Hooman Nezamfar

641 total citations
26 papers, 428 citations indexed

About

Hooman Nezamfar is a scholar working on Cognitive Neuroscience, Cellular and Molecular Neuroscience and Human-Computer Interaction. According to data from OpenAlex, Hooman Nezamfar has authored 26 papers receiving a total of 428 indexed citations (citations by other indexed papers that have themselves been cited), including 22 papers in Cognitive Neuroscience, 10 papers in Cellular and Molecular Neuroscience and 7 papers in Human-Computer Interaction. Recurrent topics in Hooman Nezamfar's work include EEG and Brain-Computer Interfaces (22 papers), Neuroscience and Neural Engineering (10 papers) and Neural dynamics and brain function (8 papers). Hooman Nezamfar is often cited by papers focused on EEG and Brain-Computer Interfaces (22 papers), Neuroscience and Neural Engineering (10 papers) and Neural dynamics and brain function (8 papers). Hooman Nezamfar collaborates with scholars based in United States, Mexico and Canada. Hooman Nezamfar's co-authors include Deniz Erdoğmuş, Barry Oken, Meghan Miller, Helané Wahbeh, Murat Akçakaya, Umut Orhan, Melanie Fried‐Oken, Seyed Sadegh Mohseni Salehi, Jeannie S. Huang and Virginia R. de and has published in prestigious journals such as IEEE Transactions on Signal Processing, IEEE Transactions on Biomedical Engineering and IEEE Transactions on Neural Systems and Rehabilitation Engineering.

In The Last Decade

Hooman Nezamfar

23 papers receiving 410 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Hooman Nezamfar United States	11	324	93	89	88	74	26	428
Wenya Nan China	13	576 1.8×	83 0.9×	121 1.4×	27 0.3×	34 0.5×	38	663
David Steyrl Austria	14	327 1.0×	27 0.3×	101 1.1×	63 0.7×	50 0.7×	54	477
Tony Steffert United Kingdom	10	391 1.2×	124 1.3×	36 0.4×	47 0.5×	75 1.0×	22	594
Zhenfu Wen United States	12	334 1.0×	32 0.3×	63 0.7×	29 0.3×	157 2.1×	24	449
Dylan DeLosAngeles Australia	10	685 2.1×	72 0.8×	143 1.6×	68 0.8×	69 0.9×	11	774
Janir Nuno da Cruz Switzerland	10	464 1.4×	60 0.6×	100 1.1×	12 0.1×	68 0.9×	20	536
Vladimir Bostanov Germany	13	744 2.3×	65 0.7×	132 1.5×	161 1.8×	211 2.9×	15	1.1k
Chi-Yuan Chang United States	6	545 1.7×	70 0.8×	70 0.8×	14 0.2×	64 0.9×	17	675
VK Sinha India	6	342 1.1×	31 0.3×	44 0.5×	64 0.7×	66 0.9×	10	488
Grzegorz M. Wójcik Poland	13	284 0.9×	27 0.3×	65 0.7×	30 0.3×	25 0.3×	52	441

Countries citing papers authored by Hooman Nezamfar

Since Specialization

Citations

This map shows the geographic impact of Hooman Nezamfar's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Hooman Nezamfar with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Hooman Nezamfar more than expected).

Fields of papers citing papers by Hooman Nezamfar

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Hooman Nezamfar. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Hooman Nezamfar. The network helps show where Hooman Nezamfar may publish in the future.

Co-authorship network of co-authors of Hooman Nezamfar

This figure shows the co-authorship network connecting the top 25 collaborators of Hooman Nezamfar. A scholar is included among the top collaborators of Hooman Nezamfar based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Hooman Nezamfar. Hooman Nezamfar is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Akçakaya, Murat, Xiaojing Xu, Virginia R. de, et al.. (2021). Automated Pain Assessment in Children Using Electrodermal Activity and Video Data Fusion via Machine Learning. IEEE Transactions on Biomedical Engineering. 69(1). 422–431. 25 indexed citations

Xu, Xiaojing, Hooman Nezamfar, Kenneth D. Craig, et al.. (2019). Towards Automated Pain Detection in Children Using Facial and Electrodermal Activity. Lecture notes in computer science. 2142. 181–189. 17 indexed citations

Akçakaya, Murat, Hooman Nezamfar, Xiaojing Xu, et al.. (2018). Automated Pain Assessment using Electrodermal Activity Data and Machine Learning. PubMed. 2018. 372–375. 37 indexed citations

Nezamfar, Hooman, et al.. (2018). Code-VEP vs. Eye Tracking: A Comparison Study. Brain Sciences. 8(7). 130–130. 8 indexed citations

Yazdi, Golnaz Eftekhari, et al.. (2018). An Adaptive Proportional BCI-Controller for Linear Dynamic Systems. 1–5. 2 indexed citations

Akçakaya, Murat, et al.. (2017). An Active RBSE Framework to Generate Optimal Stimulus Sequences in a BCI for Spelling. IEEE Transactions on Signal Processing. 65(20). 5381–5392. 10 indexed citations

Salehi, Seyed Sadegh Mohseni, et al.. (2017). Decoding complex imagery hand gestures. PubMed. 2017. 2968–2971. 10 indexed citations

Haghighi, Marzieh, et al.. (2016). Toward a brain interface for tracking attended auditory sources. 1–5. 3 indexed citations

Nezamfar, Hooman, Seyed Sadegh Mohseni Salehi, & Deniz Erdoğmuş. (2015). Stimuli with opponent colors and higher bit rate enable higher accuracy for C-VEP BCI. 1–6. 8 indexed citations

10.

Akçakaya, Murat, et al.. (2015). Active learning for efficient querying from a human oracle with noisy response in a language-model assisted brain computer interface. 1. 1–6.

11.

Orhan, Umut, et al.. (2015). Language-Model Assisted Brain Computer Interface for Typing: A Comparison of Matrix and Rapid Serial Visual Presentation. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 23(5). 910–920. 27 indexed citations

12.

Oken, Barry, et al.. (2014). Signal Processing and Machine Learning of EEG and Respiration Changes During Mindfulness Meditation State. The Journal of Alternative and Complementary Medicine. 20(5). A25–A25. 1 indexed citations

13.

Orhan, Umut, et al.. (2014). RSVP IconMessenger: icon-based brain-interfaced alternative and augmentative communication. 1(3-4). 192–203. 8 indexed citations

14.

Akçakaya, Murat, et al.. (2014). A Bayesian Framework for Intent Detection and Stimulation Selection in SSVEP BCIs. IEEE Signal Processing Letters. 22(6). 743–747. 12 indexed citations

15.

Wahbeh, Helané, et al.. (2014). Quantitative change of EEG and respiration signals during mindfulness meditation. Journal of NeuroEngineering and Rehabilitation. 11(1). 87–87. 111 indexed citations

16.

Wahbeh, Helané, et al.. (2013). Change in physiological signals during mindfulness meditation. PubMed. 1378–1381. 30 indexed citations

17.

Hild, Kenneth E., Umut Orhan, Deniz Erdoğmuş, et al.. (2011). An ERP-based Brain-Computer Interface for text entry using Rapid Serial Visual Presentation and Language Modeling. Meeting of the Association for Computational Linguistics. 38–43. 9 indexed citations

18.

Nezamfar, Hooman, Umut Orhan, Deniz Erdoğmuş, et al.. (2011). On visually evoked potentials in eeg induced by multiple pseudorandom binary sequences for brain computer interface design. PubMed. 2044–2047. 9 indexed citations

19.

Orhan, Umut, Deniz Erdoğmuş, Brian Roark, et al.. (2011). Fusion with language models improves spelling accuracy for ERP-based brain computer interface spellers. PubMed. 113. 5774–5777. 26 indexed citations

20.

Nezamfar, Hooman, et al.. (2011). Decoding of multichannel EEG activity from the visual cortex in response to pseudorandom binary sequences of visual stimuli. International Journal of Imaging Systems and Technology. 21(2). 139–147. 20 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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