Toward EEG-Based BCI Applications for Industry 4.0: Challenges and Possible Applications

Frontiers in Human Neuroscience, Journal Year: 2021, Volume and Issue: 15

Published: Aug. 13, 2021

In the last few decades, Brain-Computer Interface (BCI) research has focused predominantly on clinical applications, notably to enable severely disabled people interact with environment. However, recent studies rely mostly use of non-invasive electroencephalographic (EEG) devices, suggesting that BCI might be ready used outside laboratories. particular, Industry 4.0 is a rapidly evolving sector aims restructure traditional methods by deploying digital tools and cyber-physical systems. BCI-based solutions are attracting increasing attention in this field support industrial performance optimizing cognitive load operators, facilitating human-robot interactions, make operations critical conditions more secure. Although these advancements seem promising, numerous aspects must considered before developing any operational solutions. Indeed, development novel applications optimal laboratory raises many challenges. current study, we carried out detailed literature review investigate main challenges present criteria relevant future deployment for 4.0.

Language: Английский

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A Multibranch of Convolutional Neural Network Models for Electroencephalogram-Based Motor Imagery Classification

Biosensors, Journal Year: 2022, Volume and Issue: 12(1), P. 22 - 22

Published: Jan. 3, 2022

Automatic high-level feature extraction has become a possibility with the advancement of deep learning, and it been used to optimize efficiency. Recently, classification methods for Convolutional Neural Network (CNN)-based electroencephalography (EEG) motor imagery have proposed, achieved reasonably high accuracy. These approaches, however, use CNN single convolution scale, whereas best scale varies from subject subject. This limits precision classification. paper proposes multibranch models address this issue by effectively extracting spatial temporal features raw EEG data, where branches correspond different filter kernel sizes. The proposed method’s promising performance is demonstrated experimental results on two public datasets, BCI Competition IV 2a dataset High Gamma Dataset (HGD). technique show 9.61% improvement in accuracy EEGNet (MBEEGNet) fixed one-branch model, 2.95% variable model. In addition, ShallowConvNet (MBShallowConvNet) improved single-scale network 6.84%. outperformed other state-of-the-art methods.

Language: Английский

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On the effects of data normalization for domain adaptation on EEG data

Engineering Applications of Artificial Intelligence, Journal Year: 2023, Volume and Issue: 123, P. 106205 - 106205

Published: March 31, 2023

In the Machine Learning (ML) literature, a well-known problem is Dataset Shift where, differently from ML standard hypothesis, data in training and test sets can follow different probability distributions, leading systems toward poor generalisation performances. This intensely felt Brain-Computer Interface (BCI) context, where bio-signals as Electroencephalographic (EEG) are often used. fact, EEG signals highly non-stationary both over time between subjects. To overcome this problem, several proposed solutions based on recent transfer learning approaches such Domain Adaption (DA). cases, however, actual causes of improvements remain ambiguous. paper focuses impact normalisation, or standardisation strategies applied together with DA methods. particular, using \textit{SEED}, \textit{DEAP}, \textit{BCI Competition IV 2a} datasets, we experimentally evaluated normalization without methods, comparing obtained It results that choice normalisation strategy plays key role classifier performances scenarios, interestingly, use only an appropriate schema outperforms technique.

Language: Английский

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Brain-computer interface enhanced by virtual reality training for controlling a lower limb exoskeleton

iScience, Journal Year: 2023, Volume and Issue: 26(5), P. 106675 - 106675

Published: April 15, 2023

This study explores the use of a brain-computer interface (BCI) based on motor imagery (MI) for control lower limb exoskeleton to aid in recovery after neural injury. The BCI was evaluated ten able-bodied subjects and two patients with spinal cord injuries. Five underwent virtual reality (VR) training session accelerate BCI. Results from this group were compared five subjects, it found that employment shorter by VR did not reduce effectiveness even improved some cases. Patients gave positive feedback about system able handle experimental sessions without reaching high levels physical mental exertion. These results are promising inclusion rehabilitation programs, future research should investigate potential MI-based system.

Language: Английский

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Implementation of artificial intelligence and machine learning-based methods in brain–computer interaction

Computers in Biology and Medicine, Journal Year: 2023, Volume and Issue: 163, P. 107135 - 107135

Published: June 8, 2023

Brain–computer interfaces are used for direct two-way communication between the human brain and computer. Brain signals contain valuable information about mental state activity of examined subject. However, due to their non-stationarity susceptibility various types interference, processing, analysis interpretation challenging. For these reasons, research in field brain–computer is focused on implementation artificial intelligence, especially five main areas: calibration, noise suppression, communication, condition estimation, motor imagery. The use algorithms based intelligence machine learning has proven be very promising application domains, ability predict learn from previous experience. Therefore, within medical technologies can contribute more accurate subjects, alleviate consequences serious diseases or improve quality life disabled patients.

Language: Английский

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