Filters








75 Hits in 7.2 sec

Deep learning approaches for neural decoding: from CNNs to LSTMs and spikes to fMRI [article]

Jesse A. Livezey, Joshua I. Glaser
2020 arXiv   pre-print
The success of deep networks in other domains has led to a new wave of applications in neuroscience. In this article, we review deep learning approaches to neural decoding.  ...  Deep learning has been shown to be a useful tool for improving the accuracy and flexibility of neural decoding across a wide range of tasks, and we point out areas for future scientific development.  ...  Acknowledgements We would like to thank Ella Batty and Charles Frye for very helpful comments on this manuscript.  ... 
arXiv:2005.09687v1 fatcat:grboww5ptvah5npbl3xeehbady

Neural Decoding of Inferior Colliculus Multiunit Activity for Sound Category identification with temporal correlation and deep learning [article]

Fatma Ozcan, Ahmet Alkan
2022 bioRxiv   pre-print
For this, we applied transfer learning from Alexnet, GoogleNet and Squeezenet CNNs. The classifiers support vector machines (SVM), k-nearest neighbour (KNN), Naive Bayes and Ensemble were used.  ...  Using pre-trained convolutional neural networks (CNNs), features of the images were extracted and the type of sound heard was classified.  ...  Livezey and Glaser (2020), in their study entitled "Deep Learning approaches for neural decoding: from CNNs to LSTMs and spikes to fMRI", examined deep learning approaches for neural decoding.  ... 
doi:10.1101/2022.08.24.505211 fatcat:77kgi7rn2zdynmqjlstwpfnssi

Brain2Char: A Deep Architecture for Decoding Text from Brain Recordings [article]

Pengfei Sun and Gopala K. Anumanchipalli and Edward F. Chang
2019 arXiv   pre-print
Brain2Char framework combines state-of-the-art deep learning modules --- 3D Inception layers for multiband spatiotemporal feature extraction from neural data and bidirectional recurrent layers, dilated  ...  Decoding language representations directly from the brain can enable new Brain-Computer Interfaces (BCI) for high bandwidth human-human and human-machine communication.  ...  for decoding text from brain data.  ... 
arXiv:1909.01401v1 fatcat:mmmj75x7v5dhdk2jfgfqnzpm3m

Beyond the brain-computer interface: Decoding brain activity as a tool to understand neuronal mechanisms subtending cognition and behavior

Célia Loriette, Julian L. Amengual, Suliann Ben Hamed
2022 Frontiers in Neuroscience  
In this context, the inclusion of machine learning techniques to decode different aspects of human cognition and behavior and its use to develop brain–computer interfaces for applications in neuroprosthetics  ...  However, while these approaches have been shown quite successful for the study of the motor and sensory functions, success is still far from being reached when it comes to covert cognitive functions such  ...  As a response to this need, deep learning approaches have been introduced for neural decoding purposes.  ... 
doi:10.3389/fnins.2022.811736 pmid:36161174 pmcid:PMC9492914 fatcat:t5zsiu4hdzb2bahjjguu24cz5q

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

Xiang Zhang, Lina Yao, Xianzhi Wang, Jessica Monaghan, David Mcalpine, Yu Zhang
2020 arXiv   pre-print
In this article, we systematically investigate brain signal types for BCI and related deep learning concepts for brain signal analysis.  ...  Finally, we discuss the applied areas, opening challenges, and future directions for deep learning-based BCI.  ...  Representative Deep Learning Models The term of representative deep learning refers to use deep neural network for representation learning.  ... 
arXiv:1905.04149v5 fatcat:sjz3wvw5vvch3ncxnvflgbob3a

Epileptic seizure detection using deep learning techniques: A Review [article]

Afshin Shoeibi, Navid Ghassemi, Marjane Khodatars, Mahboobeh Jafari, Sadiq Hussain, Roohallah Alizadehsani, Parisa Moridian, Abbas Khosravi, Hossein Hosseini-Nejad, Modjtaba Rouhani, Assef Zare, Ali Khadem (+3 others)
2020 arXiv   pre-print
In this study, a comprehensive overview of the types of deep learning methods exploited to diagnose epileptic seizures from various modalities has been studied.  ...  A variety of screening approaches have been proposed to diagnose epileptic seizures, using Electroencephalography (EEG) and Magnetic Resonance Imaging (MRI) modalities.  ...  The researchers [126] designed DeepIED based on deep learning and EEG-fMRI scans for epilepsy patients, combining the general linear model with EEG-fMRI techniques to estimate the epileptogenic zone.  ... 
arXiv:2007.01276v2 fatcat:2mfgpishmrculocm32ui7iemym

Deep Learning Methods to Process fMRI Data and Their Application in the Diagnosis of Cognitive Impairment: A Brief Overview and Our Opinion

Dong Wen, Zhenhao Wei, Yanhong Zhou, Guolin Li, Xu Zhang, Wei Han
2018 Frontiers in Neuroinformatics  
When we studied this topic, we mainly searched the literature from the database of Web of Science, and reviewed references to the application of deep learning methods in the field of fMRI from 2004 to  ...  In another approach, four hidden layers were added in between the encoder and the decoder in the FNN method (Patel et al., 2016) .  ...  The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner are credited and that the original publication in this journal is cited, in accordance  ... 
doi:10.3389/fninf.2018.00023 pmid:29755334 pmcid:PMC5932168 fatcat:o5fnf7ear5ajdlae7crwny5aum

Frontal Cortex Neuron Type Classification with Deep Learning and Recurrence Plot

Fatma Özcan, Ahmet Alkan
2021 Traitement du signal  
AlexNet with CNN deep learning method, and frontal cortex nerve cell type classification was made.  ...  Classification of neurons with only spike timing values has not been studied before, with deep learning, without knowing all of the wave properties and the intercellular interactions.  ...  Livezey and Glaser [28] in his work entitled "Deep learning approaches for neural decoding: From CNNs to LSTMs and spikes to fMRI", reviewed deep learning approaches for neural decoding.  ... 
doi:10.18280/ts.380327 fatcat:irdywthqr5crnmxvdgxdwwjc3u

EEG-based Brain-Computer Interfaces (BCIs): A Survey of Recent Studies on Signal Sensing Technologies and Computational Intelligence Approaches and their Applications [article]

Xiaotong Gu, Zehong Cao, Alireza Jolfaei, Peng Xu, Dongrui Wu, Tzyy-Ping Jung, Chin-Teng Lin
2020 arXiv   pre-print
Recent technological advances such as wearable sensing devices, real-time data streaming, machine learning, and deep learning approaches have increased interest in electroencephalographic (EEG) based BCI  ...  Furthermore, we demonstrate state-of-art computational intelligence techniques, including interpretable fuzzy models, transfer learning, deep learning, and combinations, to monitor, maintain, or track  ...  ACKNOWLEDGMENT Credit authors for icons made from www.flaticon.com.  ... 
arXiv:2001.11337v1 fatcat:cmurfjykjja3rdifr7e7cqq3wy

Epileptic Seizures Detection Using Deep Learning Techniques: A Review

Afshin Shoeibi, Marjane Khodatars, Navid Ghassemi, Mahboobeh Jafari, Parisa Moridian, Roohallah Alizadehsani, Maryam Panahiazar, Fahime Khozeimeh, Assef Zare, Hossein Hosseini-Nejad, Abbas Khosravi, Amir F. Atiya (+5 others)
2021 International Journal of Environmental Research and Public Health  
Artificial intelligence encompasses a variety of areas, and one of its branches is deep learning (DL).  ...  A variety of screening approaches have been proposed to diagnose epileptic seizures, using electroencephalography (EEG) and magnetic resonance imaging (MRI) modalities.  ...  Review of Deep Learning Techniques In contrast to conventional neural networks, or so-called shallow networks, deep neural networks are structures with more than two hidden layers.  ... 
doi:10.3390/ijerph18115780 pmid:34072232 fatcat:vdok6mql4rfxln737tjb23ufte

EEG-based Brain-Computer Interfaces (BCIs): A Survey of Recent Studies on Signal Sensing Technologies and Computational Intelligence Approaches and Their Applications

Xiaotong Gu, Zehong Cao, Alireza Jolfaei, Peng Xu, Dongrui Wu, Tzyy-Ping Jung, Chin-Teng Lin
2021 IEEE/ACM Transactions on Computational Biology & Bioinformatics  
Then, we demonstrate state-of-the-art computational intelligence techniques, including fuzzy models and transfer learning in machine learning and deep learning algorithms, to detect, monitor, and maintain  ...  In this review, we survey the recent literature on EEG signal sensing technologies and computational intelligence approaches in BCI applications, compensating for the gaps in the systematic summary of  ...  This work was also supported in part by grants from US NSF (CBET-1935860, NCS-1734883, IP-1719130, and SMA-1540943) and the US Army Research Lab STRONG Program to TPJ.  ... 
doi:10.1109/tcbb.2021.3052811 fatcat:nw5ljp7s35ai3ozaabgjdb6l2e

Deep Learning in Physiological Signal Data: A Survey

Rim, Sung, Min, Hong
2020 Sensors  
of 1D signals have yet to be beneficially exploited from this novel approach to fulfil the desired medical tasks.  ...  The key parameters of deep-learning approach that we review are the input data type, deep-learning task, deep-learning model, training architecture, and dataset sources.  ...  Capsule network CNNE Convolutional neural network as a feature extractor CP-MixedNet Channel-projection mixed-scale convolutional neural network CssC DBM Contractive Slab and Spike Convolutional Deep  ... 
doi:10.3390/s20040969 pmid:32054042 pmcid:PMC7071412 fatcat:5ga4um5zsfddtpp47csw2dkyce

Deep Learning in Mining Biological Data

Mufti Mahmud, M. Shamim Kaiser, T. Martin McGinnity, Amir Hussain
2021 Cognitive Computation  
Artificial neural network-based learning systems are well known for their pattern recognition capabilities, and lately their deep architectures—known as deep learning (DL)—have been successfully applied  ...  Mining such enormous amount of data for pattern recognition is a big challenge and requires sophisticated data-intensive machine learning techniques.  ...  Acknowledgements The authors would like to thank the members of the acslab (http://www.acsla b.info/) for valuable discussions. Author Contributions  ... 
doi:10.1007/s12559-020-09773-x pmid:33425045 pmcid:PMC7783296 fatcat:n4nk7gakfbb4fbhdi5pqeojwjm

2020 Index IEEE Journal of Biomedical and Health Informatics Vol. 24

2020 IEEE journal of biomedical and health informatics  
., and Inan, O.T., A Globalized Model for Mapping Wearable Seismocardiogram Signals to Whole-Body Ballistocardiogram Signals Based on Deep Learning; JBHI May 2020 1296-1309 Herskovic, V., see Saint-Pierre  ...  , J., and Rubin, D. 2570-2579 Jiang, D., see 2473-2480 Jiang, H., see 2798-2805 Jiang, H., Yang, M., Chen, X., Li, M., Li, Y., and Wang, J., miRTMC: A miRNA Target Prediction Method Based on Matrix  ...  ., +, JBHI Sept. 2020 2473-2480 Decoding Brain States From fMRI Signals by Using Unsupervised Domain Adaptation.  ... 
doi:10.1109/jbhi.2020.3048808 fatcat:iifrkwtzazdmboabdqii7x5ukm

3D Deep Learning on Medical Images: A Review [article]

Satya P. Singh, Lipo Wang, Sukrit Gupta, Haveesh Goli, Parasuraman Padmanabhan, Balázs Gulyás
2020 arXiv   pre-print
In this paper, we trace the history of how the 3D CNN was developed from its machine learning roots, we provide a brief mathematical description of 3D CNN and provide the preprocessing steps required for  ...  The rapid advancements in machine learning, graphics processing technologies and the availability of medical imaging data have led to a rapid increase in the use of deep learning models in the medical  ...  Therefore, it is important to decode the trained network using model interpretability approaches and validate the important features learned by the network [127] .  ... 
arXiv:2004.00218v4 fatcat:iucszcjffnbwbbzc4zzqpbvahy
« Previous Showing results 1 — 15 out of 75 results