Farideh Ebrahimi

815 total citations
18 papers, 588 citations indexed

About

Farideh Ebrahimi is a scholar working on Cognitive Neuroscience, Biomedical Engineering and Cardiology and Cardiovascular Medicine. According to data from OpenAlex, Farideh Ebrahimi has authored 18 papers receiving a total of 588 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Cognitive Neuroscience, 7 papers in Biomedical Engineering and 4 papers in Cardiology and Cardiovascular Medicine. Recurrent topics in Farideh Ebrahimi's work include EEG and Brain-Computer Interfaces (13 papers), Non-Invasive Vital Sign Monitoring (6 papers) and Blind Source Separation Techniques (4 papers). Farideh Ebrahimi is often cited by papers focused on EEG and Brain-Computer Interfaces (13 papers), Non-Invasive Vital Sign Monitoring (6 papers) and Blind Source Separation Techniques (4 papers). Farideh Ebrahimi collaborates with scholars based in Iran, United States and Czechia. Farideh Ebrahimi's co-authors include H. Nazeran, Edson Estrada, Mohammad Mikaeili, Seyed Kamaledin Setarehdan, Gustavo Martínez-Sierra, Jalil Rasekhi, Mohammad Mikaili, Radek Martínek and Afsaneh Dehnad and has published in prestigious journals such as SHILAP Revista de lepidopterología, Expert Systems with Applications and Computer Methods and Programs in Biomedicine.

In The Last Decade

Farideh Ebrahimi

17 papers receiving 569 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Farideh Ebrahimi Iran	10	442	148	144	104	77	18	588
Javad Birjandtalab United States	10	356 0.8×	142 1.0×	138 1.0×	168 1.6×	65 0.8×	23	579
Dominic Heger Germany	12	548 1.2×	82 0.6×	234 1.6×	90 0.9×	48 0.6×	27	772
Wenliang Che China	11	376 0.9×	114 0.8×	42 0.3×	139 1.3×	87 1.1×	14	584
Stanislas Chambon France	4	444 1.0×	104 0.7×	88 0.6×	63 0.6×	157 2.0×	7	574
Takashi Nakamura United Kingdom	10	334 0.8×	51 0.3×	189 1.3×	93 0.9×	86 1.1×	19	534
Vipin Gupta India	11	520 1.2×	260 1.8×	74 0.5×	165 1.6×	190 2.5×	22	702
Farhad Faradji Iran	8	223 0.5×	91 0.6×	58 0.4×	38 0.4×	73 0.9×	19	474
Phattarapong Sawangjai Thailand	6	299 0.7×	76 0.5×	52 0.4×	55 0.5×	43 0.6×	10	402
Anwesha Khasnobish India	12	353 0.8×	85 0.6×	118 0.8×	36 0.3×	61 0.8×	54	496
Muhammad Zulkifal Aziz China	10	495 1.1×	133 0.9×	74 0.5×	106 1.0×	31 0.4×	22	603

Countries citing papers authored by Farideh Ebrahimi

Since Specialization

Citations

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

Fields of papers citing papers by Farideh Ebrahimi

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Farideh Ebrahimi

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

All Works

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18 of 18 papers shown

Ebrahimi, Farideh, et al.. (2024). Sudden Cardiac Death Prediction by Fusing Electrocardiogram and Heart Rate Variability Signals. International Journal of Engineering. 38(1). 86–98. 1 indexed citations

Ebrahimi, Farideh, et al.. (2024). Exploring Students’ Perceptions of Learning Medical Terminology With Teacher-Made Flashcards Used Electronically. SHILAP Revista de lepidopterología.

Ebrahimi, Farideh, et al.. (2022). A review of critical challenges in MI-BCI: From conventional to deep learning methods. Journal of Neuroscience Methods. 383. 109736–109736. 70 indexed citations

Ebrahimi, Farideh, et al.. (2022). A transfer learning-based CNN and LSTM hybrid deep learning model to classify motor imagery EEG signals. Computers in Biology and Medicine. 143. 105288–105288. 117 indexed citations

Ebrahimi, Farideh, et al.. (2022). Generalizable epileptic seizures prediction based on deep transfer learning. Cognitive Neurodynamics. 17(1). 119–131. 20 indexed citations

Ebrahimi, Farideh, et al.. (2021). Automatic sleep staging by cardiorespiratory signals: a systematic review. Sleep And Breathing. 26(2). 965–981. 11 indexed citations

Ebrahimi, Farideh, et al.. (2019). Investigating medical students’ readiness for technology-mediated autonomous learning situations in ESP programs. Education and Information Technologies. 24(6). 3289–3309. 1 indexed citations

Ebrahimi, Farideh, et al.. (2019). A hierarchical structure based on Stacking approach for skin lesion classification. Expert Systems with Applications. 145. 113127–113127. 45 indexed citations

Ebrahimi, Farideh, Seyed Kamaledin Setarehdan, Radek Martínek, & H. Nazeran. (2017). Comparative Analysis of the Discriminative Capacity of EEG, Two ECG-Derived and Respiratory Signals in Automatic Sleep Staging. Advances in Electrical and Electronic Engineering. 15(3). 5 indexed citations

10.

Ebrahimi, Farideh, et al.. (2014). Automatic sleep staging by simultaneous analysis of ECG and respiratory signals in long epochs. Biomedical Signal Processing and Control. 18. 69–79. 29 indexed citations

11.

Ebrahimi, Farideh, et al.. (2013). Automatic sleep staging using empirical mode decomposition, discrete wavelet transform, time-domain, and nonlinear dynamics features of heart rate variability signals. Computer Methods and Programs in Biomedicine. 112(1). 47–57. 69 indexed citations

12.

Ebrahimi, Farideh, et al.. (2012). DFA- and DWT- based features of HRV signal for automatic sleep staging. 4 indexed citations

13.

Estrada, Edson, H. Nazeran, Gustavo Martínez-Sierra, Farideh Ebrahimi, & Seyed Kamaledin Setarehdan. (2011). Wavelet-based EEG denoising for automatic sleep stage classification. 295–298. 37 indexed citations

14.

Ebrahimi, Farideh & Mohammad Mikaili. (2010). ASSESSMENT OF TIME, FREQUENCY, AND WAVELET PACKET TRANSFORM FEATURES EXTRACTED FROM EEG FOR SLEEP STAGING USING SELF ORGANIZING MAPS (SOM). 4(2). 0–0. 1 indexed citations

15.

Estrada, Edson, H. Nazeran, Farideh Ebrahimi, & Mohammad Mikaeili. (2009). EEG signal features for computer-aided sleep stage detection. 5. 669–672. 9 indexed citations

16.

Estrada, Edson, H. Nazeran, Farideh Ebrahimi, & Mohammad Mikaeili. (2009). Symmetric Itakura Distance as an EEG Signal Feature for Sleep Depth Determination. 723–724. 8 indexed citations

17.

Ebrahimi, Farideh, Mohammad Mikaeili, Edson Estrada, & H. Nazeran. (2008). Automatic sleep stage classification based on EEG signals by using neural networks and wavelet packet coefficients. PubMed. 2008. 1151–1154. 153 indexed citations

18.

Ebrahimi, Farideh, Mohammad Mikaili, Edson Estrada, & H. Nazeran. (2007). Assessment of Itakura Distance as a Valuable Feature for Computer-aided Classification of Sleep Stages. Conference proceedings. 2007. 3300–3303. 8 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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