Artificial Neural Networks for Magnetoencephalography: A review of an emerging field | Paper Citation Record

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This paper cites Deep Learning.

Artificial Neural Networks for Magnetoencephalography: A review of an emerging field Deep Learning

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Artificial Neural Networks for Magnetoencephalography: A review of an emerging field A deep learning framework for neuroscience

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This paper cites Toward Next-Generation Artificial Intelligence: Catalyzing the NeuroAI Revolution.

Artificial Neural Networks for Magnetoencephalography: A review of an emerging field Toward Next-Generation Artificial Intelligence: Catalyzing the NeuroAI Revolution

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This paper cites A systematic comparison of deep learning methods for eeg time series analysis.

Artificial Neural Networks for Magnetoencephalography: A review of an emerging field A systematic comparison of deep learning methods for eeg time series analysis

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Artificial Neural Networks for Magnetoencephalography: A review of an emerging field Deep learning-based electroencephalography analysis: a systematic review

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Artificial Neural Networks for Magnetoencephalography: A review of an emerging field Magnetoencephalography: basic principles

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This paper cites Meg and eeg sensitivity in a case of medial occipital epilepsy.Brain topography, 27:192–196, 2014.

Artificial Neural Networks for Magnetoencephalography: A review of an emerging field Meg and eeg sensitivity in a case of medial occipital epilepsy.Brain topography, 27:192–196, 2014

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Artificial Neural Networks for Magnetoencephalography: A review of an emerging field Electromagnetic brain mapping

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This paper cites The relationship between meg and fmri.

Artificial Neural Networks for Magnetoencephalography: A review of an emerging field The relationship between meg and fmri

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This paper cites Recent develop- ments in spatio-temporal eeg source reconstruction techniques.

Artificial Neural Networks for Magnetoencephalography: A review of an emerging field Recent develop- ments in spatio-temporal eeg source reconstruction techniques

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Artificial Neural Networks for Magnetoencephalography: A review of an emerging field Eeg and meg: relevance to neuroscience

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Artificial Neural Networks for Magnetoencephalography: A review of an emerging field Magnetoencephalography for brain electrophysiology and imaging

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Artificial Neural Networks for Magnetoencephalography: A review of an emerging field Independent component analysis: Algorithms and applications

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Artificial Neural Networks for Magnetoencephalography: A review of an emerging field Unresolved cited work

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Artificial Neural Networks for Magnetoencephalography: A review of an emerging field Rhythms of the brain

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This paper cites Über das elektrenkephalogramm des menschen.

Artificial Neural Networks for Magnetoencephalography: A review of an emerging field Über das elektrenkephalogramm des menschen

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This paper cites Peng, Shlomo Havlin, H.

Artificial Neural Networks for Magnetoencephalography: A review of an emerging field Peng, Shlomo Havlin, H

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Artificial Neural Networks for Magnetoencephalography: A review of an emerging field Eeg analysis based on time domain properties

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Artificial Neural Networks for Magnetoencephalography: A review of an emerging field Long-term storage capacity of reservoirs

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Artificial Neural Networks for Magnetoencephalography: A review of an emerging field Unresolved cited work

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This paper cites Richman and J.

Artificial Neural Networks for Magnetoencephalography: A review of an emerging field Richman and J

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This paper cites Permutation entropy: A natural complexity measure for time series.

Artificial Neural Networks for Magnetoencephalography: A review of an emerging field Permutation entropy: A natural complexity measure for time series

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Artificial Neural Networks for Magnetoencephalography: A review of an emerging field Unresolved cited work

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Artificial Neural Networks for Magnetoencephalography: A review of an emerging field Unresolved cited work

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Artificial Neural Networks for Magnetoencephalography: A review of an emerging field A mechanism for cognitive dynamics: Neuronal communication through neuronal coherence

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Artificial Neural Networks for Magnetoencephalography: A review of an emerging field Unresolved cited work

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Artificial Neural Networks for Magnetoencephalography: A review of an emerging field Stam, Guido Nolte, and Andreas Daffertshofer

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Artificial Neural Networks for Magnetoencephalography: A review of an emerging field Ilmoniemi, Jukka Knuutila, and Olli V

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Artificial Neural Networks for Magnetoencephalography: A review of an emerging field Classical statistics and statistical learning in imaging neuroscience

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Artificial Neural Networks for Magnetoencephalography: A review of an emerging field A unified bayesian framework for meg/eeg source imaging

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Artificial Neural Networks for Magnetoencephalography: A review of an emerging field When null hypothesis significance testing is unsuitable for research: a reassessment

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Artificial Neural Networks for Magnetoencephalography: A review of an emerging field The perceptron: A probabilistic model for information storage and organization in the brain

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Artificial Neural Networks for Magnetoencephalography: A review of an emerging field The Elements of Statistical Learning: Data Mining, Inference, and Prediction

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Artificial Neural Networks for Magnetoencephalography: A review of an emerging field Khoshgoftaar

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Artificial Neural Networks for Magnetoencephalography: A review of an emerging field Rumelhart, Geoffrey E

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Artificial Neural Networks for Magnetoencephalography: A review of an emerging field Long short-term memory

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Artificial Neural Networks for Magnetoencephalography: A review of an emerging field Recent advances at the interface of neuroscience and artificial neural networks

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Artificial Neural Networks for Magnetoencephalography: A review of an emerging field Classification and analysis of minimally-processed data from a large magnetoencephalography dataset using convolutional neural networks

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Artificial Neural Networks for Magnetoencephalography: A review of an emerging field Canet: A channel attention network to determine informative multi-channel for image classification from brain signals

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Artificial Neural Networks for Magnetoencephalography: A review of an emerging field Towards a speaker independent speech-bci using speaker adaptation

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Artificial Neural Networks for Magnetoencephalography: A review of an emerging field Cross-subject meg decoding using 3d convolutional neural networks

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Artificial Neural Networks for Magnetoencephalography: A review of an emerging field Inter-subject meg decoding for visual information with hybrid gated recurrent network

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This paper cites Decoding neural representations of rhythmic sounds from magnetoen- cephalography.

Artificial Neural Networks for Magnetoencephalography: A review of an emerging field Decoding neural representations of rhythmic sounds from magnetoen- cephalography

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Artificial Neural Networks for Magnetoencephalography: A review of an emerging field Comparing methods of feature extraction of brain activities for octave illusion classification using machine learning

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Artificial Neural Networks for Magnetoencephalography: A review of an emerging field A reusable benchmark of brain-age prediction from m/eeg resting-state signals

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Artificial Neural Networks for Magnetoencephalography: A review of an emerging field Categorizing objects from meg signals using eegnet

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Artificial Neural Networks for Magnetoencephalography: A review of an emerging field Decoding the temporal representation of facial expression in face-selective regions

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Artificial Neural Networks for Magnetoencephalography: A review of an emerging field Group-level brain decoding with deep learning

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Artificial Neural Networks for Magnetoencephalography: A review of an emerging field Brain decoding over the meg signals using riemannian approach and machine learning

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Artificial Neural Networks for Magnetoencephalography: A review of an emerging field Magnetoencephalogram-based brain–computer interface for hand-gesture decoding using deep learn- ing

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Artificial Neural Networks for Magnetoencephalography: A review of an emerging field Megformer: enhancing speech decoding from brain activity through extended semantic representations

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Artificial Neural Networks for Magnetoencephalography: A review of an emerging field Mad: Multi-alignment meg-to-text decoding

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Artificial Neural Networks for Magnetoencephalography: A review of an emerging field Robust discrimination of multiple naturalistic same-hand movements from meg signals with convolutional neural networks

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Artificial Neural Networks for Magnetoencephalography: A review of an emerging field NeuGPT: Unified multi-modal Neural GPT

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Artificial Neural Networks for Magnetoencephalography: A review of an emerging field The Brain's Bitter Lesson: Scaling Speech Decoding With Self-Supervised Learning

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Artificial Neural Networks for Magnetoencephalography: A review of an emerging field Sparse autoencoders for word decoding from magnetoencephalography

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Artificial Neural Networks for Magnetoencephalography: A review of an emerging field Brain network analysis and classification based on convolutional neural network

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Artificial Neural Networks for Magnetoencephalography: A review of an emerging field Automatic diagnosis of neurological diseases using meg signals with a deep neural network

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Artificial Neural Networks for Magnetoencephalography: A review of an emerging field Multi-head self-attention model for classification of temporal lobe epilepsy subtypes

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Artificial Neural Networks for Magnetoencephalography: A review of an emerging field Predicting ptsd severity using longitudinal magnetoencephalography with a multi-step learning framework

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Artificial Neural Networks for Magnetoencephalography: A review of an emerging field A graph gaussian embedding method for predicting alzheimer’s disease progression with meg brain networks

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Artificial Neural Networks for Magnetoencephalography: A review of an emerging field Deep-meg: spatiotemporal cnn features and multiband ensemble classification for predicting the early signs of alzheimer’s disease with magnetoen- cephalography

Reference 83

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Artificial Neural Networks for Magnetoencephalography: A review of an emerging field Classification of meg signals in schizophrenia based on eegnet

Reference 84

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This paper cites Resting-state magnetoencephalography source magnitude imaging with deep-learning neural network for classification of symptomatic combat-related mild traumatic brain injury.

Artificial Neural Networks for Magnetoencephalography: A review of an emerging field Resting-state magnetoencephalography source magnitude imaging with deep-learning neural network for classification of symptomatic combat-related mild traumatic brain injury

Reference 85

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Artificial Neural Networks for Magnetoencephalography: A review of an emerging field Unresolved cited work

Reference 86

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Artificial Neural Networks for Magnetoencephalography: A review of an emerging field Abnormal phase–amplitude coupling characterizes the interictal state in epilepsy

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Artificial Neural Networks for Magnetoencephalography: A review of an emerging field Functional connectivity based machine learning approach for autism detection in young children using meg signals

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Artificial Neural Networks for Magnetoencephalography: A review of an emerging field Imnmagn: Integrative multimodal approach for enhanced detection of neurodegenerative diseases using fusion of multidomain analysis with graph networks

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Artificial Neural Networks for Magnetoencephalography: A review of an emerging field Synaptic function and sensory processing in zdhhc9-associated neurodevelopmental disorder: a mechanistic account

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Artificial Neural Networks for Magnetoencephalography: A review of an emerging field Determining the optimal number of meg trials: A machine learning and speech decoding perspective

Reference 91

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Artificial Neural Networks for Magnetoencephalography: A review of an emerging field Overt speech retrieval from neuromagnetic signals using wavelets and artificial neural networks

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Artificial Neural Networks for Magnetoencephalography: A review of an emerging field Kinesthetic and visual modes of imaginary movement: Meg studies for bci development

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Artificial Neural Networks for Magnetoencephalography: A review of an emerging field A meg study of different motor imagery modes in untrained subjects for bci applications

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Artificial Neural Networks for Magnetoencephalography: A review of an emerging field Adaptive neural network classifier for decoding meg signals

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Artificial Neural Networks for Magnetoencephalography: A review of an emerging field Automatic speech activity recognition from meg signals using seq2seq learning

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Artificial Neural Networks for Magnetoencephalography: A review of an emerging field Decoding imagined and spoken phrases from non-invasive neural (meg) signals

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Artificial Neural Networks for Magnetoencephalography: A review of an emerging field Neurovad: Real-time voice activity detection from non-invasive neuromagnetic signals

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Artificial Neural Networks for Magnetoencephalography: A review of an emerging field Lstm improves accuracy of reaching trajectory prediction from magnetoencephalography signals

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Artificial Neural Networks for Magnetoencephalography: A review of an emerging field Decoding speech evoked jaw motion from non-invasive neuromagnetic oscillations

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