Akshay Paul

444 total citations · 1 hit paper
23 papers, 330 citations indexed

About

Akshay Paul is a scholar working on Cognitive Neuroscience, Cellular and Molecular Neuroscience and Biomedical Engineering. According to data from OpenAlex, Akshay Paul has authored 23 papers receiving a total of 330 indexed citations (citations by other indexed papers that have themselves been cited), including 18 papers in Cognitive Neuroscience, 11 papers in Cellular and Molecular Neuroscience and 9 papers in Biomedical Engineering. Recurrent topics in Akshay Paul's work include EEG and Brain-Computer Interfaces (15 papers), Neuroscience and Neural Engineering (11 papers) and Neural dynamics and brain function (5 papers). Akshay Paul is often cited by papers focused on EEG and Brain-Computer Interfaces (15 papers), Neuroscience and Neural Engineering (11 papers) and Neural dynamics and brain function (5 papers). Akshay Paul collaborates with scholars based in United States and South Korea. Akshay Paul's co-authors include Gert Cauwenberghs, Yuchen Xu, Stephen Deiss, Thao Thi Phuong Nguyen, Mark Bachman, Jonathan Pegan, Michael Chu, Michelle Khine, Joshua Kim and Patrick P. Mercier and has published in prestigious journals such as SHILAP Revista de lepidopterología, Nanoscale and Nature Biomedical Engineering.

In The Last Decade

Akshay Paul

21 papers receiving 328 citations

Hit Papers

In-ear integrated sensor array for the continuous monitor... 2023 2026 2024 2025 2023 25 50 75 100
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. In-ear integrated sensor array for the continuous monitoring of brain activity and of lactate in sweat
    2023 100 citations Yuchen Xu, Ernesto De la Paz et al. Nature Biomedical Engineering profile →

Peers

Akshay Paul
Yuchen Xu United States
Chuanjie Yao China
Yong Won Kwon South Korea
Ju-Chun Hsieh United States
Tucker Stuart United States
Ilyoun Na United States
Pratyush Rai United States
Özberk Öztürk Türkiye
Amir Servati Canada
Tsz Hung Wong Hong Kong
Yuchen Xu United States
Akshay Paul
Citations per year, relative to Akshay Paul Akshay Paul (= 1×) peers Yuchen Xu

Countries citing papers authored by Akshay Paul

Since Specialization
Citations

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

Fields of papers citing papers by Akshay Paul

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Akshay Paul

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

All Works

20 of 20 papers shown
1.
Liu, Zhaoyi, Akshay Paul, Florian Chapotot, et al.. (2025). In-Ear EEG Auditory Neurofeedback Towards Unobtrusive Sleep Enhancement. eScholarship (California Digital Library). 1–7.
2.
Xu, Yuchen, Kuldeep Mahato, Muyang Lin, et al.. (2024). Earable Multimodal Sensing and Stimulation: A Prospective Toward Unobtrusive Closed-Loop Biofeedback. IEEE Reviews in Biomedical Engineering. 18. 5–25. 3 indexed citations
3.
Wu, Jiajia, Abraham Akinin, Akshay Paul, et al.. (2024). A Low-Noise Low-Power 0.001Hz–1kHz Neural Recording System-on-Chip With Sample-Level Duty-Cycling. IEEE Transactions on Biomedical Circuits and Systems. 18(2). 263–273. 3 indexed citations
4.
Wang, Jun, Ren Liu, Youngbin Tchoe, et al.. (2024). Low-Power Fully Integrated 256-Channel Nanowire Electrode-on-Chip Neural Interface for Intracellular Electrophysiology. IEEE Transactions on Biomedical Circuits and Systems. 19(1). 196–208. 1 indexed citations
5.
6.
Paul, Akshay, et al.. (2023). A Versatile In-Ear Biosensing System and Body-Area Network for Unobtrusive Continuous Health Monitoring. IEEE Transactions on Biomedical Circuits and Systems. 17(3). 483–494. 23 indexed citations
7.
Xu, Yuchen, Ernesto De la Paz, Akshay Paul, et al.. (2023). In-ear integrated sensor array for the continuous monitoring of brain activity and of lactate in sweat. Nature Biomedical Engineering. 7(10). 1307–1320. 100 indexed citations breakdown →
8.
Xu, Yuchen, Ernesto De la Paz, Akshay Paul, et al.. (2023). In-ear integrated sensor array for the continuous monitoring of brain activity and of lactate in sweat. Zenodo (CERN European Organization for Nuclear Research). 1 indexed citations
9.
Paul, Akshay, et al.. (2023). Scalable Anatomically-Tunable Fully In-Ear Dry-Electrode Array for User-Generic Unobtrusive Electrophysiology. PubMed. 2023. 1–4. 2 indexed citations
10.
Wu, Jiajia, Abraham Akinin, Akshay Paul, et al.. (2023). A Low-Noise 0.001Hz-lkHz Sample-Level Duty-Cycling Neural Recording System-on-Chip. 1–5.
11.
Paul, Akshay, et al.. (2023). A Machine Learning Approach to COVID-19 Detection via Graphene Field-Effect-Transistor (GFET). PubMed. 2023. 1–4. 1 indexed citations
12.
Paul, Akshay, et al.. (2022). Characterization of Ag/AgCl Dry Electrodes for Wearable Electrophysiological Sensing. SHILAP Revista de lepidopterología. 2. 26 indexed citations
13.
Lee, Irene, et al.. (2022). Audio Mapping Using LiDAR to Assist the Visually Impaired. 4. 374–378. 1 indexed citations
14.
Paul, Akshay, et al.. (2022). A Versatile In-Ear Biosensing System for Continuous Brain and Health Monitoring. 2022 IEEE International Symposium on Circuits and Systems (ISCAS). 620–624. 5 indexed citations
15.
Wang, Keli, et al.. (2022). Unity Human Eye Model for Gaze Tracking with a Query-Driven Dynamic Vision Sensor. 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). 2022. 2194–2198. 2 indexed citations
16.
Kubendran, Rajkumar, Akshay Paul, & Gert Cauwenberghs. (2021). A 256x256 6.3pJ/pixel-event Query-driven Dynamic Vision Sensor with Energy-conserving Row-parallel Event Scanning. 1–2. 9 indexed citations
17.
Wang, Jun, Akshay Paul, Dinghong Zhang, et al.. (2020). 1024-Electrode Hybrid Voltage/Current-Clamp Neural Interface System-on-Chip with Dynamic Incremental-SAR Acquisition. 1–2. 5 indexed citations
18.
Paul, Akshay, et al.. (2019). Integrated In-Ear Device for Auditory Health Assessment. PubMed. 2019. 56–59. 13 indexed citations
19.
20.
Pegan, Jonathan, Michael Chu, Thao Thi Phuong Nguyen, et al.. (2016). Skin-mountable stretch sensor for wearable health monitoring. Nanoscale. 8(39). 17295–17303. 107 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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