A Deep Learning Scheme for Motor Imagery Classification based on Restricted Boltzmann Machines

By Na Lu, Tengfei Li, Xiaodong Ren, Hongyu Miao. IEEE transactions on neural systems and rehabilitation engineering, 2017

375 citations recorded by the source corpus.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/226575b9b189989965d44538c70b710b526d818f

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In Cellular and Molecular Neuroscience

In Cognitive Neuroscience

In Electrical and Electronic Engineering

Citations in the corpus

Current Status, Challenges, and Possible Solutions of EEG-Based Brain-Computer Interface: A Comprehensive Review (2020, methodology): “Several studies have explored MI classiﬁcation with DBN (An et al., 2014; Tang et al., 2015; Lu et al., 2017; Ortega et al., 2017).”
Applications of Deep Learning and Reinforcement Learning to Biological Data (2017, methodology): “DBN was also employed– in combination with softmax regression to classify signal frequency information as features [161]; and in conjunction with Ada-boost algorithm to classify single channels [162].”
EEG-Based Brain-Computer Interfaces Using Motor-Imagery: Techniques and Challenges (2019, methodology): “These include deep learning architectures [5,58,100], as well as RNNs [28], which were previously discussed.”
HS-CNN: a CNN with hybrid convolution scale for EEG motor imagery classification (2020, positive): “The authors of [29] developed a deep learning scheme named FDBN, which is based on a restricted Boltzmann machine (RBM) and uses frequency domain features as input.”
Deep learning for electroencephalogram (EEG) classification tasks: a review (2019, background): “1 FC 140 trials 2 OUT [105] DBN Softmax (FC) f—WD 0.”
Deep Learning in Mining Biological Data (2020, methodology): “ation ADNI dataset DBN [73,74] AD/MCI diagnosis ADHD-200 DBN [79] ADHD detection MICCAI 2009 LV DBN [80] Heart LV segmentation Signals MAHNOB-HCI DA [81] Motion action decoding BCI Competition IV DBN [82], CNN [83{85] Motion action decoding DEAP dataset DBN [86,87] Motion action decoding DEAP datase”
The Internet of Things: Impact and Implications for Health Care Delivery (2020, background): “…include predicting risk, future medical outcomes, and care decisions in diabetes and mental health [57] and predicting the progression of congestive heart failure [58,59], bone disease [60], Alzheimer disease [61], benign and malignant tumor classification [62,63], and cardiac arrhythmias [64].”
LSTM-Based EEG Classification in Motor Imagery Tasks (2018, methodology): “[21] apply Deep Belief Networks (DBN) to generates auto-encoded features.”
Deep learning for healthcare applications based on physiological signals: A review (2018, methodology): “, 2017 [109] Motor imagery classification RBM BCI competition IV data set 2b [110, 111, 112] Accuracy has been improved about 5% compared with other methods.”
EEGNet: a compact convolutional neural network for EEG-based brain–computer interfaces (2016, methodology): “Restricted Boltzman machines (RBMs) have been used for motor imagery [41].”