Ali Shoeb

3.3k total citations
26 papers, 1.9k citations indexed

About

Ali Shoeb is a scholar working on Cognitive Neuroscience, Cellular and Molecular Neuroscience and Psychiatry and Mental health. According to data from OpenAlex, Ali Shoeb has authored 26 papers receiving a total of 1.9k indexed citations (citations by other indexed papers that have themselves been cited), including 19 papers in Cognitive Neuroscience, 10 papers in Cellular and Molecular Neuroscience and 8 papers in Psychiatry and Mental health. Recurrent topics in Ali Shoeb's work include EEG and Brain-Computer Interfaces (18 papers), Neuroscience and Neural Engineering (9 papers) and Epilepsy research and treatment (8 papers). Ali Shoeb is often cited by papers focused on EEG and Brain-Computer Interfaces (18 papers), Neuroscience and Neural Engineering (9 papers) and Epilepsy research and treatment (8 papers). Ali Shoeb collaborates with scholars based in United States, Belgium and South Korea. Ali Shoeb's co-authors include John V. Guttag, Anantha P. Chandrakasan, John Guttag, Naveen Verma, John V. Guttag, Joel Dawson, Jose L. Bohorquez, Blaise F. D. Bourgeois, S. Ted Treves and Dina El-Damak and has published in prestigious journals such as Neurology, IEEE Journal of Solid-State Circuits and Epilepsia.

In The Last Decade

Ali Shoeb

26 papers receiving 1.8k citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Ali Shoeb United States	16	1.3k	557	553	525	460	26	1.9k
Martin Glasstetter Germany	9	2.1k 1.7×	646 1.2×	393 0.7×	216 0.4×	519 1.1×	11	2.4k
Robin Tibor Schirrmeister Germany	9	2.2k 1.7×	650 1.2×	435 0.8×	205 0.4×	538 1.2×	18	2.5k
Yongtian He United States	12	1.3k 1.0×	379 0.7×	184 0.3×	545 1.0×	199 0.4×	16	1.8k
Lukas D. J. Fiederer Germany	8	2.1k 1.6×	659 1.2×	390 0.7×	202 0.4×	532 1.2×	9	2.3k
Iyad Obeid United States	17	945 0.7×	470 0.8×	215 0.4×	190 0.4×	267 0.6×	71	1.3k
Muhammad Awais Bin Altaf Pakistan	18	731 0.6×	451 0.8×	157 0.3×	600 1.1×	405 0.9×	59	1.4k
Steven Lemm Germany	9	2.8k 2.2×	934 1.7×	751 1.4×	267 0.5×	451 1.0×	11	3.1k
Zheng Yang Chin Singapore	18	3.4k 2.7×	1.5k 2.8×	852 1.5×	542 1.0×	785 1.7×	34	3.7k
G. Pfurtscheller Austria	21	2.1k 1.7×	718 1.3×	230 0.4×	233 0.4×	230 0.5×	72	2.3k

Countries citing papers authored by Ali Shoeb

Since Specialization

Citations

This map shows the geographic impact of Ali Shoeb'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 Ali Shoeb with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ali Shoeb more than expected).

Fields of papers citing papers by Ali Shoeb

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ali Shoeb. 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 Ali Shoeb. The network helps show where Ali Shoeb may publish in the future.

Co-authorship network of co-authors of Ali Shoeb

This figure shows the co-authorship network connecting the top 25 collaborators of Ali Shoeb. A scholar is included among the top collaborators of Ali Shoeb 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 Ali Shoeb. Ali Shoeb is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Shoeb, Ali, et al.. (2020). Deep learning for automated sleep staging using instantaneous heart rate. npj Digital Medicine. 3(1). 106–106. 87 indexed citations

Shoeb, Ali, et al.. (2018). Evaluating Convolutional and Recurrent Neural Network Architectures for Respiratory-Effort Related Arousal Detection during Sleep. Computing in cardiology. 45. 4 indexed citations

Miocinovic, Svjetlana, Ali Shoeb, Sarah Wang, et al.. (2016). Tremor Severity Estimation Using Liftware Instrumented Eating Utensil (P4.295). Neurology. 86(16_supplement). 2 indexed citations

McLaughlin, Bryan, et al.. (2012). An electroencephalographic recording platform for real-time seizure detection. PubMed. 1. 875–878. 5 indexed citations

Yoo, Jerald, Long Yan, Dina El-Damak, et al.. (2012). An 8-channel scalable EEG acquisition SoC with fully integrated patient-specific seizure classification and recording processor. 292–294. 51 indexed citations

Larson, Anna M., Robin Ryther, Mélanie Jennesson, et al.. (2012). Impact of pediatric epilepsy on sleep patterns and behaviors in children and parents. Epilepsia. 53(7). 1162–1169. 95 indexed citations

Lee, Kyong-Ho, et al.. (2011). A data-driven modeling approach to stochastic computation for low-energy biomedical devices. PubMed. 101. 826–829. 7 indexed citations

Shoeb, Ali, et al.. (2011). A machine-learning algorithm for detecting seizure termination in scalp EEG. Epilepsy & Behavior. 22. S36–S43. 43 indexed citations

Shoeb, Ali, et al.. (2011). An algorithm for detecting seizure termination in scalp EEG. PubMed. 2011. 1443–1446. 9 indexed citations

10.

Verma, Naveen, Kyong-Ho Lee, & Ali Shoeb. (2011). Data-Driven Approaches for Computation in Intelligent Biomedical Devices: A Case Study of EEG Monitoring for Chronic Seizure Detection. DSpace@MIT (Massachusetts Institute of Technology). 1 indexed citations

11.

Kharbouch, Alaa, Ali Shoeb, John Guttag, & Sydney S. Cash. (2011). An algorithm for seizure onset detection using intracranial EEG. Epilepsy & Behavior. 22. S29–S35. 69 indexed citations

12.

Shoeb, Ali & John V. Guttag. (2010). Application of Machine Learning To Epileptic Seizure Detection. International Conference on Machine Learning. 975–982. 317 indexed citations

13.

Shoeb, Ali, et al.. (2009). A micropower support vector machine based seizure detection architecture for embedded medical devices. PubMed. 2009. 4202–4205. 54 indexed citations

14.

Shoeb, Ali, Trudy Pang, John Guttag, & Steven C. Schachter. (2009). NON-INVASIVE COMPUTERIZED SYSTEM FOR AUTOMATICALLY INITIATING VAGUS NERVE STIMULATION FOLLOWING PATIENT-SPECIFIC DETECTION OF SEIZURES OR EPILEPTIFORM DISCHARGES. International Journal of Neural Systems. 19(3). 157–172. 73 indexed citations

15.

Verma, Naveen, Ali Shoeb, John V. Guttag, & Anantha P. Chandrakasan. (2009). A Micro-power EEG acquisition SoC with integrated seizure detection processor for continuous patient monitoring. DSpace@MIT (Massachusetts Institute of Technology). 62–63. 33 indexed citations

16.

Shih, Eugene, Ali Shoeb, & John V. Guttag. (2009). Sensor selection for energy-efficient ambulatory medical monitoring. DSpace@MIT (Massachusetts Institute of Technology). 347–358. 3 indexed citations

17.

Shoeb, Ali, Blaise F. D. Bourgeois, S. Ted Treves, Steven C. Schachter, & John Guttag. (2007). Impact of Patient-Specificity on Seizure Onset Detection Performance. Conference proceedings. 4110–4114. 16 indexed citations

18.

Shoeb, Ali, Helen Edwards, James L. Connolly, et al.. (2005). Patient-specific seizure onset detection. PubMed. 3. 419–422. 65 indexed citations

19.

Shoeb, Ali, John Guttag, Steven C. Schachter, et al.. (2005). Detecting Seizure Onset in the Ambulatory Setting: Demonstrating Feasibility. PubMed. 2005. 3546–3550. 30 indexed citations

20.

Shoeb, Ali, et al.. (2004). Patient-specific seizure onset detection. Epilepsy & Behavior. 5(4). 483–498. 247 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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