Relevant bibliographies by topics / Brain decoding

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Brain decoding'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Brain decoding.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "Brain decoding"

1

Greenemeier, Larry. "Decoding the Brain." Scientific American Mind 25, no. 6 (October 16, 2014): 40–45. http://dx.doi.org/10.1038/scientificamericanmind1114-40.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Xu, Min, Duo Li, and Ping Li. "Brain decoding in multiple languages: Can cross-language brain decoding work?" Brain and Language 215 (April 2021): 104922. http://dx.doi.org/10.1016/j.bandl.2021.104922.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Thomson, Helen. "Decoding the brain beat." New Scientist 207, no. 2768 (July 2010): 28–31. http://dx.doi.org/10.1016/s0262-4079(10)61684-3.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Zajanckauskaite, Estera. "Decoding the adolescent brain." Lancet Child & Adolescent Health 2, no. 3 (March 2018): 169. http://dx.doi.org/10.1016/s2352-4642(18)30003-8.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Smith, Kerri. "Brain decoding: Reading minds." Nature 502, no. 7472 (October 2013): 428–30. http://dx.doi.org/10.1038/502428a.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Benson, Philippa J. "Decoding brain-computer interfaces." Science 360, no. 6389 (May 10, 2018): 615.8–616. http://dx.doi.org/10.1126/science.360.6389.615-h.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Heinzle, J., S. Anders, S. Bode, C. Bogler, Y. Chen, R. M. Cichy, K. Hackmack, et al. "Multivariate decoding of fMRI data." e-Neuroforum 18, no. 1 (January 1, 2012): 1–16. http://dx.doi.org/10.1007/s13295-012-0026-9.

Full text
Abstract:
AbstractThe advent of functional magnetic resonance imaging (fMRI) of brain function 20 years ago has provided a new methodology for non-in­vasive measurement of brain function that is now widely used in cognitive neurosci­ence. Traditionally, fMRI data has been an­alyzed looking for overall activity chang­es in brain regions in response to a stimu­lus or a cognitive task. Now, recent develop­ments have introduced more elaborate, con­tent-based analysis techniques. When mul­tivariate decoding is applied to the detailed patterning of regionally-specific fMRI signals, it can be used to assess the amount of infor­mation these encode about specific task-vari­ables. Here we provide an overview of sev­eral developments, spanning from applica­tions in cognitive neuroscience (perception, attention, reward, decision making, emotion­al communication) to methodology (informa­tion flow, surface-based searchlight decod­ing) and medical diagnostics.
APA, Harvard, Vancouver, ISO, and other styles
8

Friston, Karl, Carlton Chu, Janaina Mourão-Miranda, Oliver Hulme, Geraint Rees, Will Penny, and John Ashburner. "Bayesian decoding of brain images." NeuroImage 39, no. 1 (January 2008): 181–205. http://dx.doi.org/10.1016/j.neuroimage.2007.08.013.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Schultz, Johannes. "Brain Imaging: Decoding Your Memories." Current Biology 20, no. 6 (March 2010): R269—R271. http://dx.doi.org/10.1016/j.cub.2010.02.001.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Kohl, Johannes, and Gregory S. X. E. Jefferis. "Neuroanatomy: Decoding the Fly Brain." Current Biology 21, no. 1 (January 2011): R19—R20. http://dx.doi.org/10.1016/j.cub.2010.11.067.

Full text
APA, Harvard, Vancouver, ISO, and other styles
More sources

Dissertations / Theses on the topic "Brain decoding"

1

Stetner, Michael E. "Improving decoding in intracortical brain-machine interfaces." Cleveland, Ohio : Case Western Reserve University, 2009. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=case1254235417.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Stetner, Michael E. "Improving decoding in intracortical brain-machine interfaces." Case Western Reserve University School of Graduate Studies / OhioLINK, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=case1254235417.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Peng, Catherine Yee-yuen. "Decoding facial expressions of emotion." Thesis, University of Oxford, 1989. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.253287.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Wei, Chun-Shu. "Towards Brain Decoding for Real-World Drowsiness Detection." Thesis, University of California, San Diego, 2018. http://pqdtopen.proquest.com/#viewpdf?dispub=10641645.

Full text
Abstract:

A brain-computer interface (BCI) allows human to communicate with a computer by thoughts. Recent advances in brain decoding have shown the capability of BCIs in monitoring physiological and cognitive state of the brain, including drowsiness. Since drowsy driving has been an urgent issue in vehicle safety that causes numerous deaths and injuries, BCIs based on non-invasive electroencephalogram (EEG) are developed to monitor drivers’ drowsiness continuously and instantaneously. Nonetheless, on the pathway of transitioning laboratory-oriented BCI into real-world applications, there are major challenges that limit the usability and convenience for drowsiness detection (DD). To completely understand the association between human EEG and drowsiness, this study employed a large-scale dataset collected from simulated driving experiments with a lane-keeping task and EEG recordings. A DD-BCI that acquires EEG from only non-hair-bearing (NHB) areas was proposed to maximize the comfort and convenience. The performance of the NHB DD-BCI was validated and compared with that using whole-scalp EEG, showing no significant difference in the accuracy of alert/drowsy classification. In addition, a subject-transfer framework that leverages large-scale existing data from other subjects was proposed to reduce the calibration time of a DD-BCI. Alert baseline data were involved to enhance the efficiency of subject-to-subject model transfer. The subject-transfer approach significantly reduced the calibration time of the DD-BCI, exhibiting the potential in facilitating plug-and-play brain decoding for real-world BCI applications. Overall, this thesis presents the contributions to developing a DD-BCI for real-world use with maximal usability and convenience. The methodologies and findings could further catalyze the exploration of real-world BCIs in more applications.

APA, Harvard, Vancouver, ISO, and other styles
5

Markides, Loizos. "Scalable, data-driven brain decoding using functional MRI." Thesis, Imperial College London, 2014. http://hdl.handle.net/10044/1/28082.

Full text
Abstract:
Functional Magnetic Resonance Imaging (fMRI) has established the field of brain decoding, meaning the prediction of the task that a subject is performing in the MRI scanner, given the corresponding images. This has been quite successful, especially when attempting discrimination between the representations of two or four distinct stimuli across the brains of multiple subjects. However, there are currently only a few studies that deal with ways to improve the scalability of existing brain decoding methodologies, in order for the resulting classifiers to be able to discriminate among tens or hundreds of possible stimuli. Such advances have potential for the creation of rigorous brain-computer interfaces, which could establish a solid communication channel with people in a vegetative state. In this work, I propose and evaluate a series of methods leading to the development of a new data-driven, scalable brain decoding framework that will enable better stimulus discrimination. The methods include: (1) A novel inter-subject spatial feature selection method that can be run using the native brain images of each subject directly, and which is not sensitive to differences in the morphology of the brain of each subject. (2) Three novel data-driven feature selection methods that use statistical association metrics in order to select regions that exhibit similar behaviour across-subjects over the course of a given experiment. The methods aim to promote enhanced exploratory power and are not susceptible to region-specific variations of the haemodynamic response function. (3) Two novel data-driven temporal denoising algorithms that can be used to improve the signal-to-noise ratio of any given task-related fMRI image and which do not impose constraints in either the experimental design nor the nature of the involved stimuli. (4) A thorough evaluation of four intensity normalisation techniques that are commonly used for across-subjects and across-sessions decoding, in order to determine their applicability for across-datasets decoding. (5) A novel feature compression and information recovery method that aims at lowering the system memory requirements for training and testing a large-scale brain decoding model using multiple datasets simultaneously.
APA, Harvard, Vancouver, ISO, and other styles
6

Marathe, Amar Ravindra. "Improved decoding for brain-machine interfaces for continuous movement control." Case Western Reserve University School of Graduate Studies / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=case1301667321.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Zhang, Suyi. "Encoding and decoding of pain relief in the human brain." Thesis, University of Cambridge, 2019. https://www.repository.cam.ac.uk/handle/1810/286332.

Full text
Abstract:
The studies in this thesis explored how pain and its relief are represented in the human brain. Pain and relief are important survival signals that motivate escape from danger and search for safety, however, they are often evaluated by subjective descriptions only. Studying how humans learn and adapt to pain and relief allows objective investigation of the information processing and neural circuitry underlying these internal experiences. My research set out to use computational learning models to provide mechanistic explanations for the behavioural and functional neuroimaging data collected in pain/relief learning experiments with independent groups of healthy human participants. With a Pavlovian acute pain conditioning task in Experiment 1, I found that 'associability' (a form of uncertainty signal) had a crucial role in controlling the learning rates of different conditioned responses, and can be used to anatomically dissociate underlying neural systems. Experiment 2 focused on relief learning of terminating a tonic pain stimulus, in which the priority for relief-seeking is in conflict with the general suppression of cognition and attention. I showed that associability during active learning not only controls the relief learning rate, but also correlates with endogenously modulated (reduced) ongoing pain. This finding was confirmed in Experiment 3 using an independent active relief learning paradigm in a complex dynamic environment. Critically, both experiments showed that associability was correlated with responses in the pregenual anterior cingulate cortex (pgACC), a brain region previously implicated in aspects of endogenous pain control related to attention and controllability. This provided a potential computational account of an information-sensitive endogenous analgesic mechanism. In Experiment 4, I explored the implications of endogenous controllability for technology-based pain therapeutics. I designed an adaptive closed-loop system that learned to control pain stimulation using decoded real-time pain representations from the brain. Subjects were shown to actively enhance the discriminability of pain only in the pgACC, and uncertainty during learning again correlated with endogenously modulated pain and were associated with pgACC responses. Together, these studies (i) show the importance of uncertainty in controlling learning during both acute and tonic pain, (ii) describe how uncertainty also flexibly modulates pain to maximise the impact of learning, (iii) illustrate a central role for the pgACC in this process, and (iv) reveal the implications for future technology-based therapeutic systems.
APA, Harvard, Vancouver, ISO, and other styles
8

JUBIEN, Guillaume. "Decoding Electrocorticography Signals by Deep Learning for Brain-Computer Interface." Thesis, KTH, Skolan för kemi, bioteknologi och hälsa (CBH), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-243903.

Full text
Abstract:
Brain-Computer Interface (BCI) offers the opportunity to paralyzed patients to control their movements without any neuromuscular activity. Signal processing of neuronal activity enables to decode movement intentions. Ability for patient to control an effector is closely linked to this decoding performance. In this study, I tackle a recent way to decode neuronal activity: Deep learning. The study is based on public data extracted by Schalk et al. for BCI Competition IV. Electrocorticogram (ECoG) data from three epileptic patients were recorded. During the experiment setup, the team asked subjects to move their fingers and recorded finger movements thanks to a data glove. An artificial neural network (ANN) was built based on a common BCI feature extraction pipeline made of successive convolutional layers. This network firstly mimics a spatial filtering with a spatial reduction of sources. Then, it realizes a time-frequency analysis and performs a log power extraction of the band-pass filtered signals. The first investigation was on the optimization of the network. Then, the same architecture was used on each subject and the decoding performances were computed for a 6-class classification. I especially investigated the spatial and temporal filtering. Finally, a preliminary study was conducted on prediction of finger movement. This study demonstrated that deep learning could be an effective way to decode brain signal. For 6-class classification, results stressed similar performances as traditional decoding algorithm. As spatial or temporal weights after training are slightly described in the literature, we especially worked on interpretation of weights after training. The spatial weight study demonstrated that the network is able to select specific ECoG channels notified in the literature as the most informative. Moreover, the network is able to converge to the same spatial solution, independently to the initialization. Finally, a preliminary study was conducted on prediction of movement position and gives encouraging results.
APA, Harvard, Vancouver, ISO, and other styles
9

Mayor, Torres Juan Manuel. "Modeling Heart and Brain signals in the context of Wellbeing and Autism Applications: A Deep Learning Approach." Doctoral thesis, Università degli studi di Trento, 2020. http://hdl.handle.net/11572/247209.

Full text
Abstract:
The analysis and understanding of physiological and brain signals is critical in order to decode user’s behavioral/neural outcome measures in different domain scenarios. Personal Health-Care agents have been proposed recently in order to monitor and acquire reliable data from daily activities to enhance control participants’ wellbeing, and the quality of life of multiple non-neurotypical participants in clinical lab-controlled studies. The inclusion of new wearable devices with increased and more compact memory requirements,and the possibility to include long-size datasets on the cloud and network-based applications agile the implementation of new improved computational health-care agents. These new enhanced agents are able to provide services including real time health-care,medical monitoring, and multiple biological outcome measures-based alarms for medicaldoctor diagnosis. In this dissertation we will focus on multiple Signal Processing (SP), Machine Learning (ML), Saliency Relevance Maps (SRM) techniques and classifiers with the purpose to enhance the Personal Health-care agents in a multimodal clinical environment. Therefore, we propose the evaluation of current state-of-the-art methods to evaluate the incidence of successful hypertension detection, categorical and emotion stimuli decoding using biosignals. To evaluate the performance of ML, SP, and SRM techniques proposed in this study, wedivide this thesis document in two main implementations: 1) Four different initial pipelines where we evaluate the SP, and ML methodologies included here for an enhanced a) Hypertension detection based on Blood-Volume-Pulse signal (BVP) and Photoplethysmography (PPG) wearable sensors, b) Heart-Rate (HR) and Inter-beat-interval (IBI) prediction using light adaptive filtering for physical exercise/real environments, c) Object Category stimuli decoding using EEG features and features subspace transformations, and d) Emotion recognition using EEG features from recognized datasets. And 2) A complete performance and robust SRM evaluation of a neural-based Emotion Decoding/Recognition pipeline using EEG features from Autism Spectrum Disorder (ASD) groups. This pipeline is presented as a novel assistive system for lab-controlled Face Emotion Recognition (FER) intervention ASD subjects. In this pipeline we include a Deep ConvNet asthe Deep classifier to extract the correct neural information and decode emotions successfully.
APA, Harvard, Vancouver, ISO, and other styles
10

Meyers, Ethan M. "Using neural population decoding to understand high level visual processing." Thesis, Massachusetts Institute of Technology, 2011. http://hdl.handle.net/1721.1/62718.

Full text
Abstract:
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Brain and Cognitive Sciences, 2011.
Cataloged from PDF version of thesis.
Includes bibliographical references.
The field of neuroscience has the potential to address profound questions including explaining how neural activity enables complex behaviors and conscious experience. However, currently the field is a long way from understanding these issues, and progress has been slow. One of the main problems holding back the pace of discovery is that it is still unclear how to interpret neural activity once it has been recorded. This lack of understanding has led to many different data analysis methods, which makes it difficult to evaluate the validity and importance of many reported results. If a clearer understanding of how to interpret neural data existed, it should be much easier to answer other questions about how the brain functions. In this thesis I describe how to use a data analysis method called 'neural population decoding' to analyze data in a way that is potentially more relevant for understanding neural information processing. By applying this method in novel ways to data from several vision experiments, I am able to make several new discoveries, including the fact that abstract category information is coded in the inferior temporal cortex (ITC) and prefrontal cortex (PFC) by dynamic patterns of neural activity, and that when a monkey attends to an object in a cluttered display, the pattern of ITC activity returns to a state that is similar to when the attended object is presented alone. These findings are not only interesting for insights that they give into the content and coding of information in high level visual areas, but they also demonstrate the benefits of using neural population decoding to analyze data. Thus, the methods developed in this thesis should enable more rapid progress toward an algorithmic level understanding of vision and information processing in other neural systems.
by Ethan M. Meyers.
Ph.D.
APA, Harvard, Vancouver, ISO, and other styles
More sources

Books on the topic "Brain decoding"

1

Brian Friel: Decoding the language of the tribe. Dublin, Ireland: Liffey Press, 2008.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
2

Corbett, Tony. Brian Friel: Decoding the language of the tribe. Dublin: Liffey Press, 2002.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
3

D, Michael Polsky M. Decoding the Brain. Biblio Books, 2003.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
4

Haynes, John-Dylan. Brain Reading: Decoding Mental States From Brain Activity In Humans. Oxford University Press, 2011. http://dx.doi.org/10.1093/oxfordhb/9780199570706.013.0013.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Calhoun, Vince, and Tulay Adali. Brain Image Analysis Using Blind Source Separation: Decoding and Explaining the Brain. Elsevier Science & Technology Books, 2021.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
6

Niedenthal, Paula M., Adrienne Wood, Magdalena Rychlowska, and Sebastian Korb. Embodied Simulation in Decoding Facial Expression. Oxford University Press, 2017. http://dx.doi.org/10.1093/acprof:oso/9780190613501.003.0021.

Full text
Abstract:
The present chapter explores evidence for the role of embodied simulation and facial mimicry in the decoding of facial expression of emotion. We begin the chapter by reviewing evidence in favor of the hypothesis that mimicking a perceived facial expression helps the perceiver achieve greater decoding accuracy. We report experimental and correlational evidence in favor of the general effect, and we also examine the assertion that facial mimicry influences perceptual processing of facial expression. Finally, after examining the behavioral evidence, we look into the brain to explore the neural circuitry and chemistry involved in embodied simulation of facial expressions of emotion.
APA, Harvard, Vancouver, ISO, and other styles
7

A Discriminative Approach to Bayesian Filtering with Applications to Human Neural Decoding. Providence, USA: Brown University, 2019.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
8

Ott, Walter. The Early Descartes. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198791713.003.0002.

Full text
Abstract:
Descartes’s earliest theory of perception attempts to marry the remnants of the Baconian and Aristotelian views while divorcing them from hylomorphism and the innocent view of sensible qualities. Descartes holds the ‘overlap thesis,’ the claim that any behavior exhibited by non-human animals and inattentive humans must receive the same explanation. Corporeal perception requires the presence of a brain image that resembles its object. When the mind attends to its environment, it is immediately aware of this brain image and, through it, of the common sensibles. The claim that the mind ‘turns toward’ the brain is a thoroughly traditional one. The proper sensibles are summoned by the mind on the occasion of its undergoing certain brain events. Descartes thinks of the mind as ‘decoding’ the language of the brain in order to provide itself with the appropriate sensations. But those sensations do nothing to explain our awareness of objects.
APA, Harvard, Vancouver, ISO, and other styles
9

Seife, Charles. Decoding the Universe: How the New Science of Information Is Explaining Everything in the Cosmos, from Our Brains to Black Holes. Viking Adult, 2006.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
10

Seife, Charles. Decoding the Universe: How the New Science of Information Is Explaining Everything in the Cosmos, from Our Brains to Black Holes. Penguin (Non-Classics), 2007.

Find full text
APA, Harvard, Vancouver, ISO, and other styles

Book chapters on the topic "Brain decoding"

1

Dell’Arciprete, Lorenzo, Brian Murphy, and Fabio Massimo Zanzotto. "Parallels between Machine and Brain Decoding." In Brain Informatics, 162–74. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-35139-6_16.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Brockmeier, Austin J., and José C. Príncipe. "Decoding Algorithms for Brain–Machine Interfaces." In Neural Engineering, 223–57. Boston, MA: Springer US, 2012. http://dx.doi.org/10.1007/978-1-4614-5227-0_4.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Schrouff, Jessica, and Christophe L. M. Phillips. "Multivariate Pattern Recognition Analysis: Brain Decoding." In Coma and Disorders of Consciousness, 35–43. London: Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-2440-5_4.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Croteau, Nicole, Farouk S. Nathoo, Jiguo Cao, and Ryan Budney. "High-Dimensional Classification for Brain Decoding." In Contributions to Statistics, 305–24. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-41573-4_15.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

De Martino, Federico, Cheryl Olman, and Giancarlo Valente. "Information Decoding from fMRI Images." In fMRI: From Nuclear Spins to Brain Functions, 661–97. Boston, MA: Springer US, 2015. http://dx.doi.org/10.1007/978-1-4899-7591-1_23.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Fernández-Irigoyen, Joaquín, and Enrique Santamaría. "Brain Proteomics: Decoding Neuroproteomes Using Mass Spectrometry." In Neuromethods, 3–7. New York, NY: Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4939-7119-0_1.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Kanas, Vasileios G., Iosif Mporas, Griffin W. Milsap, Kyriakos N. Sgarbas, Nathan E. Crone, and Anastasios Bezerianos. "Time-Varying Parametric Modeling of ECoG for Syllable Decoding." In Brain Informatics and Health, 222–31. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23344-4_22.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Sasane, Sudhir, and Lars Schwabe. "Decoding of EEG Activity from Object Views: Active Detection vs. Passive Visual Tasks." In Brain Informatics, 277–87. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-35139-6_26.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Owen, Adrian M., and Lorina Naci. "Decoding Thoughts in Disorders of Consciousness." In Brain Function and Responsiveness in Disorders of Consciousness, 67–80. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-21425-2_6.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Dash, Debadatta, Paul Ferrari, Saleem Malik, Albert Montillo, Joseph A. Maldjian, and Jun Wang. "Determining the Optimal Number of MEG Trials: A Machine Learning and Speech Decoding Perspective." In Brain Informatics, 163–72. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-05587-5_16.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "Brain decoding"

1

Alkan, Sarper, and Fatos T. Yarman-Vural. "Ensembling brain regions for brain decoding." In 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2015. http://dx.doi.org/10.1109/embc.2015.7319010.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Porbadnigk, Anne K., Matthias S. Treder, Siamac Fazli, Michael Tangermann, Carmen Vidaurre, Stefan Haufe, Gabriel Curio, Benjamin Blankertz, and Klaus-Robert Miiller. "Decoding cognitive brain states." In 2013 International Winter Workshop on Brain-Computer Interface (BCI). IEEE, 2013. http://dx.doi.org/10.1109/iww-bci.2013.6506613.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Olivetti, Emanuele, Andrea Mognon, Susanne Greiner, and Paolo Avesani. "Brain Decoding: Biases in Error Estimation." In 2010 First Workshop on Brain Decoding: Pattern Recognition Challenges in Neuroimaging (WBD). IEEE, 2010. http://dx.doi.org/10.1109/wbd.2010.9.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Rahmani, Honeye, S. Mahmoud Taheri, and Elahe Yargholi. "From Brain Decoding To Brain-Driven Computer Vision." In 2020 International Conference on Machine Vision and Image Processing (MVIP). IEEE, 2020. http://dx.doi.org/10.1109/mvip49855.2020.9116906.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Vega-Pons, Sandro, and Paolo Avesani. "Brain Decoding via Graph Kernels." In 2013 International Workshop on Pattern Recognition in Neuroimaging (PRNI). IEEE, 2013. http://dx.doi.org/10.1109/prni.2013.43.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Kefayati, Mohammad Hadi, Hamid Sheikhzadeh, Hamid R. Rabiee, and Ali Soltani-Farani. "Semi-spatiotemporal fMRI Brain Decoding." In 2013 International Workshop on Pattern Recognition in Neuroimaging (PRNI). IEEE, 2013. http://dx.doi.org/10.1109/prni.2013.54.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Firat, Orhan, Like Oztekin, and Fatos T. Yarman Vural. "Deep learning for brain decoding." In 2014 IEEE International Conference on Image Processing (ICIP). IEEE, 2014. http://dx.doi.org/10.1109/icip.2014.7025563.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Tripathy, Kalyan, Andrew Fishell, Zachary Markow, Tracy M. Burns-Yocum, Dillan J. Newbold, Pooja Tripathy, Bradley L. Schlaggar, and Joseph P. Culver. "Decoding Visual Information from High Density Diffuse Optical Tomography Neuroimaging Data." In Optics and the Brain. Washington, D.C.: OSA, 2018. http://dx.doi.org/10.1364/brain.2018.btu4c.3.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Jiao, Zhicheng, Haoxuan You, Fan Yang, Xin Li, Han Zhang, and Dinggang Shen. "Decoding EEG by Visual-guided Deep Neural Networks." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/192.

Full text
Abstract:
Decoding visual stimuli from brain activities is an interdisciplinary study of neuroscience and computer vision. With the emerging of Human-AI Collaboration, Human-Computer Interaction, and the development of advanced machine learning models, brain decoding based on deep learning attracts more attention. Electroencephalogram (EEG) is a widely used neurophysiology tool. Inspired by the success of deep learning on image representation and neural decoding, we proposed a visual-guided EEG decoding method that contains a decoding stage and a generation stage. In the classification stage, we designed a visual-guided convolutional neural network (CNN) to obtain more discriminative representations from EEG, which are applied to achieve the classification results. In the generation stage, the visual-guided EEG features are input to our improved deep generative model with a visual consistence module to generate corresponding visual stimuli. With the help of our visual-guided strategies, the proposed method outperforms traditional machine learning methods and deep learning models in the EEG decoding task.
APA, Harvard, Vancouver, ISO, and other styles
10

Anders, Silke, John-Dylan Haynes, and Thomas Ethofer. "Decoding Inter-individual Relations from Spatial Similarity of Brain Activity." In 2010 First Workshop on Brain Decoding: Pattern Recognition Challenges in Neuroimaging (WBD). IEEE, 2010. http://dx.doi.org/10.1109/wbd.2010.18.

Full text
APA, Harvard, Vancouver, ISO, and other styles
You might also be interested in the extended bibliographies on the topic 'Brain decoding' for particular source types:
Journal articles Dissertations / Theses
We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!

To the bibliography