2019
DOI: 10.48550/arxiv.1905.04149
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A Survey on Deep Learning-based Non-Invasive Brain Signals:Recent Advances and New Frontiers

Abstract: Brain-Computer Interface (BCI) bridges human's neural world and the outer physical world by decoding individuals' brain signals into commands recognizable by computer devices. Deep learning has enhanced the performance of brain-computer interface systems signi cantly in recent years. In this article, we systematically investigate brain signal types for BCI and related deep learning concepts for brain signal analysis. We then present a comprehensive survey of deep learning techniques used for BCI, by summarizin… Show more

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Cited by 32 publications
(39 citation statements)
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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%
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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%
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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%