Deep Learning Techniques to Improve Intraoperative Awareness Detection from Electroencephalographic Signals

Avilov, Oleksii; Rimbert, Sébastien; Попов, Антон; Bougrain, Laurent

doi:10.1109/embc44109.2020.9176228

Cited by 21 publications

(10 citation statements)

References 15 publications

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“…The difference between results of MI + MNS vs. MNS and MI vs. Rest paradigms was demonstrated earlier in [10], [23]. But previous investigations were done only for the 128 electrode setup.…”

Section: Comparing Paradigmsmentioning

confidence: 87%

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Optimizing Motor Intention Detection With Deep Learning: Towards Management of Intraoperative Awareness

Avilov

Rimbert

Попов

et al. 2021

IEEE Trans. Biomed. Eng.

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Objective: This article shows the interest in deep learning techniques to detect motor imagery (MI) from raw electroencephalographic (EEG) signals when a functional electrical stimulation is added or not. Impacts of electrode montages and bandwidth are also reported. The perspective of this work is to improve the detection of intraoperative awareness during general anesthesia. Methods: Various architectures of EEGNet were investigated to optimize MI detection. They have been compared to the state-of-the-art classifiers in Brain-Computer Interfaces (based on Riemannian geometry, linear discriminant analysis), and other deep learning architectures (deep convolution network, shallow convolutional network). EEG data were measured from 22 participants performing motor imagery with and without median nerve stimulation. Results: The proposed architecture of EEGNet reaches the best classification accuracy (83.2%) and false-positive rate (FPR 19.0%) for a setup with only six electrodes over the motor cortex and frontal lobe and for an extended 4-38 Hz EEG frequency range while the subject is being stimulated via a median nerve. Configurations with a larger number of electrodes result in higher accuracy (94.5%) and FPR (6.1%) for 128 electrodes (and respectively 88.0% and 12.9% for 13 electrodes). Conclusion: The present work demonstrates that using an extended EEG frequency band and a modified EEGNet deep neural network increases the accuracy of MI detection when used with as few as 6 electrodes which include frontal channels. Significance: The proposed method contributes to the development of Brain-Computer Interface systems based on MI detection from EEG.

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Section: Comparing Paradigmsmentioning

confidence: 87%

“…There are several deep learning architectures that already showed competitive results for MI vs. Rest task in BCI domain [20]- [22]. At the same time, we already showed potential of convolutional neural networks to classify MI + MNS vs. MNS task on the smaller dataset [23]. Also, one more challenge is to decrease number of EEG electrodes.…”

Section: Introductionmentioning

confidence: 95%

Optimizing Motor Intention Detection With Deep Learning: Towards Management of Intraoperative Awareness

Avilov

Rimbert

Попов

et al. 2021

IEEE Trans. Biomed. Eng.

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“…There has been a growing interest in deep learning methods for MI classification. A recent study ( Avilov et al, 2020 ) tested three deep learning methods, including DeepConvNet, ShallowConvNet, and EEGNet, to detect MI with Median Nerve Stimulation (MNS) versus MNS during rest to prevent Accidental Awareness during General Anesthesia. The authors demonstrated that the deep learning network EEGNet outperformed not only traditional classifiers like CSP-LDA and the minimum distance to Riemannian mean algorithm (MDRM), but the other two deep learning architectures as well.…”

Section: Methodsmentioning

confidence: 99%

Toward an Adapted Neurofeedback for Post-stroke Motor Rehabilitation: State of the Art and Perspectives

Franc

Altamira

Guillen

et al. 2022

Front. Hum. Neurosci.

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Stroke is a severe health issue, and motor recovery after stroke remains an important challenge in the rehabilitation field. Neurofeedback (NFB), as part of a brain–computer interface, is a technique for modulating brain activity using on-line feedback that has proved to be useful in motor rehabilitation for the chronic stroke population in addition to traditional therapies. Nevertheless, its use and applications in the field still leave unresolved questions. The brain pathophysiological mechanisms after stroke remain partly unknown, and the possibilities for intervention on these mechanisms to promote cerebral plasticity are limited in clinical practice. In NFB motor rehabilitation, the aim is to adapt the therapy to the patient’s clinical context using brain imaging, considering the time after stroke, the localization of brain lesions, and their clinical impact, while taking into account currently used biomarkers and technical limitations. These modern techniques also allow a better understanding of the physiopathology and neuroplasticity of the brain after stroke. We conducted a narrative literature review of studies using NFB for post-stroke motor rehabilitation. The main goal was to decompose all the elements that can be modified in NFB therapies, which can lead to their adaptation according to the patient’s context and according to the current technological limits. Adaptation and individualization of care could derive from this analysis to better meet the patients’ needs. We focused on and highlighted the various clinical and technological components considering the most recent experiments. The second goal was to propose general recommendations and enhance the limits and perspectives to improve our general knowledge in the field and allow clinical applications. We highlighted the multidisciplinary approach of this work by combining engineering abilities and medical experience. Engineering development is essential for the available technological tools and aims to increase neuroscience knowledge in the NFB topic. This technological development was born out of the real clinical need to provide complementary therapeutic solutions to a public health problem, considering the actual clinical context of the post-stroke patient and the practical limits resulting from it.

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“…In deep learning, a convolutional neural network (CNN) is a class of artificial neural network (ANN), most commonly applied to image and video recognition, recommender systems, image classification, image segmentation, medical image analysis, natural language processing, braincomputer interfaces, and financial time series [1][2][3][4][5][6][7][8][9][10]. CNNs are regularized versions of multilayer perceptrons.…”

Section: Deep Learningmentioning

confidence: 99%

Hyper-Parameter Tuning in Deep Neural Network Learning

Zhan¹

2022

Artificial Intelligence and Applications

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Deep learning has been increasingly used in various applications such as image and video recognition, recommender systems, image classification, image segmentation, medical image analysis, natural language processing, brain–computer interfaces, and financial time series. In deep learning, a convolutional neural network (CNN) is regularized versions of multilayer perceptrons. Multilayer perceptrons usually mean fully connected networks, that is, each neuron in one layer is connected to all neurons in the next layer. The full connectivity of these networks makes them prone to overfitting data. Typical ways of regularization, or preventing overfitting, include penalizing parameters during training or trimming connectivity. CNNs use relatively little pre-processing compared to other image classification algorithms. Given the rise in popularity and use of deep neural network learning, the problem of tuning hyperparameters is increasingly prominent tasks in constructing efficient deep neural networks. In this paper, the tuning of deep neural network learning (DNN) hyper-parameters is explored using an evolutionary based approach popularized for use in estimating solutions to problems where the problem space is too large to get an exact solution.

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Deep Learning Techniques to Improve Intraoperative Awareness Detection from Electroencephalographic Signals

Cited by 21 publications

References 15 publications

Optimizing Motor Intention Detection With Deep Learning: Towards Management of Intraoperative Awareness

Optimizing Motor Intention Detection With Deep Learning: Towards Management of Intraoperative Awareness

Toward an Adapted Neurofeedback for Post-stroke Motor Rehabilitation: State of the Art and Perspectives

Hyper-Parameter Tuning in Deep Neural Network Learning

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