to good sensitivity for
fatigue detection. Notably, we found no incremental benefit of using
high-density EEG sensors for application in mental fatigue detection. Given the
findings, we provide a critical discussion on the integration of wearable EEG
and ambient sensors in the context of achieving real-world monitoring. Future
work required to advance and adapt the technologies toward widespread
deployment of wearable sensors and systems for fatigue monitoring in
semi-autonomous and autonomous industries is examined.Comment: 19 Pages, 3 Figure
Deep Riemannian Networks for EEG Decoding
State-of-the-art performance in electroencephalography (EEG) decoding tasks
is currently often achieved with either Deep-Learning or
Riemannian-Geometry-based decoders. Recently, there is growing interest in Deep
Riemannian Networks (DRNs) possibly combining the advantages of both previous
classes of methods. However, there are still a range of topics where additional
insight is needed to pave the way for a more widespread application of DRNs in
EEG. These include architecture design questions such as network size and
end-to-end ability as well as model training questions. How these factors
affect model performance has not been explored. Additionally, it is not clear
how the data within these networks is transformed, and whether this would
correlate with traditional EEG decoding. Our study aims to lay the groundwork
in the area of these topics through the analysis of DRNs for EEG with a wide
range of hyperparameters. Networks were tested on two public EEG datasets and
compared with state-of-the-art ConvNets. Here we propose end-to-end EEG SPDNet
(EE(G)-SPDNet), and we show that this wide, end-to-end DRN can outperform the
ConvNets, and in doing so use physiologically plausible frequency regions. We
also show that the end-to-end approach learns more complex filters than
traditional band-pass filters targeting the classical alpha, beta, and gamma
frequency bands of the EEG, and that performance can benefit from channel
specific filtering approaches. Additionally, architectural analysis revealed
areas for further improvement due to the possible loss of Riemannian specific
information throughout the network. Our study thus shows how to design and
train DRNs to infer task-related information from the raw EEG without the need
of handcrafted filterbanks and highlights the potential of end-to-end DRNs such
as EE(G)-SPDNet for high-performance EEG decoding.Comment: 26 pages, 15 Figure
Multimodal approach for pilot mental state detection based on EEG
The safety of flight operations depends on the cognitive abilities of pilots. In recent years, there has been growing concern about potential accidents caused by the declining mental states of pilots. We have developed a novel multimodal approach for mental state detection in pilots using electroencephalography (EEG) signals. Our approach includes an advanced automated preprocessing pipeline to remove artefacts from the EEG data, a feature extraction method based on Riemannian geometry analysis of the cleaned EEG data, and a hybrid ensemble learning technique that combines the results of several machine learning classifiers. The proposed approach provides improved accuracy compared to existing methods, achieving an accuracy of 86% when tested on cleaned EEG data. The EEG dataset was collected from 18 pilots who participated in flight experiments and publicly released at NASA’s open portal. This study presents a reliable and efficient solution for detecting mental states in pilots and highlights the potential of EEG signals and ensemble learning algorithms in developing cognitive cockpit systems. The use of an automated preprocessing pipeline, feature extraction method based on Riemannian geometry analysis, and hybrid ensemble learning technique set this work apart from previous efforts in the field and demonstrates the innovative nature of the proposed approach
Multiattention Adaptation Network for Motor Imagery Recognition
This work was supported in part by the National Natural Science Foundation of China under Grants Nos. 61873181 and 61922062Peer reviewedPostprin
EEG-based Brain-Computer Interfaces (BCIs): A Survey of Recent Studies on Signal Sensing Technologies and Computational Intelligence Approaches and Their Applications.
Brain-Computer interfaces (BCIs) enhance the capability of human brain activities to interact with the environment. Recent advancements in technology and machine learning algorithms have increased interest in electroencephalographic (EEG)-based BCI applications. EEG-based intelligent BCI systems can facilitate continuous monitoring of fluctuations in human cognitive states under monotonous tasks, which is both beneficial for people in need of healthcare support and general researchers in different domain areas. In this review, we survey the recent literature on EEG signal sensing technologies and computational intelligence approaches in BCI applications, compensating for the gaps in the systematic summary of the past five years. Specifically, we first review the current status of BCI and signal sensing technologies for collecting reliable EEG signals. Then, we demonstrate state-of-the-art computational intelligence techniques, including fuzzy models and transfer learning in machine learning and deep learning algorithms, to detect, monitor, and maintain human cognitive states and task performance in prevalent applications. Finally, we present a couple of innovative BCI-inspired healthcare applications and discuss future research directions in EEG-based BCI research
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