A Survey on Deep Learning-based Non-Invasive Brain Signals:Recent Advances and New Frontiers

Zhang, Xiang; Yao, Lina; Wang, Xianzhi; Monaghan, Jessica J. M.; McAlpine, David; Zhang, Yu

doi:10.48550/arxiv.1905.04149

Cited by 32 publications

(39 citation statements)

References 259 publications

(252 reference statements)

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“…Because this dataset is not separated into training and test samples, we conducted a five-fold cross-validation for a fair evaluation. For the MI datasets, we preprocessed signals by applying a large Laplacian filtering 5 , baseline correction by subtracting the mean value of the fixation signal from each MI trial, and band-pass filtering between 4 and 40Hz. Then, we removed the first and last 0.5 sec from each trial, and finally applied Gaussian normalization.…”

Section: A Datasets and Preprocessingmentioning

confidence: 99%

“…In this paper, our focus is not only on active BCIs but also on passive BCIs. Generally, two types of brain signals such as evoked and spontaneous EEG are primarily considered for active/reactive W. Ko BCIs [5]. Evoked BCIs exploit unintentional electrical potentials reacting to external or internal stimuli.…”

Section: Introductionmentioning

confidence: 99%

“…While hand-crafted feature representation learning has a pivotal role in a conventional machine learning framework [1], [7], [14], deep learning-based representation has had remarkable results in the BCI community [2], [3], [5]. These deep learning-based methods have integrated a feature extraction step with a classifier learning step such that those steps are jointly optimized, thereby improving performance.…”

Section: Introductionmentioning

confidence: 99%

“…All codes used in our experiments are available at 'https: //github.com/DeepBCI/Deep-BCI/tree/master/1 Intelligent BCI/Multi Scale Neural Network for EEG Representation Learning in BCI. '2 Available at http://gigadb.org/dataset/1002953 Available at http://gigadb.org/dataset/1005424 Experimental results of the KU-MI dataset[6] are reported in Supplementary B 5. When the target channel does not have four nearest neighbors, we just used available channels and their average value to filter the target channel.…”

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confidence: 99%

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Multi-Scale Neural network for EEG Representation Learning in BCI

Ko¹,

Jeon²,

Jeong

et al. 2020

Preprint

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Recent advances in deep learning have had a methodological and practical impact on brain-computer interface (BCI) research. Among the various deep network architectures, convolutional neural networks (CNNs) have been well suited for spatio-spectral-temporal electroencephalogram (EEG) signal representation learning. Most of the existing CNN-based methods described in the literature extract features at a sequential level of abstraction with repetitive nonlinear operations and involve densely connected layers for classification. However, studies in neurophysiology have revealed that EEG signals carry information in different ranges of frequency components. To better reflect these multi-frequency properties in EEGs, we propose a novel deep multi-scale neural network that discovers feature representations in multiple frequency/time ranges and extracts relationships among electrodes, i.e., spatial representations, for subject intention/condition identification. Furthermore, by completely representing EEG signals with spatio-spectral-temporal information, the proposed method can be utilized for diverse paradigms in both active and passive BCIs, contrary to existing methods that are primarily focused on single-paradigm BCIs. To demonstrate the validity of our proposed method, we conducted experiments on various paradigms of active/passive BCI datasets. Our experimental results demonstrated that the proposed method achieved performance improvements when judged against comparable state-of-the-art methods. Additionally, we analyzed the proposed method using different techniques, such as PSD curves and relevance score inspection to validate the multi-scale EEG signal information capturing ability, activation pattern maps for investigating the learned spatial filters, and t-SNE plotting for visualizing represented features. Finally, we also demonstrated our method's application to real-world problems.

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Section: A Datasets and Preprocessingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Multi-Scale Neural network for EEG Representation Learning in BCI

Ko¹,

Jeon²,

Jeong

et al. 2020

Preprint

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“…For different movement imaginations that reflect users' intentions, brain signals induce different spatial and temporal frequency information. During MI task, event-related desynchronization/synchronization (ERD/ERS) features are revealed in beta band [14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30] Hz and mu band [8][9][10][11][12][13][14] Hz respectively [22], [23], which are referred to as a sensory-motor rhythm in the primary sensorimotor area. Based on this neurophysiological basis, we considered that MI is suitable for controlling BCI-based devices that reflect user intentions.…”

Section: Introductionmentioning

confidence: 99%

Motor Imagery Classification Emphasizing Corresponding Frequency Domain Method based on Deep Learning Framework

Kwon

Lee

Jeong

2021

2021 9th International Winter Conference on Brain-Computer Interface (BCI)

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The electroencephalogram, a type of non-invasivebased brain signal that has a user intention-related feature provides an efficient bidirectional pathway between user and computer. In this work, we proposed a deep learning framework based on corresponding frequency empahsize method to decode the motor imagery (MI) data from 2020 International BCI competition dataset. The MI dataset consists of 3-class, namely 'Cylindrical', 'Spherical', and 'Lumbrical'. We utilized power spectral density as an emphasize method and a convolutional neural network to classify the modified MI data. The results showed that MI-related frequency range was activated during MI task, and provide neurophysiological evidence to design the proposed method. When using the proposed method, the average classification performance in intra-session condition was 69.68% and the average classification performance in intersession condition was 52.76%. Our results provided the possibility of developing a BCI-based device control system for practical applications.

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Soft Electronics for Health Monitoring Assisted by Machine Learning

et al. 2023

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Due to the development of the novel materials, the past two decades have witnessed the rapid advances of soft electronics. The soft electronics have huge potential in the physical sign monitoring and health care. One of the important advantages of soft electronics is forming good interface with skin, which can increase the user scale and improve the signal quality. Therefore, it is easy to build the specific dataset, which is important to improve the performance of machine learning algorithm. At the same time, with the assistance of machine learning algorithm, the soft electronics have become more and more intelligent to realize real-time analysis and diagnosis. The soft electronics and machining learning algorithms complement each other very well. It is indubitable that the soft electronics will bring us to a healthier and more intelligent world in the near future. Therefore, in this review, we will give a careful introduction about the new soft material, physiological signal detected by soft devices, and the soft devices assisted by machine learning algorithm. Some soft materials will be discussed such as two-dimensional material, carbon nanotube, nanowire, nanomesh, and hydrogel. Then, soft sensors will be discussed according to the physiological signal types (pulse, respiration, human motion, intraocular pressure, phonation, etc.). After that, the soft electronics assisted by various algorithms will be reviewed, including some classical algorithms and powerful neural network algorithms. Especially, the soft device assisted by neural network will be introduced carefully. Finally, the outlook, challenge, and conclusion of soft system powered by machine learning algorithm will be discussed.

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A Survey on Deep Learning-based Non-Invasive Brain Signals:Recent Advances and New Frontiers

Cited by 32 publications

References 259 publications

Multi-Scale Neural network for EEG Representation Learning in BCI

Multi-Scale Neural network for EEG Representation Learning in BCI

Motor Imagery Classification Emphasizing Corresponding Frequency Domain Method based on Deep Learning Framework

Soft Electronics for Health Monitoring Assisted by Machine Learning

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