Journal articles: '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.

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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.

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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.

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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.

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

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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.

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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.

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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.

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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.

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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.

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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.

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Oizumi, M., T. Ishii, K. Ishibashi, T. Hosoya, and M. Okada. "Mismatched Decoding in the Brain." Journal of Neuroscience 30, no. 13 (March 31, 2010): 4815–26. http://dx.doi.org/10.1523/jneurosci.4360-09.2010.

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Bidelman, Gavin M., Caitlin N. Price, Md Sultan Mahmud, and Mohammed Yeasin. "Decoding Hearing Loss From Brain Signals." Hearing Journal 73, no. 11 (November 2020): 42,44,45. http://dx.doi.org/10.1097/01.hj.0000722524.69484.01.

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13

Flamary, R., N. Jrad, R. Phlypo, M. Congedo, and A. Rakotomamonjy. "Mixed-Norm Regularization for Brain Decoding." Computational and Mathematical Methods in Medicine 2014 (2014): 1–13. http://dx.doi.org/10.1155/2014/317056.

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This work investigates the use of mixed-norm regularization for sensor selection in event-related potential (ERP) based brain-computer interfaces (BCI). The classification problem is cast as a discriminative optimization framework where sensor selection is induced through the use of mixed-norms. This framework is extended to the multitask learning situation where several similar classification tasks related to different subjects are learned simultaneously. In this case, multitask learning helps in leveraging data scarcity issue yielding to more robust classifiers. For this purpose, we have introduced a regularizer that induces both sensor selection and classifier similarities. The different regularization approaches are compared on three ERP datasets showing the interest of mixed-norm regularization in terms of sensor selection. The multitask approaches are evaluated when a small number of learning examples are available yielding to significant performance improvements especially for subjects performing poorly.

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Parra, Lucas, Christoforos Christoforou, Adam Gerson, Mads Dyrholm, An Luo, Mark Wagner, Marios Philiastides, and Paul Sajda. "Spatiotemporal Linear Decoding of Brain State." IEEE Signal Processing Magazine 25, no. 1 (2008): 107–15. http://dx.doi.org/10.1109/msp.2008.4408447.

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Tong, Frank, and Michael S. Pratte. "Decoding Patterns of Human Brain Activity." Annual Review of Psychology 63, no. 1 (January 10, 2012): 483–509. http://dx.doi.org/10.1146/annurev-psych-120710-100412.

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Janoos, Firdaus, Raghu Machiraju, and Istvan A. Morocz. "Decoding brain states from fMRI data." International Journal of Psychophysiology 77, no. 3 (September 2010): 322–23. http://dx.doi.org/10.1016/j.ijpsycho.2010.06.244.

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17

Martens, S. M. M., J. M. Mooij, N. J. Hill, J. Farquhar, and B. Schölkopf. "A Graphical Model Framework for Decoding in the Visual ERP-Based BCI Speller." Neural Computation 23, no. 1 (January 2011): 160–82. http://dx.doi.org/10.1162/neco_a_00066.

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We present a graphical model framework for decoding in the visual ERP-based speller system. The proposed framework allows researchers to build generative models from which the decoding rules are obtained in a straightforward manner. We suggest two models for generating brain signals conditioned on the stimulus events. Both models incorporate letter frequency information but assume different dependencies between brain signals and stimulus events. For both models, we derive decoding rules and perform a discriminative training. We show on real visual speller data how decoding performance improves by incorporating letter frequency information and using a more realistic graphical model for the dependencies between the brain signals and the stimulus events. Furthermore, we discuss how the standard approach to decoding can be seen as a special case of the graphical model framework. The letter also gives more insight into the discriminative approach for decoding in the visual speller system.

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Gibson, S., J. W. Judy, and D. Markovic. "Spike Sorting: The First Step in Decoding the Brain: The first step in decoding the brain." IEEE Signal Processing Magazine 29, no. 1 (January 2012): 124–43. http://dx.doi.org/10.1109/msp.2011.941880.

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19

Sun, Jingyuan, Shaonan Wang, Jiajun Zhang, and Chengqing Zong. "Towards Sentence-Level Brain Decoding with Distributed Representations." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 7047–54. http://dx.doi.org/10.1609/aaai.v33i01.33017047.

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Decoding human brain activities based on linguistic representations has been actively studied in recent years. However, most previous studies exclusively focus on word-level representations, and little is learned about decoding whole sentences from brain activation patterns. This work is our effort to mend the gap. In this paper, we build decoders to associate brain activities with sentence stimulus via distributed representations, the currently dominant sentence representation approach in natural language processing (NLP). We carry out a systematic evaluation, covering both widely-used baselines and state-of-the-art sentence representation models. We demonstrate how well different types of sentence representations decode the brain activation patterns and give empirical explanations of the performance difference. Moreover, to explore how sentences are neurally represented in the brain, we further compare the sentence representation’s correspondence to different brain areas associated with high-level cognitive functions. We find the supervised structured representation models most accurately probe the language atlas of human brain. To the best of our knowledge, this work is the first comprehensive evaluation of distributed sentence representations for brain decoding. We hope this work can contribute to decoding brain activities with NLP representation models, and understanding how linguistic items are neurally represented.

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20

Grootswagers, Tijl, Susan G. Wardle, and Thomas A. Carlson. "Decoding Dynamic Brain Patterns from Evoked Responses: A Tutorial on Multivariate Pattern Analysis Applied to Time Series Neuroimaging Data." Journal of Cognitive Neuroscience 29, no. 4 (April 2017): 677–97. http://dx.doi.org/10.1162/jocn_a_01068.

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Multivariate pattern analysis (MVPA) or brain decoding methods have become standard practice in analyzing fMRI data. Although decoding methods have been extensively applied in brain–computer interfaces, these methods have only recently been applied to time series neuroimaging data such as MEG and EEG to address experimental questions in cognitive neuroscience. In a tutorial style review, we describe a broad set of options to inform future time series decoding studies from a cognitive neuroscience perspective. Using example MEG data, we illustrate the effects that different options in the decoding analysis pipeline can have on experimental results where the aim is to “decode” different perceptual stimuli or cognitive states over time from dynamic brain activation patterns. We show that decisions made at both preprocessing (e.g., dimensionality reduction, subsampling, trial averaging) and decoding (e.g., classifier selection, cross-validation design) stages of the analysis can significantly affect the results. In addition to standard decoding, we describe extensions to MVPA for time-varying neuroimaging data including representational similarity analysis, temporal generalization, and the interpretation of classifier weight maps. Finally, we outline important caveats in the design and interpretation of time series decoding experiments.

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21

Smith, Marie L., Garrison W. Cottrell, FrédéAric Gosselin, and Philippe G. Schyns. "Transmitting and Decoding Facial Expressions." Psychological Science 16, no. 3 (March 2005): 184–89. http://dx.doi.org/10.1111/j.0956-7976.2005.00801.x.

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This article examines the human face as a transmitter of expression signals and the brain as a decoder of these expression signals. If the face has evolved to optimize transmission of such signals, the basic facial expressions should have minimal overlap in their information. If the brain has evolved to optimize categorization of expressions, it should be efficient with the information available from the transmitter for the task. In this article, we characterize the information underlying the recognition of the six basic facial expression signals and evaluate how efficiently each expression is decoded by the underlying brain structures.

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22

de Brecht, Matthew, and Noriko Yamagishi. "Decoding brain activity with smooth sparse regression." Neuroscience Research 71 (September 2011): e201-e202. http://dx.doi.org/10.1016/j.neures.2011.07.872.

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23

Akhtar, Javed Iqbal. "Digital Decoding of Pediatric Traumatic Brain Injury*." Critical Care Medicine 43, no. 3 (March 2015): 722–23. http://dx.doi.org/10.1097/ccm.0000000000000799.

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24

Richiardi, Jonas, Hamdi Eryilmaz, Sophie Schwartz, Patrik Vuilleumier, and Dimitri Van De Ville. "Decoding brain states from fMRI connectivity graphs." NeuroImage 56, no. 2 (May 2011): 616–26. http://dx.doi.org/10.1016/j.neuroimage.2010.05.081.

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25

LaConte, Stephen M. "Decoding fMRI brain states in real-time." NeuroImage 56, no. 2 (May 2011): 440–54. http://dx.doi.org/10.1016/j.neuroimage.2010.06.052.

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26

Haynes, John-Dylan. "Decoding visual consciousness from human brain signals." Trends in Cognitive Sciences 13, no. 5 (May 2009): 194–202. http://dx.doi.org/10.1016/j.tics.2009.02.004.

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27

Taschereau-Dumouchel, Vincent, and Mathieu Roy. "Could Brain Decoding Machines Change Our Minds?" Trends in Cognitive Sciences 24, no. 11 (November 2020): 856–58. http://dx.doi.org/10.1016/j.tics.2020.09.006.

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28

Robinson, Richard. "Real-Time Speech Decoding from Brain Waves." Neurology Today 19, no. 18 (September 2019): 36–37. http://dx.doi.org/10.1097/01.nt.0000584108.45378.1b.

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29

Awangga, R. M., T. L. R. Mengko, and N. P. Utama. "A literature review of brain decoding research." IOP Conference Series: Materials Science and Engineering 830 (May 19, 2020): 032049. http://dx.doi.org/10.1088/1757-899x/830/3/032049.

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30

Curtin, Judith A. "Visual Cues, Decoding, Literacy, and the Brain." Perspectives on Hearing and Hearing Disorders in Childhood 17, no. 2 (September 2007): 9–12. http://dx.doi.org/10.1044/hhdc17.2.9.

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31

Zhang, Chenyu, and Dihua Yu. "Advances in decoding breast cancer brain metastasis." Cancer and Metastasis Reviews 35, no. 4 (November 21, 2016): 677–84. http://dx.doi.org/10.1007/s10555-016-9638-9.

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32

Zheng, Xiao, Wanzhong Chen, Mingyang Li, Tao Zhang, Yang You, and Yun Jiang. "Decoding human brain activity with deep learning." Biomedical Signal Processing and Control 56 (February 2020): 101730. http://dx.doi.org/10.1016/j.bspc.2019.101730.

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33

Yoo, Yongseok, and Woori Kim. "On Decoding Grid Cell Population Codes Using Approximate Belief Propagation." Neural Computation 29, no. 3 (March 2017): 716–34. http://dx.doi.org/10.1162/neco_a_00902.

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Neural systems are inherently noisy. One well-studied example of a noise reduction mechanism in the brain is the population code, where representing a variable with multiple neurons allows the encoded variable to be recovered with fewer errors. Studies have assumed ideal observer models for decoding population codes, and the manner in which information in the neural population can be retrieved remains elusive. This letter addresses a mechanism by which realistic neural circuits can recover encoded variables. Specifically, the decoding problem of recovering a spatial location from populations of grid cells is studied using belief propagation. We extend the belief propagation decoding algorithm in two aspects. First, beliefs are approximated rather than being calculated exactly. Second, decoding noises are introduced into the decoding circuits. Numerical simulations demonstrate that beliefs can be effectively approximated by combining polynomial nonlinearities with divisive normalization. This approximate belief propagation algorithm is tolerant to decoding noises. Thus, this letter presents a realistic model for decoding neural population codes and investigates fault-tolerant information retrieval mechanisms in the brain.

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34

Islam, Mohammad S., Khondaker A. Mamun, and Hai Deng. "Decoding of Human Movements Based on Deep Brain Local Field Potentials Using Ensemble Neural Networks." Computational Intelligence and Neuroscience 2017 (2017): 1–16. http://dx.doi.org/10.1155/2017/5151895.

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Decoding neural activities related to voluntary and involuntary movements is fundamental to understanding human brain motor circuits and neuromotor disorders and can lead to the development of neuromotor prosthetic devices for neurorehabilitation. This study explores using recorded deep brain local field potentials (LFPs) for robust movement decoding of Parkinson’s disease (PD) and Dystonia patients. The LFP data from voluntary movement activities such as left and right hand index finger clicking were recorded from patients who underwent surgeries for implantation of deep brain stimulation electrodes. Movement-related LFP signal features were extracted by computing instantaneous power related to motor response in different neural frequency bands. An innovative neural network ensemble classifier has been proposed and developed for accurate prediction of finger movement and its forthcoming laterality. The ensemble classifier contains three base neural network classifiers, namely, feedforward, radial basis, and probabilistic neural networks. The majority voting rule is used to fuse the decisions of the three base classifiers to generate the final decision of the ensemble classifier. The overall decoding performance reaches a level of agreement (kappa value) at about0.729±0.16for decoding movement from the resting state and about0.671±0.14for decoding left and right visually cued movements.

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MANYAKOV, NIKOLAY V., NIKOLAY CHUMERIN, and MARC M. VAN HULLE. "MULTICHANNEL DECODING FOR PHASE-CODED SSVEP BRAIN–COMPUTER INTERFACE." International Journal of Neural Systems 22, no. 05 (September 26, 2012): 1250022. http://dx.doi.org/10.1142/s0129065712500220.

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We propose a complex-valued multilayer feedforward neural network classifier for decoding of phase-coded information from steady-state visual evoked potentials. To optimize the performance of the classifier we supply it with two filter-based feature selection strategies. The proposed approaches could be used for a phase-coded brain–computer interface, enabling to encode several targets using only one stimulation frequency. The proposed classifier is a multichannel one, which distinguishes our approach from the existing single-channel ones. We show that the proposed approach outperforms others in terms of accuracy and length of the data segments used for decoding. We show that the decoding based on one optimally selected channel yields an inferior performance compared to the one based on several features, which supports our argument for a multichannel approach.

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36

Saminu, Sani, Guizhi Xu, Zhang Shuai, Abd El Kader Isselmou, Adamu Halilu Jabire, Ibrahim Abdullahi Karaye, Isah Salim Ahmad, and Abubakar Abdulkarim. "Electroencephalogram (EEG) Based Imagined Speech Decoding and Recognition." Journal of Applied Materials and Technology 2, no. 2 (June 7, 2021): 74–84. http://dx.doi.org/10.31258/jamt.2.2.74-84.

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The recent investigations and advances in imagined speech decoding and recognition has tremendously improved the decoding of speech directly from brain activity with the help of several neuroimaging techniques that assist us in exploring the neurological processes of imagined speech. This development leads to assist people with disabilities to benefit from neuroprosthetic devices that improve the life of those suffering from neurological disorders. This paper presents the summary of recent progress in decoding imagined speech using Electroenceplography (EEG) signal, as this neuroimaging method enable us to monitor brain activity with high temporal resolution, it is very portable, low cost, and safer as compared to other methods. Therefore, it is a good candidate in investigating an imagined speech decoding from the human cortex which remains a challenging task. The paper also reviews some recent techniques, challenges, future recommendations and possible solutions to improve prosthetic devices and the development of brain computer interface system (BCI).

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37

Laurent, Raphaël, Clément Moulin-Frier, Pierre Bessière, Jean-Luc Schwartz, and Julien Diard. "Integrate, yes, but what and how? A computational approach of sensorimotor fusion in speech." Behavioral and Brain Sciences 36, no. 4 (June 24, 2013): 364–65. http://dx.doi.org/10.1017/s0140525x12002634.

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AbstractWe consider a computational model comparing the possible roles of “association” and “simulation” in phonetic decoding, demonstrating that these two routes can contain similar information in some “perfect” communication situations and highlighting situations where their decoding performance differs. We conclude that optimal decoding should involve some sort of fusion of association and simulation in the human brain.

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38

Saengpetch, Piyawat, Luepol Pipanmemekaporn, and Suwatchai Kamolsantiroj. "Functional magnetic resonance imaging-based brain decoding with visual semantic model." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 6 (December 1, 2020): 6682. http://dx.doi.org/10.11591/ijece.v10i6.pp6682-6690.

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The activity pattern of the brain has been activated to identify a person in mind. Using the function magnetic resonance imaging (fMRI) to decipher brain decoding is the most accepted method. However, the accuracy of fMRI-based brain decoder is still restricted due to limited training samples. The limitations of the brain decoder using fMRI are passed through the design features proposed for many label coding and model training to predict these characteristics for a particular label. Moreover, what kind of semantic features for deciphering the neurological activity patterns are unclear. In current work, a new calculation model for learning decoding labels that is consistent with fMRI activity responses. The approach demonstrates the proposed corresponding label's success in terms of accuracy, which is decoded from brain activity patterns and compared with conventional text-derived feature technique. Besides, experimental studies present a training model based on multi-tasking to reduce the problems of limited training data sets. Therefore, the multi-task learning model is more efficient than modern methods of calculation, and decoding features may be easily obtained.

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39

Cao, Benchun, Yanchun Liang, Shinichi Yoshida, and Renchu Guan. "Facial Expression Decoding based on fMRI Brain Signal." International Journal of Computers Communications & Control 14, no. 4 (August 5, 2019): 475–88. http://dx.doi.org/10.15837/ijccc.2019.4.3433.

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The analysis of facial expressions is a hot topic in brain-computer interface research. To determine the facial expressions of the subjects under the corresponding stimulation, we analyze the fMRI images acquired by the Magnetic Resonance. There are six kinds of facial expressions: "anger", "disgust", "sadness", "happiness", "joy" and "surprise". We demonstrate that brain decoding is achievable through the parsing of two facial expressions ("anger" and "joy"). Support vector machine and extreme learning machine are selected to classify these expressions based on time series features. Experimental results show that the classification performance of the extreme learning machine algorithm is better than support vector machine. Among the eight participants in the trials, the classification accuracy of three subjects reached 70-80%, and the remaining five subjects also achieved accuracy of 50-60%. Therefore, we can conclude that the brain decoding can be used to help analyzing human facial expressions.

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40

Wang, Guang-Zhong, and Genevieve Konopka. "Decoding human gene expression signatures in the brain." Transcription 4, no. 3 (May 2013): 102–8. http://dx.doi.org/10.4161/trns.24885.

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41

HRUBY, GEORGE G., and GEORGE W. HYND. "Decoding Shaywitz: The modular brain and its discontents." Reading Research Quarterly 41, no. 4 (October 12, 2006): 544–56. http://dx.doi.org/10.1598/rrq.41.4.7.

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42

Van De Ville, Dimitri, and Seong-Whan Lee. "Brain decoding: Opportunities and challenges for pattern recognition." Pattern Recognition 45, no. 6 (June 2012): 2033–34. http://dx.doi.org/10.1016/j.patcog.2011.06.001.

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43

Doyle, O. M., J. Ashburner, F. O. Zelaya, S. C. R. Williams, M. A. Mehta, and A. F. Marquand. "Multivariate decoding of brain images using ordinal regression." NeuroImage 81 (November 2013): 347–57. http://dx.doi.org/10.1016/j.neuroimage.2013.05.036.

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Schwab, Simon, Andrea Federspiel, Yosuke Morishima, Masahito Nakataki, Werner Strik, Roland Wiest, Markus Heinrichs, Dominique de Quervain, and Leila M. Soravia. "Glucocorticoids and cortical decoding in the phobic brain." Psychiatry Research: Neuroimaging 300 (June 2020): 111066. http://dx.doi.org/10.1016/j.pscychresns.2020.111066.

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45

Heinzle, J., and JD Haynes. "Decoding the information flow between visual brain regions." NeuroImage 47 (July 2009): S57. http://dx.doi.org/10.1016/s1053-8119(09)70218-6.

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46

Kragel, Philip A., and Kevin S. LaBar. "Decoding the Nature of Emotion in the Brain." Trends in Cognitive Sciences 20, no. 6 (June 2016): 444–55. http://dx.doi.org/10.1016/j.tics.2016.03.011.

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47

de Cheveigné, Alain, Daniel D. E. Wong, Giovanni M. Di Liberto, Jens Hjortkjær, Malcolm Slaney, and Edmund Lalor. "Decoding the auditory brain with canonical component analysis." NeuroImage 172 (May 2018): 206–16. http://dx.doi.org/10.1016/j.neuroimage.2018.01.033.

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48

Spiers, Hugo J., and Eleanor A. Maguire. "Decoding human brain activity during real-world experiences." Trends in Cognitive Sciences 11, no. 8 (August 2007): 356–65. http://dx.doi.org/10.1016/j.tics.2007.06.002.

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49

Haynes, John-Dylan, and Geraint Rees. "Decoding mental states from brain activity in humans." Nature Reviews Neuroscience 7, no. 7 (July 2006): 523–34. http://dx.doi.org/10.1038/nrn1931.

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Speer, Sebastian P. H., and Maarten A. S. Boksem. "Decoding fairness motivations from multivariate brain activity patterns." Social Cognitive and Affective Neuroscience 14, no. 11 (November 1, 2019): 1197–207. http://dx.doi.org/10.1093/scan/nsz097.

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Abstract A preference for fairness may originate from prosocial or strategic motivations: we may wish to improve others’ well-being or avoid the repercussions of selfish behavior. Here, we used functional magnetic resonance imaging to identify neural patterns that dissociate these two motivations. Participants played both the ultimatum and dictator game (UG–DG) as proposers. Because responders can reject the offer in the UG, but not the DG, offers and neural patterns between the games should differ for strategic players but not prosocial players. Using multivariate pattern analysis, we found that the decoding accuracy of neural patterns associated with UG and DG decisions correlated significantly with differences in offers between games in regions associated with theory of mind (ToM), such as the temporoparietal junction, and cognitive control, such as the dorsolateral prefrontal cortex and inferior frontal cortex. We conclude that individual differences in prosocial behavior may be driven by variations in the degree to which self-control and ToM processes are engaged during decision-making such that the extent to which these processes are engaged is indicative of either selfish or prosocial motivations.

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Journal articles on the topic 'Brain decoding'

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