Advanced Search
Display options
Filter resources
Text Availability
Article type
Publication date
Species
Language
Sex
Age
Showing 1297 to 1307 of 1307 entries
Sorted by: Best Match Show Resources per page
Diffusion Decision Model: Current Issues and History.

Trends in cognitive sciences

Ratcliff R, Smith PL, Brown SD, McKoon G.
PMID: 26952739
Trends Cogn Sci. 2016 Apr;20(4):260-281. doi: 10.1016/j.tics.2016.01.007. Epub 2016 Mar 05.

There is growing interest in diffusion models to represent the cognitive and neural processes of speeded decision making. Sequential-sampling models like the diffusion model have a long history in psychology. They view decision making as a process of noisy...

Cite
Ratcliff R, Smith PL, Brown SD, et al. Diffusion Decision Model: Current Issues and History. Trends Cogn Sci. 2016;20(4):260-281doi: 10.1016/j.tics.2016.01.007.
Ratcliff, R., Smith, P. L., Brown, S. D., & McKoon, G. (2016). Diffusion Decision Model: Current Issues and History. Trends in cognitive sciences, 20(4), 260-281. https://doi.org/10.1016/j.tics.2016.01.007
Ratcliff, Roger, et al. "Diffusion Decision Model: Current Issues and History." Trends in cognitive sciences vol. 20,4 (2016): 260-281. doi: https://doi.org/10.1016/j.tics.2016.01.007
Ratcliff R, Smith PL, Brown SD, McKoon G. Diffusion Decision Model: Current Issues and History. Trends Cogn Sci. 2016 Apr;20(4):260-281. doi: 10.1016/j.tics.2016.01.007. Epub 2016 Mar 05. PMID: 26952739; PMCID: PMC4928591.
Copy Download .nbib Format: NLM
Epileptic headache: A cephalic pain seizure, confirmed by the close coincidence with epileptiform EEG abnormalities.

Seizure

Cianchetti C.
PMID: 27020564
Seizure. 2016 May;38:75-6. doi: 10.1016/j.seizure.2016.03.004. Epub 2016 Mar 16.

No abstract available.

Cite
Cianchetti C. Epileptic headache: A cephalic pain seizure, confirmed by the close coincidence with epileptiform EEG abnormalities. Seizure. 2016;38:75-6doi: 10.1016/j.seizure.2016.03.004.
Cianchetti, C. (2016). Epileptic headache: A cephalic pain seizure, confirmed by the close coincidence with epileptiform EEG abnormalities. Seizure, 3875-6. https://doi.org/10.1016/j.seizure.2016.03.004
Cianchetti, Carlo. "Epileptic headache: A cephalic pain seizure, confirmed by the close coincidence with epileptiform EEG abnormalities." Seizure vol. 38 (2016): 75-6. doi: https://doi.org/10.1016/j.seizure.2016.03.004
Cianchetti C. Epileptic headache: A cephalic pain seizure, confirmed by the close coincidence with epileptiform EEG abnormalities. Seizure. 2016 May;38:75-6. doi: 10.1016/j.seizure.2016.03.004. Epub 2016 Mar 16. PMID: 27020564.
Copy Download .nbib Format: NLM
Tracking of Mental Workload with a Mobile EEG Sensor.

Sensors (Basel, Switzerland)

Kutafina E, Heiligers A, Popovic R, Brenner A, Hankammer B, Jonas SM, Mathiak K, Zweerings J.
PMID: 34372445
Sensors (Basel). 2021 Jul 31;21(15). doi: 10.3390/s21155205.

The aim of the present investigation was to assess if a mobile electroencephalography (EEG) setup can be used to track mental workload, which is an important aspect of learning performance and motivation and may thus represent a valuable source...

Cite
Kutafina E, Heiligers A, Popovic R, et al. Tracking of Mental Workload with a Mobile EEG Sensor. Sensors (Basel). 2021;21(15)doi: 10.3390/s21155205.
Kutafina, E., Heiligers, A., Popovic, R., Brenner, A., Hankammer, B., Jonas, S. M., Mathiak, K., & Zweerings, J. (2021). Tracking of Mental Workload with a Mobile EEG Sensor. Sensors (Basel, Switzerland), 21(15), . https://doi.org/10.3390/s21155205
Kutafina, Ekaterina, et al. "Tracking of Mental Workload with a Mobile EEG Sensor." Sensors (Basel, Switzerland) vol. 21,15 (2021). doi: https://doi.org/10.3390/s21155205
Kutafina E, Heiligers A, Popovic R, Brenner A, Hankammer B, Jonas SM, Mathiak K, Zweerings J. Tracking of Mental Workload with a Mobile EEG Sensor. Sensors (Basel). 2021 Jul 31;21(15). doi: 10.3390/s21155205. PMID: 34372445; PMCID: PMC8348794.
Copy Download .nbib Format: NLM
Galvanic Vestibular Stimulation-Based Prediction Error Decoding and Channel Optimization.

International journal of neural systems

Shi Y, Ganesh G, Ando H, Koike Y, Yoshida E, Yoshimura N.
PMID: 34376123
Int J Neural Syst. 2021 Nov;31(11):2150034. doi: 10.1142/S0129065721500349. Epub 2021 Aug 11.

A significant problem in brain-computer interface (BCI) research is decoding - obtaining required information from very weak noisy electroencephalograph signals and extracting considerable information from limited data. Traditional intention decoding methods, which obtain information from induced or spontaneous brain...

Cite
Shi Y, Ganesh G, Ando H, et al. Galvanic Vestibular Stimulation-Based Prediction Error Decoding and Channel Optimization. Int J Neural Syst. 2021;31(11):2150034doi: 10.1142/S0129065721500349.
Shi, Y., Ganesh, G., Ando, H., Koike, Y., Yoshida, E., & Yoshimura, N. (2021). Galvanic Vestibular Stimulation-Based Prediction Error Decoding and Channel Optimization. International journal of neural systems, 31(11), 2150034. https://doi.org/10.1142/S0129065721500349
Shi, Yuxi, et al. "Galvanic Vestibular Stimulation-Based Prediction Error Decoding and Channel Optimization." International journal of neural systems vol. 31,11 (2021): 2150034. doi: https://doi.org/10.1142/S0129065721500349
Shi Y, Ganesh G, Ando H, Koike Y, Yoshida E, Yoshimura N. Galvanic Vestibular Stimulation-Based Prediction Error Decoding and Channel Optimization. Int J Neural Syst. 2021 Nov;31(11):2150034. doi: 10.1142/S0129065721500349. Epub 2021 Aug 11. PMID: 34376123.
Copy Download .nbib Format: NLM
Level-K Classification from EEG Signals Using Transfer Learning.

Sensors (Basel, Switzerland)

Mizrahi D, Zuckerman I, Laufer I.
PMID: 34883911
Sensors (Basel). 2021 Nov 27;21(23). doi: 10.3390/s21237908.

Tacit coordination games are games in which communication between the players is not allowed or not possible. In these games, the more salient solutions, that are often perceived as more prominent, are referred to as

Cite
Mizrahi D, Zuckerman I, Laufer I. Level-K Classification from EEG Signals Using Transfer Learning. Sensors (Basel). 2021;21(23)doi: 10.3390/s21237908.
Mizrahi, D., Zuckerman, I., & Laufer, I. (2021). Level-K Classification from EEG Signals Using Transfer Learning. Sensors (Basel, Switzerland), 21(23), . https://doi.org/10.3390/s21237908
Mizrahi, Dor, et al. "Level-K Classification from EEG Signals Using Transfer Learning." Sensors (Basel, Switzerland) vol. 21,23 (2021). doi: https://doi.org/10.3390/s21237908
Mizrahi D, Zuckerman I, Laufer I. Level-K Classification from EEG Signals Using Transfer Learning. Sensors (Basel). 2021 Nov 27;21(23). doi: 10.3390/s21237908. PMID: 34883911; PMCID: PMC8659931.
Copy Download .nbib Format: NLM
Deep learning applied to electroencephalogram data in mental disorders: A systematic review.

Biological psychology

de Bardeci M, Ip CT, Olbrich S.
PMID: 33991592
Biol Psychol. 2021 May;162:108117. doi: 10.1016/j.biopsycho.2021.108117. Epub 2021 May 13.

In recent medical research, tremendous progress has been made in the application of deep learning (DL) techniques. This article systematically reviews how DL techniques have been applied to electroencephalogram (EEG) data for diagnostic and predictive purposes in conducting research...

Cite
de Bardeci M, Ip CT, Olbrich S. Deep learning applied to electroencephalogram data in mental disorders: A systematic review. Biol Psychol. 2021;162:108117doi: 10.1016/j.biopsycho.2021.108117.
de Bardeci, M., Ip, C. T., & Olbrich, S. (2021). Deep learning applied to electroencephalogram data in mental disorders: A systematic review. Biological psychology, 162108117. https://doi.org/10.1016/j.biopsycho.2021.108117
de Bardeci, Mateo, et al. "Deep learning applied to electroencephalogram data in mental disorders: A systematic review." Biological psychology vol. 162 (2021): 108117. doi: https://doi.org/10.1016/j.biopsycho.2021.108117
de Bardeci M, Ip CT, Olbrich S. Deep learning applied to electroencephalogram data in mental disorders: A systematic review. Biol Psychol. 2021 May;162:108117. doi: 10.1016/j.biopsycho.2021.108117. Epub 2021 May 13. PMID: 33991592.
Copy Download .nbib Format: NLM
Role of MRI and EEG in the initial evaluation of children with headaches.

Pediatrics international : official journal of the Japan Pediatric Society

Martens D, Oster I, Papanagiotou P, Gortner L, Meyer S.
PMID: 22830555
Pediatr Int. 2012 Aug;54(4):580-1. doi: 10.1111/j.1442-200X.2012.03643.x.

No abstract available.

Cite
Martens D, Oster I, Papanagiotou P, et al. Role of MRI and EEG in the initial evaluation of children with headaches. Pediatr Int. 2012;54(4):580-1doi: 10.1111/j.1442-200X.2012.03643.x.
Martens, D., Oster, I., Papanagiotou, P., Gortner, L., & Meyer, S. (2012). Role of MRI and EEG in the initial evaluation of children with headaches. Pediatrics international : official journal of the Japan Pediatric Society, 54(4), 580-1. https://doi.org/10.1111/j.1442-200X.2012.03643.x
Martens, Daniel, et al. "Role of MRI and EEG in the initial evaluation of children with headaches." Pediatrics international : official journal of the Japan Pediatric Society vol. 54,4 (2012): 580-1. doi: https://doi.org/10.1111/j.1442-200X.2012.03643.x
Martens D, Oster I, Papanagiotou P, Gortner L, Meyer S. Role of MRI and EEG in the initial evaluation of children with headaches. Pediatr Int. 2012 Aug;54(4):580-1. doi: 10.1111/j.1442-200X.2012.03643.x. PMID: 22830555.
Copy Download .nbib Format: NLM
When Worlds Collide.

Clinical EEG and neuroscience

Collura T.
PMID: 33754902
Clin EEG Neurosci. 2021 Mar;52(2):79-81. doi: 10.1177/1550059421993957.

No abstract available.

Cite
Collura T. When Worlds Collide. Clin EEG Neurosci. 2021;52(2):79-81doi: 10.1177/1550059421993957.
Collura, T. (2021). When Worlds Collide. Clinical EEG and neuroscience, 52(2), 79-81. https://doi.org/10.1177/1550059421993957
Collura, Thomas. "When Worlds Collide." Clinical EEG and neuroscience vol. 52,2 (2021): 79-81. doi: https://doi.org/10.1177/1550059421993957
Collura T. When Worlds Collide. Clin EEG Neurosci. 2021 Mar;52(2):79-81. doi: 10.1177/1550059421993957. PMID: 33754902.
Copy Download .nbib Format: NLM
Single-trial modeling separates multiple overlapping prediction errors during reward processing in human EEG.

Communications biology

Hoy CW, Steiner SC, Knight RT.
PMID: 34302057
Commun Biol. 2021 Jul 23;4(1):910. doi: 10.1038/s42003-021-02426-1.

Learning signals during reinforcement learning and cognitive control rely on valenced reward prediction errors (RPEs) and non-valenced salience prediction errors (PEs) driven by surprise magnitude. A core debate in reward learning focuses on whether valenced and non-valenced PEs can...

Cite
Hoy CW, Steiner SC, Knight RT. Single-trial modeling separates multiple overlapping prediction errors during reward processing in human EEG. Commun Biol. 2021;4(1):910doi: 10.1038/s42003-021-02426-1.
Hoy, C. W., Steiner, S. C., & Knight, R. T. (2021). Single-trial modeling separates multiple overlapping prediction errors during reward processing in human EEG. Communications biology, 4(1), 910. https://doi.org/10.1038/s42003-021-02426-1
Hoy, Colin W, et al. "Single-trial modeling separates multiple overlapping prediction errors during reward processing in human EEG." Communications biology vol. 4,1 (2021): 910. doi: https://doi.org/10.1038/s42003-021-02426-1
Hoy CW, Steiner SC, Knight RT. Single-trial modeling separates multiple overlapping prediction errors during reward processing in human EEG. Commun Biol. 2021 Jul 23;4(1):910. doi: 10.1038/s42003-021-02426-1. PMID: 34302057; PMCID: PMC8302587.
Copy Download .nbib Format: NLM
Sharing an Open Stimulation System for Auditory EEG Experiments Using Python, Raspberry Pi, and HifiBerry.

eNeuro

Corneyllie A, Perrin F, Heine L.
PMID: 34301720
eNeuro. 2021 Aug 25;8(4). doi: 10.1523/ENEURO.0524-20.2021. Print 2021.

In auditory behavioral and EEG experiments, the variability of stimulation solutions, for both software and hardware, adds unnecessary technical constraints. Currently, there is no easy to use, inexpensive, and shareable solution that could improve collaborations and data comparisons across...

Cite
Corneyllie A, Perrin F, Heine L. Sharing an Open Stimulation System for Auditory EEG Experiments Using Python, Raspberry Pi, and HifiBerry. eNeuro. 2021;8(4)doi: 10.1523/ENEURO.0524-20.2021.
Corneyllie, A., Perrin, F., & Heine, L. (2021). Sharing an Open Stimulation System for Auditory EEG Experiments Using Python, Raspberry Pi, and HifiBerry. eNeuro, 8(4), . https://doi.org/10.1523/ENEURO.0524-20.2021
Corneyllie, Alexandra, et al. "Sharing an Open Stimulation System for Auditory EEG Experiments Using Python, Raspberry Pi, and HifiBerry." eNeuro vol. 8,4 (2021). doi: https://doi.org/10.1523/ENEURO.0524-20.2021
Corneyllie A, Perrin F, Heine L. Sharing an Open Stimulation System for Auditory EEG Experiments Using Python, Raspberry Pi, and HifiBerry. eNeuro. 2021 Aug 25;8(4). doi: 10.1523/ENEURO.0524-20.2021. Print 2021. PMID: 34301720; PMCID: PMC8387158.
Copy Download .nbib Format: NLM
MRI correlates of cognitive improvement after home-based EEG neurofeedback training in patients with multiple sclerosis: a pilot study.

Journal of neurology

Pinter D, Kober SE, Fruhwirth V, Berger L, Damulina A, Khalil M, Neuper C, Wood G, Enzinger C.
PMID: 33786666
J Neurol. 2021 Oct;268(10):3808-3816. doi: 10.1007/s00415-021-10530-9. Epub 2021 Mar 30.

OBJECTIVE: Neurofeedback training may improve cognitive function in patients with neurological disorders. However, the underlying cerebral mechanisms of such improvements are poorly understood. Therefore, we aimed to investigate MRI correlates of cognitive improvement after EEG-based neurofeedback training in patients...

Cite
Pinter D, Kober SE, Fruhwirth V, et al. MRI correlates of cognitive improvement after home-based EEG neurofeedback training in patients with multiple sclerosis: a pilot study. J Neurol. 2021;268(10):3808-3816doi: 10.1007/s00415-021-10530-9.
Pinter, D., Kober, S. E., Fruhwirth, V., Berger, L., Damulina, A., Khalil, M., Neuper, C., Wood, G., & Enzinger, C. (2021). MRI correlates of cognitive improvement after home-based EEG neurofeedback training in patients with multiple sclerosis: a pilot study. Journal of neurology, 268(10), 3808-3816. https://doi.org/10.1007/s00415-021-10530-9
Pinter, Daniela, et al. "MRI correlates of cognitive improvement after home-based EEG neurofeedback training in patients with multiple sclerosis: a pilot study." Journal of neurology vol. 268,10 (2021): 3808-3816. doi: https://doi.org/10.1007/s00415-021-10530-9
Pinter D, Kober SE, Fruhwirth V, Berger L, Damulina A, Khalil M, Neuper C, Wood G, Enzinger C. MRI correlates of cognitive improvement after home-based EEG neurofeedback training in patients with multiple sclerosis: a pilot study. J Neurol. 2021 Oct;268(10):3808-3816. doi: 10.1007/s00415-021-10530-9. Epub 2021 Mar 30. PMID: 33786666; PMCID: PMC8463344.
Copy Download .nbib Format: NLM
Showing 1297 to 1307 of 1307 entries