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4. Chronic pain detection from resting-state raw EEG signals using improved feature selection

Author

Li, Jean, De Ridder, Dirk, Adhia, Divya, Hall, Matthew, and Deng, Jeremiah D.

Subjects
Computer Science - Machine Learning
Abstract

We present an automatic approach that works on resting-state raw EEG data for chronic pain detection. A new feature selection algorithm - modified Sequential Floating Forward Selection (mSFFS) - is proposed. The improved feature selection scheme is rather compact but displays better class separability as indicated by the Bhattacharyya distance measures and better visualization results. It also outperforms selections generated by other benchmark methods, boosting the test accuracy to 97.5% and yielding a test accuracy of 81.4% on an external dataset that contains different types of chronic pain, Comment: 9 pages, 4 figures, journal submission

Published
2023

5. The Bayesian Brain and Tinnitus

Author

De Ridder, Dirk, Vanneste, Sven, Sedley, William, Friston, Karl, Schlee, Winfried, editor, Langguth, Berthold, editor, De Ridder, Dirk, editor, Vanneste, Sven, editor, Kleinjung, Tobias, editor, and Møller, Aage R., editor

Published
2024
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7. Tinnitus Treatment: Evidence and Guidelines

Author

Langguth, Berthold, Kleinjung, Tobias, Schlee, Winfried, Vanneste, Sven, De Ridder, Dirk, Schlee, Winfried, editor, Langguth, Berthold, editor, De Ridder, Dirk, editor, Vanneste, Sven, editor, Kleinjung, Tobias, editor, and Møller, Aage R., editor

Published
2024
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8. Invasive Brain Stimulation

Author

De Ridder, Dirk, Vanneste, Sven, Schlee, Winfried, editor, Langguth, Berthold, editor, De Ridder, Dirk, editor, Vanneste, Sven, editor, Kleinjung, Tobias, editor, and Møller, Aage R., editor

Published
2024
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9. Trauma-Associated Tinnitus

Author

De Ridder, Dirk, Schlee, Winfried, editor, Langguth, Berthold, editor, De Ridder, Dirk, editor, Vanneste, Sven, editor, Kleinjung, Tobias, editor, and Møller, Aage R., editor

Published
2024
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10. Pulsatile Tinnitus

Author

Song, Jae-Jin, De Ridder, Dirk, Schlee, Winfried, editor, Langguth, Berthold, editor, De Ridder, Dirk, editor, Vanneste, Sven, editor, Kleinjung, Tobias, editor, and Møller, Aage R., editor

Published
2024
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12. Tinnitus and Vestibular Schwannoma

Author

De Ridder, Dirk, Kleinjung, Tobias, Schlee, Winfried, editor, Langguth, Berthold, editor, De Ridder, Dirk, editor, Vanneste, Sven, editor, Kleinjung, Tobias, editor, and Møller, Aage R., editor

Published
2024
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13. History of Tinnitus

Author

De Ridder, Dirk, De Ridder-Symoens, Hilde, Schlee, Winfried, editor, Langguth, Berthold, editor, De Ridder, Dirk, editor, Vanneste, Sven, editor, Kleinjung, Tobias, editor, and Møller, Aage R., editor

Published
2024
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15. Similarities Between Tinnitus and Pain

Author

De Ridder, Dirk, Møller, Aage R., Schlee, Winfried, editor, Langguth, Berthold, editor, De Ridder, Dirk, editor, Vanneste, Sven, editor, Kleinjung, Tobias, editor, and Møller, Aage R., editor

Published
2024
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16. Tinnitus and the Somatosensory System

Author

Møller, Aage R., De Ridder, Dirk, Schlee, Winfried, editor, Langguth, Berthold, editor, De Ridder, Dirk, editor, Vanneste, Sven, editor, Kleinjung, Tobias, editor, and Møller, Aage R., editor

Published
2024
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18. Variational Autoencoder Learns Better Feature Representations for EEG-based Obesity Classification

Author

Yue, Yuan, Deng, Jeremiah D., De Ridder, Dirk, Manning, Patrick, and Adhia, Divya

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Neural and Evolutionary Computing
Abstract

Obesity is a common issue in modern societies today that can lead to various diseases and significantly reduced quality of life. Currently, research has been conducted to investigate resting state EEG (electroencephalogram) signals with an aim to identify possible neurological characteristics associated with obesity. In this study, we propose a deep learning-based framework to extract the resting state EEG features for obese and lean subject classification. Specifically, a novel variational autoencoder framework is employed to extract subject-invariant features from the raw EEG signals, which are then classified by a 1-D convolutional neural network. Comparing with conventional machine learning and deep learning methods, we demonstrate the superiority of using VAE for feature extraction, as reflected by the significantly improved classification accuracies, better visualizations and reduced impurity measures in the feature representations. Future work can be directed to gaining an in-depth understanding regarding the spatial patterns that have been learned by the proposed model from a neurological view, as well as improving the interpretability of the proposed model by allowing it to uncover any temporal-related information., Comment: 8 pages, 6 figures

Published
2023

19. Finding neural signatures for obesity through feature selection on source-localized EEG

Author

Yue, Yuan, De Ridder, Dirk, Manning, Patrick, Ross, Samantha, and Deng, Jeremiah D.

Subjects
Computer Science - Machine Learning, Electrical Engineering and Systems Science - Signal Processing, Quantitative Biology - Neurons and Cognition
Abstract

Obesity is a serious issue in the modern society and is often associated to significantly reduced quality of life. Current research conducted to explore obesity-related neurological evidences using electroencephalography (EEG) data are limited to traditional approaches. In this study, we developed a novel machine learning model to identify brain networks of obese females using alpha band functional connectivity features derived from EEG data. An overall classification accuracy of 0.937 is achieved. Our finding suggests that the obese brain is characterized by a dysfunctional network in which the areas that responsible for processing self-referential information and environmental context information are impaired., Comment: 4 pages, 3 figures, conference submission

Published
2022

21. The Neurostimulation Appropriateness Consensus Committee (NACC)®: Recommendations for Spinal Cord Stimulation Long-Term Outcome Optimization and Salvage Therapy

Author

Deer, Timothy R., Russo, Marc, Grider, Jay S., Sayed, Dawood, Lamer, Tim J., Dickerson, David M., Hagedorn, Jonathan M., Petersen, Erika A., Fishman, Michael A., FitzGerald, James, Baranidharan, Ganesan, De Ridder, Dirk, Chakravarthy, Krishnan V., Al-Kaisy, Adnan, Hunter, Corey W., Buchser, Eric, Chapman, Kenneth, Gilligan, Chris, Hayek, Salim M., Thomson, Simon, Strand, Natalie, Jameson, Jessica, Simopoulos, Thomas T., Yang, Ajax, De Coster, Olivier, Cremaschi, Fabián, Christo, Paul J., Varshney, Vishal, Bojanic, Stana, and Levy, Robert M.

Published
2024
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27. Resting-state EEG sex classification using selected brain connectivity representation

Author

Li, Jean, Deng, Jeremiah D., Adhia, Divya, and de Ridder, Dirk

Subjects
Electrical Engineering and Systems Science - Signal Processing, Computer Science - Machine Learning
Abstract

Effective analysis of EEG signals for potential clinical applications remains a challenging task. So far, the analysis and conditioning of EEG have largely remained sex-neutral. This paper employs a machine learning approach to explore the evidence of sex effects on EEG signals, and confirms the generality of these effects by achieving successful sex prediction of resting-state EEG signals. We have found that the brain connectivity represented by the coherence between certain sensor channels are good predictors of sex., Comment: 11 pages, 6 figures, book chapter to be published by Springer

Published
2020

30. Consensus on the reporting and experimental design of clinical and cognitive-behavioural neurofeedback studies (CRED-nf checklist).

Author

Paret, Christian, Ossadtchi, Alexei, Nicholson, Andrew, Nan, Wenya, Minguez, Javier, Micoulaud-Franchi, Jean-Arthur, Mehler, David, Lührs, Michael, Lubar, Joel, Lotte, Fabien, Linden, David, Lewis-Peacock, Jarrod, Lebedev, Mikhail, Lanius, Ruth, Kübler, Andrea, Kranczioch, Cornelia, Koush, Yury, Konicar, Lilian, Kohl, Simon, Kober, Silivia, Klados, Manousos, Jeunet, Camille, Janssen, T, Huster, Rene, Hoedlmoser, Kerstin, Hirshberg, Laurence, Heunis, Stephan, Hendler, Talma, Hampson, Michelle, Guggisberg, Adrian, Guggenberger, Robert, Gruzelier, John, Göbel, Rainer, Gninenko, Nicolas, Gharabaghi, Alireza, Frewen, Paul, Fovet, Thomas, Fernández, Thalía, Escolano, Carlos, Ehlis, Ann-Christine, Drechsler, Renate, Christopher deCharms, R, Debener, Stefan, De Ridder, Dirk, Davelaar, Eddy, Congedo, Marco, Cavazza, Marc, Breteler, Marinus, Brandeis, Daniel, Bodurka, Jerzy, Birbaumer, Niels, Bazanova, Olga, Barth, Beatrix, Bamidis, Panagiotis, Auer, Tibor, Arns, Martijn, Thibault, Robert, Ros, Tomas, Enriquez-Geppert, Stefanie, Zotev, Vadim, Young, Kymberly, Wood, Guilherme, Whitfield-Gabrieli, Susan, Wan, Feng, Vuilleumier, Patrik, Vialatte, François, Van De Ville, Dimitri, Todder, Doron, Surmeli, Tanju, Sulzer, James, Strehl, Ute, Sterman, Maurice, Steiner, Naomi, Sorger, Bettina, Soekadar, Surjo, Sitaram, Ranganatha, Sherlin, Leslie, Schönenberg, Michael, Scharnowski, Frank, Schabus, Manuel, Rubia, Katya, Rosa, Agostinho, Reiner, Miriam, and Pineda, Jaime

Subjects
checklist, consensus, guidelines, neurofeedback, regulation, Adult, Checklist, Consensus, Female, Humans, Male, Middle Aged, Neurofeedback, Peer Review, Research, Research Design, Stakeholder Participation
Abstract

Neurofeedback has begun to attract the attention and scrutiny of the scientific and medical mainstream. Here, neurofeedback researchers present a consensus-derived checklist that aims to improve the reporting and experimental design standards in the field.

Published
2020

31. Consensus on the reporting and experimental design of clinical and cognitive-behavioural neurofeedback studies (CRED-nf checklist)

Author

Ros, Tomas, Enriquez-Geppert, Stefanie, Zotev, Vadim, Young, Kymberly D, Wood, Guilherme, Whitfield-Gabrieli, Susan, Wan, Feng, Vuilleumier, Patrik, Vialatte, François, Van De Ville, Dimitri, Todder, Doron, Surmeli, Tanju, Sulzer, James S, Strehl, Ute, Sterman, Maurice Barry, Steiner, Naomi J, Sorger, Bettina, Soekadar, Surjo R, Sitaram, Ranganatha, Sherlin, Leslie H, Schönenberg, Michael, Scharnowski, Frank, Schabus, Manuel, Rubia, Katya, Rosa, Agostinho, Reiner, Miriam, Pineda, Jaime A, Paret, Christian, Ossadtchi, Alexei, Nicholson, Andrew A, Nan, Wenya, Minguez, Javier, Micoulaud-Franchi, Jean-Arthur, Mehler, David MA, Lührs, Michael, Lubar, Joel, Lotte, Fabien, Linden, David EJ, Lewis-Peacock, Jarrod A, Lebedev, Mikhail A, Lanius, Ruth A, Kübler, Andrea, Kranczioch, Cornelia, Koush, Yury, Konicar, Lilian, Kohl, Simon H, Kober, Silivia E, Klados, Manousos A, Jeunet, Camille, Janssen, TWP, Huster, Rene J, Hoedlmoser, Kerstin, Hirshberg, Laurence M, Heunis, Stephan, Hendler, Talma, Hampson, Michelle, Guggisberg, Adrian G, Guggenberger, Robert, Gruzelier, John H, Göbel, Rainer W, Gninenko, Nicolas, Gharabaghi, Alireza, Frewen, Paul, Fovet, Thomas, Fernández, Thalía, Escolano, Carlos, Ehlis, Ann-Christine, Drechsler, Renate, deCharms, R Christopher, Debener, Stefan, De Ridder, Dirk, Davelaar, Eddy J, Congedo, Marco, Cavazza, Marc, Breteler, Marinus HM, Brandeis, Daniel, Bodurka, Jerzy, Birbaumer, Niels, Bazanova, Olga M, Barth, Beatrix, Bamidis, Panagiotis D, Auer, Tibor, Arns, Martijn, and Thibault, Robert T

Subjects
Mind and Body, Adult, Checklist, Consensus, Female, Humans, Male, Middle Aged, Neurofeedback, Peer Review, Research, Research Design, Stakeholder Participation, neurofeedback, regulation, consensus, checklist, guidelines, Medical and Health Sciences, Psychology and Cognitive Sciences, Neurology & Neurosurgery
Abstract

Neurofeedback has begun to attract the attention and scrutiny of the scientific and medical mainstream. Here, neurofeedback researchers present a consensus-derived checklist that aims to improve the reporting and experimental design standards in the field.

Published
2020

35. Digitalized transcranial electrical stimulation: A consensus statement

Author

Brunoni, Andre R, Ekhtiari, Hamed, Antal, Andrea, Auvichayapat, Paradee, Baeken, Chris, Benseñor, Isabela M., Bikson, Marom, Boggio, Paulo, Borroni, Barbara, Brighina, Filippo, Brunelin, Jerome, Carvalho, Sandra, Caumo, Wolnei, Ciechanski, Patrick, Charvet, Leigh, Clark, Vincent P., Cohen Kadosh, Roi, Cotelli, Maria, Datta, Abhishek, Deng, Zhi-De, De Raedt, Rudi, De Ridder, Dirk, Fitzgerald, Paul B., Floel, Agnes, Frohlich, Flavio, George, Mark S., Ghobadi-Azbari, Peyman, Goerigk, Stephan, Hamilton, Roy H., Jaberzadeh, Shapour J., Hoy, Kate, Kidgell, Dawson J., Zonoozi, Arash Khojasteh, Kirton, Adam, Laureys, Steven, Lavidor, Michal, Lee, Kiwon, Leite, Jorge, Lisanby, Sarah H., Loo, Colleen, Martin, Donel M., Miniussi, Carlo, Mondino, Marine, Monte-Silva, Katia, Morales-Quezada, Leon, Nitsche, Michael A., Okano, Alexandre H., Oliveira, Claudia S., Onarheim, Balder, Pacheco-Barrios, Kevin, Padberg, Frank, Nakamura-Palacios, Ester M., Palm, Ulrich, Paulus, Walter, Plewnia, Christian, Priori, Alberto, Rajji, Tarek K., Razza, Lais B., Rehn, Erik M., Ruffini, Giulio, Schellhorn, Klaus, Zare-Bidoky, Mehran, Simis, Marcel, Skorupinski, Pawel, Suen, Paulo, Thibaut, Aurore, Valiengo, Leandro C.L., Vanderhasselt, Marie-Anne, Vanneste, Sven, Venkatasubramanian, Ganesan, Violante, Ines R., Wexler, Anna, Woods, Adam J., and Fregni, Felipe

Published
2022
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46. Emerging Topics in the Behavioral Neuroscience of Tinnitus

Author

Searchfield, Grant D., Zhang, Jinsheng, Biswas, Roshni, De Ridder, Dirk, Deutsch, Brian, Hall, Deborah A., Hébert, Sylvie, Kleinjung, Tobias, Kleinstäuber, Maria, Langguth, Berthold, Lopez-Escamez, Jose Antonio, Maslin, Michael R. D., Mazurek, Birgit, Piccirillo, Jay F., Salvi, Richard, Schlee, Winfried, Shulman, Abraham, Shore, Susan, Szczepek, Agnieszka J., Smith, Paul F., Theodoroff, Sarah M., Vajsakovic, Dunja, Weise, Cornelia, Zheng, Yiwen, Geyer, Mark A., Series Editor, Ellenbroek, Bart A., Series Editor, Marsden, Charles A., Series Editor, Barnes, Thomas R.E., Series Editor, Andersen, Susan L., Series Editor, Paulus, Martin P., Series Editor, Searchfield, Grant D., editor, and Zhang, Jinsheng, editor

Published
2021
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47. Tinnitus and Brain Stimulation

Author

De Ridder, Dirk, Adhia, Divya, Langguth, Berthold, Geyer, Mark A., Series Editor, Ellenbroek, Bart A., Series Editor, Marsden, Charles A., Series Editor, Barnes, Thomas R.E., Series Editor, Andersen, Susan L., Series Editor, Paulus, Martin P., Series Editor, Searchfield, Grant D., editor, and Zhang, Jinsheng, editor

Published
2021
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48. Resting-State EEG Sex Classification Using Selected Brain Connectivity Representation

Author

Li, Jean, Deng, Jeremiah D., Adhia, Divya, De Ridder, Dirk, Crippen, Gordon, Advisory Editor, Dress, Andreas, Editor-in-Chief, Giegerich, Robert, Editorial Board Member, Kelso, Janet, Editorial Board Member, Linial, Michal, Editor-in-Chief, Felsenstein, Joseph, Advisory Editor, Troyanskaya, Olga, Editor-in-Chief, Gusfield, Dan, Advisory Editor, Myers, Gene, Editorial Board Member, Istrail, Sorin, Advisory Editor, Pevzner, Pavel, Editorial Board Member, Vingron, Martin, Editor-in-Chief, Lengauer, Thomas, Advisory Editor, McClure, Marcella, Advisory Editor, Nowak, Martin, Advisory Editor, Sankoff, David, Advisory Editor, Shamir, Ron, Advisory Editor, Steel, Mike, Advisory Editor, Stormo, Gary, Advisory Editor, Tavaré, Simon, Advisory Editor, Warnow, Tandy, Advisory Editor, Welch, Lonnie, Advisory Editor, Pham, Tuan D., editor, Yan, Hong, editor, Ashraf, Muhammad W., editor, and Sjöberg, Folke, editor

Published
2021
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