
@article{abadi_decaf_2015,
	title = {{DECAF}: {MEG}-{Based} {Multimodal} {Database} for {Decoding} {Affective} {Physiological} {Responses}},
	volume = {6},
	issn = {1949-3045},
	shorttitle = {{DECAF}},
	url = {https://ieeexplore.ieee.org/abstract/document/7010926},
	doi = {10.1109/TAFFC.2015.2392932},
	language = {en},
	number = {3},
	urldate = {2024-04-30},
	journal = {IEEE Transactions on Affective Computing},
	author = {Abadi, Mojtaba Khomami and Subramanian, Ramanathan and Kia, Seyed Mostafa and Avesani, Paolo and Patras, Ioannis and Sebe, Nicu},
	month = jul,
	year = {2015},
	note = {Conference Name: IEEE Transactions on Affective Computing},
	keywords = {affective computing, Affective computing, Databases, Educational institutions, Electrocardiography, Electroencephalography, Emotion recognition, MEG, Motion pictures, Physiology, single-trial classification, Single-trial classification, user physiological responses, User physiological responses},
	pages = {209--222},
}

@article{bota_biosppy_2024,
	title = {{BioSPPy}: {A} {Python} toolbox for physiological signal processing},
	volume = {26},
	issn = {2352-7110},
	shorttitle = {{BioSPPy}},
	url = {https://www.sciencedirect.com/science/article/pii/S2352711024000839},
	doi = {10.1016/j.softx.2024.101712},
	language = {en},
	urldate = {2024-04-30},
	journal = {SoftwareX},
	author = {Bota, Patrícia and Silva, Rafael and Carreiras, Carlos and Fred, Ana and da Silva, Hugo Plácido},
	month = may,
	year = {2024},
	keywords = {Biomedical research, Feature extraction, Physiological signals, Python, Signal processing, Time series},
	pages = {101712},
}

@article{chen_emotion_2022,
	title = {Emotion {Recognition} {With} {Audio}, {Video}, {EEG}, and {EMG}: {A} {Dataset} and {Baseline} {Approaches}},
	volume = {10},
	issn = {2169-3536},
	shorttitle = {Emotion {Recognition} {With} {Audio}, {Video}, {EEG}, and {EMG}},
	url = {https://ieeexplore.ieee.org/abstract/document/9693926},
	doi = {10.1109/ACCESS.2022.3146729},
	language = {en},
	urldate = {2024-04-30},
	journal = {IEEE Access},
	author = {Chen, Jin and Ro, Tony and Zhu, Zhigang},
	year = {2022},
	note = {Conference Name: IEEE Access},
	keywords = {data collection, electroencephalography, Electroencephalography, electromyography, Electromyography, Emotion recognition, Feature extraction, Physiology, Support vector machines, Videos},
	pages = {13229--13242},
}

@article{de_luca_practicum_2008,
	title = {A practicum on the use of {sEMG} signals in movement sciences.},
	journal = {Delsys Inc.},
	author = {De Luca, C. J.},
	year = {2008},
}

@article{eyben_geneva_2016,
	title = {The {Geneva} {Minimalistic} {Acoustic} {Parameter} {Set} ({GeMAPS}) for {Voice} {Research} and {Affective} {Computing}},
	volume = {7},
	issn = {1949-3045},
	url = {https://ieeexplore.ieee.org/abstract/document/7160715},
	doi = {10.1109/TAFFC.2015.2457417},
	language = {en},
	number = {2},
	urldate = {2024-04-30},
	journal = {IEEE Transactions on Affective Computing},
	author = {Eyben, Florian and Scherer, Klaus R. and Schuller, Björn W. and Sundberg, Johan and André, Elisabeth and Busso, Carlos and Devillers, Laurence Y. and Epps, Julien and Laukka, Petri and Narayanan, Shrikanth S. and Truong, Khiet P.},
	month = apr,
	year = {2016},
	note = {Conference Name: IEEE Transactions on Affective Computing},
	keywords = {acoustic features, Acoustic Features, Affective computing, Affective Computing, emotion recognition, Emotion Recognition, Frequency measurement, geneva minimalistic parameter set, Geneva Minimalistic Parameter Set, Harmonic analysis, Licenses, Mel frequency cepstral coefficient, Speech, speech analysis, Speech Analysis, standard, Standard, Standards},
	pages = {190--202},
}

@article{fridlund_guidelines_1986,
	title = {Guidelines for {Human} {Electromyographic} {Research}},
	volume = {23},
	issn = {1469-8986},
	url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1469-8986.1986.tb00676.x},
	doi = {10.1111/j.1469-8986.1986.tb00676.x},
	language = {en},
	number = {5},
	urldate = {2024-04-30},
	journal = {Psychophysiology},
	author = {Fridlund, Alan J. and Cacioppo, John T.},
	year = {1986},
	note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1469-8986.1986.tb00676.x},
	keywords = {Apparatus, Biofeedback, Electromyography, Electrophysiological recording, Experimental design, Facial expression, Facial muscles, Instrumentation, Muscles, Statistical analysis},
	pages = {567--589},
	file = {Full Text PDF:C\:\\Users\\willi\\Zotero\\storage\\H64S2N83\\Fridlund and Cacioppo - 1986 - Guidelines for Human Electromyographic Research.pdf:application/pdf;Snapshot:C\:\\Users\\willi\\Zotero\\storage\\NLWTMFVK\\j.1469-8986.1986.tb00676.html:text/html},
}

@Inbook{gabrieli_pysiology_2020,
	author="Gabrieli, Giulio
	and Azhari, Atiqah
	and Esposito, Gianluca",
	editor="Esposito, Anna
	and Faundez-Zanuy, Marcos
	and Morabito, Francesco Carlo
	and Pasero, Eros",
	title="PySiology: A Python Package for Physiological Feature Extraction",
	bookTitle="Neural Approaches to Dynamics of Signal Exchanges",
	year="2020",
	publisher="Springer Singapore",
	address="Singapore",
	pages="395--402",
	abstract="Physiological signals have been widely used to measure continuous data from the autonomic nervousGabrieli, Giulio system in the fields of computer science, psychology, and human--computer interaction. Signal processing andAzhari, Atiqah feature estimation of physiological measurements can beEsposito, Gianluca performed with several commercial tools. Unfortunately, those tools possess a steep learning curve and do not usually allow for complete customization of estimation parameters. For these reasons, we designed PySiology, an open-source package for the estimation of features from physiological signals, suitable for both novice and expert users. This package provides clear documentation of utilized methodology, guided functionalities for semi-automatic feature estimation, and options for extensive customization. In this article, a brief introduction to the features of the package, and to its design workflow, are presented. To demonstrate the usage of the package in a real-world context, an advanced example of image valence estimation from physiological measurements (ECG, EMG, and EDA) is described. Preliminary tests have shown high reliability of feature estimated using PySiology.",
	isbn="978-981-13-8950-4",
	doi="10.1007/978-981-13-8950-4_35",
	url="https://doi.org/10.1007/978-981-13-8950-4_35"
}

@misc{greene_peakaffectds_2022,
	title = {{PeakAffectDS}},
	url = {https://zenodo.org/records/6403363},
	doi = {10.5281/zenodo.6403363},
	language = {eng},
	urldate = {2024-04-30},
	publisher = {Zenodo},
	author = {Greene, Nick and Livingstone, Steven R. and Szymanski, Lech},
	month = mar,
	year = {2022},
	keywords = {dataset, deep learning, emotion, machine learning, physiology},
}

@article{hegedus_adaptation_2020,
	title = {Adaptation of {Fatigue} {Affected} {Changes} in {Muscle} {EMG} {Frequency} {Characteristics} for the {Determination} of {Training} {Load} in {Physical} {Therapy} for {Cancer} {Patients}},
	volume = {26},
	issn = {1532-2807},
	url = {https://doi.org/10.1007/s12253-019-00668-3},
	doi = {10.1007/s12253-019-00668-3},
	language = {en},
	number = {2},
	urldate = {2024-04-30},
	journal = {Pathology \& Oncology Research},
	author = {Hegedus, Adam and Trzaskoma, Lukasz and Soldos, Peter and Tuza, Kornelia and Katona, Peter and Greger, Zsolt and Zsarnoczky-Dulhazi, Fanni and Kopper, Bence},
	month = apr,
	year = {2020},
	keywords = {Cancer treatment, physical rehabilitation, neuromuscular fatigue, Isometric, Motor unit activation},
	pages = {1129--1135},
}

@article{koelstra_deap_2012,
	title = {{DEAP}: {A} {Database} for {Emotion} {Analysis} ;{Using} {Physiological} {Signals}},
	volume = {3},
	issn = {1949-3045},
	shorttitle = {{DEAP}},
	url = {https://ieeexplore.ieee.org/abstract/document/5871728},
	doi = {10.1109/T-AFFC.2011.15},
	abstract = {We present a multimodal data set for the analysis of human affective states. The electroencephalogram (EEG) and peripheral physiological signals of 32 participants were recorded as each watched 40 one-minute long excerpts of music videos. Participants rated each video in terms of the levels of arousal, valence, like/dislike, dominance, and familiarity. For 22 of the 32 participants, frontal face video was also recorded. A novel method for stimuli selection is proposed using retrieval by affective tags from the last.fm website, video highlight detection, and an online assessment tool. An extensive analysis of the participants' ratings during the experiment is presented. Correlates between the EEG signal frequencies and the participants' ratings are investigated. Methods and results are presented for single-trial classification of arousal, valence, and like/dislike ratings using the modalities of EEG, peripheral physiological signals, and multimedia content analysis. Finally, decision fusion of the classification results from different modalities is performed. The data set is made publicly available and we encourage other researchers to use it for testing their own affective state estimation methods.},
	number = {1},
	urldate = {2024-04-30},
	journal = {IEEE Transactions on Affective Computing},
	author = {Koelstra, Sander and Muhl, Christian and Soleymani, Mohammad and Lee, Jong-Seok and Yazdani, Ashkan and Ebrahimi, Touradj and Pun, Thierry and Nijholt, Anton and Patras, Ioannis},
	month = jan,
	year = {2012},
	note = {Conference Name: IEEE Transactions on Affective Computing},
	keywords = {affective computing., Databases, EEG, Electroencephalography, Emotion classification, Face, Motion pictures, Multimedia communication, pattern classification, physiological signals, signal processing, Videos, Visualization},
	pages = {18--31},
}

@article{lindstrom1977electromyographic,
  title={An electromyographic index for localized muscle fatigue},
  doi = {10.1152/jappl.1977.43.4.750},
  author={Lindstrom, L\_ and Kadefors, ROLAND and Petersen, INGEMAR},
  journal={Journal of applied physiology},
  volume={43},
  number={4},
  pages={750--754},
  year={1977}
}

@article{livingstone_deficits_2016,
	title = {Deficits in the {Mimicry} of {Facial} {Expressions} in {Parkinson}'s {Disease}},
	volume = {7},
	issn = {1664-1078},
	url = {https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2016.00780/full},
	doi = {10.3389/fpsyg.2016.00780},
	language = {English},
	urldate = {2024-04-30},
	journal = {Frontiers in Psychology},
	author = {Livingstone, Steven R. and Vezer, Esztella and McGarry, Lucy M. and Lang, Anthony E. and Russo, Frank A.},
	month = jun,
	year = {2016},
	note = {Publisher: Frontiers},
	keywords = {emotion, facial expressions, Facial mimicry, Hypomimia, masked face, masked facies, mimicry, Parkinson’s disease, Rapid facial reactions},
}

@article{makowski_neurokit2_2021,
	title = {{NeuroKit2}: {A} {Python} toolbox for neurophysiological signal processing},
	volume = {53},
	issn = {1554-3528},
	shorttitle = {{NeuroKit2}},
	url = {https://doi.org/10.3758/s13428-020-01516-y},
	doi = {10.3758/s13428-020-01516-y},
	language = {en},
	number = {4},
	urldate = {2024-04-30},
	journal = {Behavior Research Methods},
	author = {Makowski, Dominique and Pham, Tam and Lau, Zen J. and Brammer, Jan C. and Lespinasse, François and Pham, Hung and Schölzel, Christopher and Chen, S. H. Annabel},
	month = aug,
	year = {2021},
	keywords = {Biosignals, ECG, EDA, EMG, Neurophysiology, Python},
	pages = {1689--1696},
}

@article{mcmanus_analysis_2020,
	title = {Analysis and {Biophysics} of {Surface} {EMG} for {Physiotherapists} and {Kinesiologists}: {Toward} a {Common} {Language} {With} {Rehabilitation} {Engineers}},
	volume = {11},
	issn = {1664-2295},
	shorttitle = {Analysis and {Biophysics} of {Surface} {EMG} for {Physiotherapists} and {Kinesiologists}},
	url = {https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2020.576729/full},
	doi = {10.3389/fneur.2020.576729},
	language = {English},
	urldate = {2024-04-30},
	journal = {Frontiers in Neurology},
	author = {McManus, Lara and De Vito, Giuseppe and Lowery, Madeleine M.},
	month = oct,
	year = {2020},
	note = {Publisher: Frontiers},
	keywords = {clinical application, Kinesiology, Physiotherapy, Rehabilitation, Surface Electromography, tutorial},
}

@misc{mermaidjs,
  title        = {{Mermaid.js}: Generation of diagrams and flowcharts from text},
  author       = {Sveidqvist, Knut and the Mermaid Contributors},
  howpublished = {\url{https://mermaid-js.github.io}},
  note         = {Version 11.6.x, accessed April 2025}
}

@article{phinyomark2009novel,
  title={A novel feature extraction for robust EMG pattern recognition},
  doi = {10.48550/arXiv.0912.3973},
  author={Phinyomark, Angkoon and Limsakul, Chusak and Phukpattaranont, Pornchai},
  journal={arXiv preprint arXiv:0912.3973},
  year={2009}
}

@article{sato_emotional_2021,
	title = {Emotional valence sensing using a wearable facial {EMG} device},
	volume = {11},
	copyright = {2021 The Author(s)},
	issn = {2045-2322},
	url = {https://www.nature.com/articles/s41598-021-85163-z},
	doi = {10.1038/s41598-021-85163-z},
	language = {en},
	number = {1},
	urldate = {2024-04-30},
	journal = {Scientific Reports},
	author = {Sato, Wataru and Murata, Koichi and Uraoka, Yasuyuki and Shibata, Kazuaki and Yoshikawa, Sakiko and Furuta, Masafumi},
	month = mar,
	year = {2021},
	note = {Publisher: Nature Publishing Group},
	keywords = {Human behaviour, Neurophysiology},
	pages = {5757},
}

@inproceedings{schmidt_introducing_2018,
	address = {New York, NY, USA},
	series = {{ICMI} '18},
	title = {Introducing {WESAD}, a {Multimodal} {Dataset} for {Wearable} {Stress} and {Affect} {Detection}},
	isbn = {978-1-4503-5692-3},
	url = {https://doi.org/10.1145/3242969.3242985},
	doi = {10.1145/3242969.3242985},
	urldate = {2024-04-30},
	booktitle = {Proceedings of the 20th {ACM} {International} {Conference} on {Multimodal} {Interaction}},
	publisher = {Association for Computing Machinery},
	author = {Schmidt, Philip and Reiss, Attila and Duerichen, Robert and Marberger, Claus and Van Laerhoven, Kristof},
	month = oct,
	year = {2018},
	keywords = {affective computing, benchmark, emotion recognition, multimodal dataset, sensor fusion, stress detection, user study},
	pages = {400--408},
}

@article{sharma_dataset_2019,
	title = {A dataset of continuous affect annotations and physiological signals for emotion analysis},
	volume = {6},
	copyright = {2019 The Author(s)},
	issn = {2052-4463},
	url = {https://www.nature.com/articles/s41597-019-0209-0},
	doi = {10.1038/s41597-019-0209-0},
	language = {en},
	number = {1},
	urldate = {2024-04-30},
	journal = {Scientific Data},
	author = {Sharma, Karan and Castellini, Claudio and van den Broek, Egon L. and Albu-Schaeffer, Alin and Schwenker, Friedhelm},
	month = oct,
	year = {2019},
	note = {Publisher: Nature Publishing Group},
	keywords = {Biomedical engineering, Cognitive neuroscience, Electrocardiography – EKG, Electromyography – EMG, Physiology},
	pages = {196},
}

@software{sjak-shie_physiodata_2022,
	title = {{PhysioData} {Toolbox}},
	url = {https://physiodatatoolbox.leidenuniv.nl/},
	urldate = {2024-07-15},
	author = {Sjak-Shie},
	version = {0.6.3},
	year = {2022},
}

@article{soleymani_toolbox_2017,
	title = {Toolbox for {Emotional} {feature} extraction from {Physiological} signals ({TEAP})},
	volume = {4},
	issn = {2297-198X},
	url = {https://www.frontiersin.org/articles/10.3389/fict.2017.00001},
	doi = {10.3389/fict.2017.00001},
	language = {English},
	urldate = {2024-04-30},
	journal = {Frontiers in ICT},
	author = {Soleymani, Mohammad and Villaro-Dixon, Frank and Pun, Thierry and Chanel, Guillaume},
	month = feb,
	year = {2017},
	note = {Publisher: Frontiers},
	keywords = {Affective Computing, code:matlab, code:octave, Electroencephalogram, Emotions, physiological signals, Signal processing, Toolbox},
}

@article{stepp_surface_2012,
	title = {Surface {Electromyography} for {Speech} and {Swallowing} {Systems}: {Measurement}, {Analysis}, and {Interpretation}},
	volume = {55},
	shorttitle = {Surface {Electromyography} for {Speech} and {Swallowing} {Systems}},
	url = {https://pubs.asha.org/doi/abs/10.1044/1092-4388%282011/11-0214%29},
	doi = {10.1044/1092-4388(2011/11-0214)},
	number = {4},
	urldate = {2024-04-30},
	journal = {Journal of Speech, Language, and Hearing Research},
	author = {Stepp, Cara E.},
	month = aug,
	year = {2012},
	note = {Publisher: American Speech-Language-Hearing Association},
	keywords = {electromyography, respiration, speech, swallowing, voice},
	pages = {1232--1246},
}

@article{tamietto_unseen_2009,
	title = {Unseen facial and bodily expressions trigger fast emotional reactions},
	volume = {106},
	url = {https://www.pnas.org/doi/abs/10.1073/pnas.0908994106},
	doi = {10.1073/pnas.0908994106},
	number = {42},
	urldate = {2024-04-30},
	journal = {Proceedings of the National Academy of Sciences},
	author = {Tamietto, Marco and Castelli, Lorys and Vighetti, Sergio and Perozzo, Paola and Geminiani, Giuliano and Weiskrantz, Lawrence and de Gelder, Beatrice},
	month = oct,
	year = {2009},
	note = {Publisher: Proceedings of the National Academy of Sciences},
	pages = {17661--17666},
}

@article{tkach_study_2010,
	title = {Study of stability of time-domain features for electromyographic pattern recognition},
	volume = {7},
	issn = {1743-0003},
	url = {https://doi.org/10.1186/1743-0003-7-21},
	doi = {10.1186/1743-0003-7-21},
	language = {en},
	number = {1},
	urldate = {2024-04-30},
	journal = {Journal of NeuroEngineering and Rehabilitation},
	author = {Tkach, Dennis and Huang, He and Kuiken, Todd A.},
	month = may,
	year = {2010},
	keywords = {Average Classification Accuracy, Cepstrum Coefficient, Classification Accuracy, Classification Performance, Muscle Fatigue},
	pages = {21},
}

@article{van1983changes,
  title={Changes in electromyogram power spectra of facial and jaw-elevator muscles during fatigue},
  doi = {10.1152/jappl.1983.54.1.51},
  author={Van Boxtel, A and Goudswaard, P and Van der Molen, GM and Van Den Bosch, WE},
  journal={Journal of Applied Physiology},
  volume={54},
  number={1},
  pages={51--58},
  year={1983}
}

@misc{vitepress,
  title        = {{VitePress}: Vite \& Vue powered static-site generator},
  author       = {You, Evan and the VitePress Contributors},
  howpublished = {\url{https://vitepress.dev}},
  note         = {1.6.3, accessed April 2025}
}

@article{virtanen_scipy_2020,
  title={SciPy 1.0: fundamental algorithms for scientific computing in Python},
  author={Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E and Haberland, Matt and Reddy, Tyler and Cournapeau, David and Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and Bright, Jonathan and others},
  journal={Nature methods},
  volume={17},
  number={3},
  pages={261--272},
  year={2020},
  publisher={Nature Publishing Group US New York}
}

@inproceedings{zhang_biovid_2016,
	title = {“{BioVid} {Emo} {DB}”: {A} multimodal database for emotion analyses validated by subjective ratings},
	shorttitle = {“{BioVid} {Emo} {DB}”},
	url = {https://ieeexplore.ieee.org/abstract/document/7849931},
	doi = {10.1109/SSCI.2016.7849931},
	urldate = {2024-04-30},
	booktitle = {2016 {IEEE} {Symposium} {Series} on {Computational} {Intelligence} ({SSCI})},
	author = {Zhang, Lin and Walter, Steffen and Ma, Xueyao and Werner, Philipp and Al-Hamadi, Ayoub and Traue, Harald C. and Gruss, Sascha},
	month = dec,
	year = {2016},
	keywords = {Cameras, data mining, Data mining, database, Databases, discrete emotions, Electrodes, emotion recognition, film clips, Films, Skin, Streaming media},
	pages = {1--6},
}
