James Mountstephens

1.3k total citations · 1 hit paper
40 papers, 895 citations indexed

About

James Mountstephens is a scholar working on Cognitive Neuroscience, Experimental and Cognitive Psychology and Cardiology and Cardiovascular Medicine. According to data from OpenAlex, James Mountstephens has authored 40 papers receiving a total of 895 indexed citations (citations by other indexed papers that have themselves been cited), including 28 papers in Cognitive Neuroscience, 27 papers in Experimental and Cognitive Psychology and 11 papers in Cardiology and Cardiovascular Medicine. Recurrent topics in James Mountstephens's work include EEG and Brain-Computer Interfaces (27 papers), Emotion and Mood Recognition (26 papers) and Heart Rate Variability and Autonomic Control (11 papers). James Mountstephens is often cited by papers focused on EEG and Brain-Computer Interfaces (27 papers), Emotion and Mood Recognition (26 papers) and Heart Rate Variability and Autonomic Control (11 papers). James Mountstephens collaborates with scholars based in Malaysia, Pakistan and Japan. James Mountstephens's co-authors include Jason Teo, Nazmi Sofian Suhaimi, Kenneth Tze Kin Teo, Yuto Lim, Kashif Nisar, Mohd Hanafi Ahmad Hijazi, Chin Kim On and Say Leng Goh and has published in prestigious journals such as Sensors, Journal Of Big Data and Computational Intelligence and Neuroscience.

In The Last Decade

James Mountstephens

38 papers receiving 862 citations

Hit Papers

EEG-Based Emotion Recognition: A State-of-the-Art Review ... 2020 2026 2022 2024 2020 50 100 150 200
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. EEG-Based Emotion Recognition: A State-of-the-Art Review of Current Trends and Opportunities
    2020 241 citations Nazmi Sofian Suhaimi, James Mountstephens et al. profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
James Mountstephens Malaysia 12 492 426 205 144 114 40 895
Matteo Spezialetti Italy 17 341 0.7× 213 0.5× 171 0.8× 100 0.7× 72 0.6× 36 782
Fatma Nasoz United States 11 394 0.8× 603 1.4× 135 0.7× 284 2.0× 162 1.4× 18 945
Zirui Lan Singapore 13 700 1.4× 522 1.2× 192 0.9× 99 0.7× 48 0.4× 21 909
Jian Cui Singapore 13 296 0.6× 231 0.5× 190 0.9× 127 0.9× 61 0.5× 55 780
Robert Schleicher Germany 15 438 0.9× 322 0.8× 253 1.2× 253 1.8× 220 1.9× 57 970
Thorsten O. Zander Germany 17 1.3k 2.7× 170 0.4× 346 1.7× 195 1.4× 79 0.7× 45 1.6k
Gert-Jan de Vries Netherlands 9 265 0.5× 212 0.5× 110 0.5× 142 1.0× 46 0.4× 17 587
Felix Putze Germany 19 817 1.7× 145 0.3× 401 2.0× 240 1.7× 171 1.5× 102 1.4k
Zhao Lv China 17 526 1.1× 201 0.5× 255 1.2× 36 0.3× 90 0.8× 85 888
Gene Davis United States 5 522 1.1× 223 0.5× 123 0.6× 318 2.2× 38 0.3× 8 907

Countries citing papers authored by James Mountstephens

Since Specialization
Citations

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

Fields of papers citing papers by James Mountstephens

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of James Mountstephens

This figure shows the co-authorship network connecting the top 25 collaborators of James Mountstephens. A scholar is included among the top collaborators of James Mountstephens 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 James Mountstephens. James Mountstephens 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.
Goh, Say Leng, et al.. (2023). A Great Deluge Algorithm for University Course Timetabling. 6. 1–5. 1 indexed citations
2.
Mountstephens, James, et al.. (2022). Eye-Tracking Feature Extraction for Biometric Machine Learning. Frontiers in Neurorobotics. 15. 796895–796895. 30 indexed citations
3.
4.
Suhaimi, Nazmi Sofian, James Mountstephens, & Jason Teo. (2021). Class-Based Analysis of Russell's Four-Quadrant Emotion Prediction in Virtual Reality using Multi-Layer Feedforward ANNs. 155–161. 2 indexed citations
5.
Teo, Jason, et al.. (2020). Pushing the boundaries of EEG-based emotion classification using consumer-grade wearable brain-computer interfacing devices and ensemble classifiers. UMS Institutional Repository (Universiti Malaysia Sabah). 2 indexed citations
6.
Mountstephens, James, et al.. (2020). Emotion Recognition Using Eye-Tracking: Taxonomy, Review and Current Challenges. Sensors. 20(8). 2384–2384. 175 indexed citations
7.
Mountstephens, James. (2020). Enhancing Memory For Technical Lists With Computer-Generated Mnemonics. 271–274. 1 indexed citations
8.
Mountstephens, James, et al.. (2020). Four-Class Emotion Classification using Electrocardiography (ECG) in Virtual Reality (VR). UMS Institutional Repository (Universiti Malaysia Sabah). 2 indexed citations
9.
Mountstephens, James, et al.. (2020). Four-class emotion classification in virtual reality using pupillometry. Journal Of Big Data. 7(1). 35 indexed citations
10.
Teo, Jason, et al.. (2018). Preference Classification Using Electroencephalography (EEG) and Deep Learning. Journal of Telecommunication Electronic and Computer Engineering (JTEC). 10. 87–91. 11 indexed citations
11.
Teo, Jason, et al.. (2018). Classification of Affective States via EEG and Deep Learning. International Journal of Advanced Computer Science and Applications. 9(5). 20 indexed citations
12.
Teo, Jason, et al.. (2018). Empirical Analysis of Intra vs. Inter-Subject Variability in VR EEG-Based Emotion Modelling. UMS Institutional Repository (Universiti Malaysia Sabah). 2 indexed citations
13.
14.
Teo, Jason, et al.. (2017). Deep learning for EEG-Based preference classification. AIP conference proceedings. 22 indexed citations
15.
Mountstephens, James, et al.. (2015). EEG-based recognition of positive and negative emotions using for pleasant vs. unpleasant images. UMS Institutional Repository (Universiti Malaysia Sabah). 1 indexed citations
16.
Teo, Jason, et al.. (2015). Aesthetic preference recognition of 3D shapes using EEG. Cognitive Neurodynamics. 10(2). 165–173. 67 indexed citations
17.
Mountstephens, James. (2014). TOWARDS COMPUTER-GENERATED MNEMONIC PHRASES: EXPERIMENTS WITH GENETIC ALGORITHMS AND N-GRAMS. 1 indexed citations
18.
Mountstephens, James. (2013). Mnemonic phrase generation using genetic algorithms and natural language processing. 527–530. 1 indexed citations
19.
On, Chin Kim, et al.. (2012). Game AI generation using evolutionary multi-objective optimization. 2003. 1–8. 4 indexed citations
20.
Mountstephens, James, et al.. (2012). Multistage Image Clustering and Segmentation with Normalised Cuts. 362–367. 10 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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