Fo Hu

430 total citations
29 papers, 261 citations indexed

About

Fo Hu is a scholar working on Biomedical Engineering, Cognitive Neuroscience and Social Psychology. According to data from OpenAlex, Fo Hu has authored 29 papers receiving a total of 261 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Biomedical Engineering, 12 papers in Cognitive Neuroscience and 6 papers in Social Psychology. Recurrent topics in Fo Hu's work include EEG and Brain-Computer Interfaces (12 papers), Muscle activation and electromyography studies (10 papers) and Advanced Sensor and Energy Harvesting Materials (6 papers). Fo Hu is often cited by papers focused on EEG and Brain-Computer Interfaces (12 papers), Muscle activation and electromyography studies (10 papers) and Advanced Sensor and Energy Harvesting Materials (6 papers). Fo Hu collaborates with scholars based in China. Fo Hu's co-authors include Bin Zhou, Yanzheng Lu, Hong Wang, Xusheng Yang, Hao Tang, Hong Wang, Wen‐An Zhang, Lekai Zhang, Zhihan Zhang and Jichi Chen and has published in prestigious journals such as Journal of Affective Disorders, IEEE Transactions on Intelligent Transportation Systems and Frontiers in Neuroscience.

In The Last Decade

Fo Hu

27 papers receiving 255 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Fo Hu China 11 137 96 59 32 24 29 261
Gianluca De Luca United States 6 197 1.4× 121 1.3× 72 1.2× 25 0.8× 40 1.7× 12 365
Debatri Chatterjee India 12 63 0.5× 199 2.1× 95 1.6× 66 2.1× 42 1.8× 54 396
P. A. Karthick India 12 289 2.1× 199 2.1× 37 0.6× 18 0.6× 15 0.6× 35 456
Antonello Florio Italy 12 101 0.7× 125 1.3× 22 0.4× 42 1.3× 26 1.1× 35 405
Szczepan Paszkiel Poland 10 50 0.4× 139 1.4× 34 0.6× 21 0.7× 12 0.5× 37 257
Maged S. Al-Quraishi Malaysia 10 187 1.4× 154 1.6× 42 0.7× 14 0.4× 18 0.8× 25 360
Mojtaba Taherisadr United States 7 142 1.0× 63 0.7× 25 0.4× 41 1.3× 72 3.0× 9 254
Ericka Janet Rechy-Ramirez Mexico 10 150 1.1× 143 1.5× 117 2.0× 12 0.4× 37 1.5× 23 322
Long Meng China 11 174 1.3× 193 2.0× 85 1.4× 42 1.3× 54 2.3× 43 398
Nadica Miljković Serbia 9 80 0.6× 75 0.8× 31 0.5× 8 0.3× 10 0.4× 40 286

Countries citing papers authored by Fo Hu

Since Specialization
Citations

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

Fields of papers citing papers by Fo Hu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Fo Hu

This figure shows the co-authorship network connecting the top 25 collaborators of Fo Hu. A scholar is included among the top collaborators of Fo Hu 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 Fo Hu. Fo Hu 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.
Hu, Fo, et al.. (2025). STRFLNet: Spatio-Temporal Representation Fusion Learning Network for EEG-Based Emotion Recognition. IEEE Transactions on Affective Computing. 17(1). 204–218. 6 indexed citations
2.
Li, Gang, B Huang, Yuling Wang, et al.. (2025). Neurophysiological mechanisms and predictive modeling of SSRI treatment response in depression disorder based on multidimensional EEG features. Journal of Affective Disorders. 393(Pt B). 120424–120424.
3.
Zhang, Lekai, et al.. (2025). Exploring the efficacy of olfactory stimuli in angry driving through EEG analysis. Journal of Engineering Design. 1–22.
4.
Hu, Fo, et al.. (2024). STAFNet: an adaptive multi-feature learning network via spatiotemporal fusion for EEG-based emotion recognition. Frontiers in Neuroscience. 18. 1519970–1519970. 1 indexed citations
5.
Hu, Fo, et al.. (2024). Effects of Emotional Olfactory Stimuli on Modulating Angry Driving Based on an EEG Connectivity Study. International Journal of Neural Systems. 34(11). 2450058–2450058. 3 indexed citations
6.
Hu, Fo, Lekai Zhang, Xusheng Yang, & Wen‐An Zhang. (2024). EEG-Based Driver Fatigue Detection Using Spatio-Temporal Fusion Network With Brain Region Partitioning Strategy. IEEE Transactions on Intelligent Transportation Systems. 25(8). 9618–9630. 30 indexed citations
7.
Hu, Fo, et al.. (2024). A Novel Multi-Feature Fusion Network With Spatial Partitioning Strategy and Cross-Attention for Armband-Based Gesture Recognition. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 32. 3878–3890. 8 indexed citations
8.
Zhang, Lekai, Fo Hu, Xingyu Liu, et al.. (2024). Intelligent emotion recognition in product design using multimodal physiological signals and machine learning. Journal of Engineering Design. 36(5-6). 836–856. 4 indexed citations
9.
Hu, Fo, et al.. (2024). TFN-FICFM: sEMG-Based Gesture Recognition Using Temporal Fusion Network and Fuzzy Integral-based Classifier Fusion. Journal of Bionic Engineering. 21(4). 1878–1891. 5 indexed citations
10.
Zhang, Lekai, et al.. (2023). The power of humorous audio: exploring emotion regulation in traffic congestion through EEG-based study. EURASIP Journal on Audio Speech and Music Processing. 2023(1). 1 indexed citations
11.
Lu, Yanzheng, et al.. (2021). Effective recognition of human lower limb jump locomotion phases based on multi-sensor information fusion and machine learning. Medical & Biological Engineering & Computing. 59(4). 883–899. 27 indexed citations
12.
Wang, Hong, et al.. (2021). Recognition of lower limb movements using empirical mode decomposition and k-nearest neighbor entropy estimator with surface electromyogram signals. Biomedical Signal Processing and Control. 71. 103198–103198. 18 indexed citations
13.
Hu, Fo, et al.. (2021). Hybrid Graph Convolutional Networks for Skeleton-Based and EEG-Based Jumping Action Recognition. 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). 4156–4161. 3 indexed citations
14.
Wang, Hong, et al.. (2020). Using convolutional neural networks to decode EEG-based functional brain network with different severity of acrophobia. Journal of Neural Engineering. 18(1). 16007–16007. 9 indexed citations
15.
Zhou, Bin, et al.. (2020). Accurate recognition of lower limb ambulation mode based on surface electromyography and motion data using machine learning. Computer Methods and Programs in Biomedicine. 193. 105486–105486. 37 indexed citations
16.
Hu, Fo, et al.. (2020). Decoding of voluntary and involuntary upper-limb motor imagery based on graph fourier transform and cross-frequency coupling coefficients. Journal of Neural Engineering. 17(5). 56043–56043. 10 indexed citations
17.
Wang, Hong, et al.. (2019). A fiber-reinforced human-like soft robotic manipulator based on sEMG force estimation. Engineering Applications of Artificial Intelligence. 86. 56–67. 15 indexed citations
18.
Wang, Hong, et al.. (2019). Are you afraid of heights and suitable for working at height?. Biomedical Signal Processing and Control. 52. 23–31. 13 indexed citations
19.
Lu, Yanzheng, Hong Wang, Bin Zhou, et al.. (2019). Human’s Jump State Recognition Based on Surface Electromyography and Wearable Plantar-ground Contact Sensor. 173. 242–247. 1 indexed citations
20.
Wang, Hong, et al.. (2018). Humanoid Soft Hand Design Based on sEMG Control. 20. 187–191. 1 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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