Lang He

43 papers receiving 1.1k citations

Lang He's Hit Papers

Deep learning for depression recognition with audiovisual cues: A review 2021 · 145 citations
1450+1+3Years since publication4080120

Peers

Lang He
Comparison fields: 5 of 99
  • Experimental and Cognitive Psychology 662
  • Applied Psychology 181
  • Social Psychology 333
  • Cognitive Neuroscience 210
  • Computer Vision and Pattern Recognition 211
Replace Yekta Said Can with:
Yekta Said Can Türkiye
Ziping Zhao China
Matthew Pediaditis Greece
Meshia Cédric Oveneke Belgium
Claus Marberger Germany
Panagiotis Tzirakis United Kingdom
Lin Shu China
Heysem Kaya Türkiye
Miguel Bordallo López Finland
Patrícia Bota Portugal
Lang He relative to Yekta Said Can Türkiye Yekta Said Can's profile →
Citations per field
00.5×10×13.5×
Yekta Said Can · 1×
Citations per year

Countries citing papers authored by Lang He

Since Specialization
Citations

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

Fields of papers citing papers by Lang He

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Lang He, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Lang He Line = papers co-authored together Lang He links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 52 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2018165
2
Deep learning for depression recognition with audiovisual cues: A review
Hit paper breakdown →
2021145
3 2020109
4 2016104
5 201599
6 201871
7 202147
8 201842
9 201540
10 202127
11 202227
12 202225
13 201824
14 202218
15 201917
16 201815
17 202112
18 202411
19 202211
20 201911

About Lang He

Lang He is a scholar working on Experimental and Cognitive Psychology, Computer Vision and Pattern Recognition, Social Psychology, Artificial Intelligence and Cognitive Neuroscience, having authored 52 papers that have together received 1.1k indexed citations. Recurring topics across this work include Emotion and Mood Recognition (25 papers), Mental Health via Writing (11 papers), EEG and Brain-Computer Interfaces (8 papers), Digital Mental Health Interventions (6 papers), Functional Brain Connectivity Studies (6 papers), Anomaly Detection Techniques and Applications (4 papers), Mental Health Research Topics (4 papers) and Sentiment Analysis and Opinion Mining (3 papers). The work is most often cited by research in Experimental and Cognitive Psychology (662 citations), Applied Psychology (181 citations), Social Psychology (333 citations), Cognitive Neuroscience (210 citations) and Computer Vision and Pattern Recognition (211 citations). Lang He has collaborated with scholars based in China, Belgium and Finland. Frequent co-authors include Dongmei Jiang, Hichem Sahli, Zhongmin Wang, Ercheng Pei, Jonathan Cheung-Wai Chan, Le Yang, Prayag Tiwari, Mingyue Niu, Rui Su and Wei Dang. Their work appears in journals such as Biomedical Signal Processing and Control, Information Fusion, IEEE Internet of Things Journal, Knowledge-Based Systems and IEEE Access.

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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