INTERNAL PROJECT PROPOSALS
Music is usually employed as a stimulus to induce emotions in a listener.
Induced emotions are strongly dependent from the listener's musical preferences, their mood while listening to a song, their experience, and numerous other factors.
The appreciation for a specific song could be bounded non only to subjective factors, but also to objective characteristics of the song.
What is the general variation of the song? Is it instrumental or is there a singer giving voice to the melody? Are the lyrics understandable?
The proposed activities have two objectives: (i) prepare a questionnaire to allow volunteers to evaluate subjectively the complexity of popular songs, while considering the variation of their complexity over time, and (ii) devise some objective measures to evaluate if there is a link between the complexity of the audio signal (instrumental) and the listener preferences.erazione delle variazioni di complessità nel tempo.
The student will work on a dataset presenting the top 10 songs for each year starting from the Fifties.
The student will have the opportunity to work with a multidisciplinary group constituted by experts in the analyses of multimedia signals, Prof. Francesca Gasparini and Dr. Aurora Saibene of the MMSP lab, and experts in the field of psychology and neuroscience of music, Dr. Laura Ferreri and Dr. Carlotta Lega of the University of Pavia.
For more information, please contact Aurora Saibene.
The goal of this project is to analyze a public dataset of electronic health records of 425 patients diagnosed with depression who also suffered from heart failure. By applying computational statistics and supervised machine learning methods for feature ranking, we would like to detect what clinical features result being more correlated with heart failure, and which clinical features result being more correlated with depression. Computational methods will include traditional biostatistics tests (Student's t-test, Mann-Whitney U test, chi-squared test, etc.), and supervised machine learning approaches (for example, Random Forests, XGBoost, or others) for feature ranking. The feature ranking step will be carried out through feature recursive elimination (RFE), Shapley feature importance method, or others. The project will be carried out through open source programming languages such as R or Python. In case of interesting results, we will consider the possibility to write a scientific article to submit to journals such as BMC Medical Informatics or BioData Mining, or others.
For more information contact Davide Chicco.
Electroencephalography (EEG) has been exploited in rehabilitation systems of lower and upper limbs, having that the response from the motor cortex area presents specific synchronization and desynchronization in specific frequency ranges, and that the rehearsal of movements present the same neural patterns when compared with their executed counterparts.
Galvanic Skin Response (GSR) and photoplethysmographic (PPG) signals are good indicators of cognitive overload and stress, which can arise while performing motor imagery (MI) experiments due to the difficulty of these tasks and the necessity of repeating these activities multiple times to develop reliable Brain-Computer Interfaces (BCIs).
This thesis proposal has three main objectives: 1) find a literature corpus regarding MI in the EEG domain and understand how the use of GSR and PPG signals may improve MI-based BCIs, 2) design an experimental protocol for a preliminary study on single upper limb movement (e.g., hand grasping and flexing), exploiting EEG, GSR and PPG wearable devices, and 3) propose a second experimental session during which a feedback will be provided to the subjects while imaging the required movements in form of a simple game.
For more information contact Aurora Saibene.
While emotions are neural responses to specific feeling or external stimuli, the mood is a persisting state that is not necessarily tide to a specific stimulus and that may present different feelings and evoked emotions. However, a clear and well-established definition of mood in respect to emotions and feelings has yet to be found.
Different physiological signals have been demonstrated to be associated with specific emotions, while virtual reality (VR) environments have proved their ability to elicit specific emotions.
This thesis proposal has three main objectives: 1) moving from emotion to mood detection considering the use of wearable sensing technologies, 2) define specific scenarios to reproduce in VR environments, 3) provide a tentative analysis of the data derived from experimental sessions with the main objective of providing a mood manipulation.
This thesis will be performed in the context of an open collaboration with Professor Francesco Ferrise of the Department of Mechanical Engineering, Politecnico di Milano.
For more information contact Francesca Gasparini and Aurora Saibene.
Electroencephalography (EEG) has been exploited in rehabilitation systems of lower and upper limbs, having that the response from the motor cortex area presents specific synchronization and desynchronization in specific frequency ranges. Unfortunately, EEG signals can record only the surface activity and may be unable to provide evidence regarding intricate movements.
Electromyographic (EMG) signals are instead able to acquire the muscular activity with more precision and may thus represent an ally to provide a more in-depth assessment of movements.
This thesis proposal has three main objectives: 1) find a literature corpus regarding EEG and EMG signals (used separately or considering sensor fusion) used for upper or lower limb movement recognition, 2) design an experimental protocol for a preliminary study on single upper or lower limb movement (e.g., foot flexion, hand gestures), exploiting EEG and EMG wearable devices, and 3) propose a second experimental session during which a feedback will be provided to the subjects while executing or imaging the required movements.
For more information contact Aurora Saibene.
Emotion recognition has been drawing attention from multidisciplinary research communities and has been relying increasingly on artificial intelligence strategies to better model dependencies between cognition and physiological processes.
Physiological signals like Galvanic Skin Response (GSR) have revealed to be precise indicators of emotions, but also electroencephalographic (EEG) and photoplethysmographic (PPG) signals have been exploited in emotion-related experimentations.
This thesis proposal has three main objectives: 1) exploit multi-modal datasets from the literature (e.g., DEAP) to propose new emotion-recognition models, 2) record GSR, EEG, and PPG signals using wearable devices while subjects undergo an emotional engaging experiment, and 3) provide a preliminary assessment of the acquired data on a population subset.
For more information contact Alessandra Grossi and Aurora Saibene.
Stress detection has been drawing attention from multidisciplinary research communities, especially concerning mental health care, and has been relying increasingly on artificial intelligence strategies to better model dependencies between cognition and physiological processes.
Physiological signals like Galvanic Skin Response (GSR) and data from the photoplethysmogram (PPG) have revealed to be precise indicators of stress, but also electroencephalographic (EEG) signals have been exploited in cognitive overload experimentations.
This thesis proposal has three main objectives: 1) explore the literature for datasets using one or all the presented modalities, 2) record GSR, EEG, and PPG signals using wearable devices while subjects undergo a stress and relax inducing experiment, and 3) provide a preliminary assessment of the acquired data on a population subset.
For more information contact Aurora Saibene.
EXTERNAL PROJECT PROPOSALS
- SOFTWARE DEVELOPER POSITION at Xnext s.r.l. (Xnext® is a deep tech SME that produces and markets X-ray inspection solutions), click for details.
- THESIS PROPOSALS at DIGITAL ATTITUDE, click here for more details.
ONGOING PROJECTS
The project purpose is to analyse a subject physiological signal in a stressing environment, thus the experiment takes place on a two-way transit street (not controlled by traffic lights).
The experimental protocol its repeated for 4 trials and is described in the following:
- 1 minute baseline recording;
- the volunteer walks safely back and forth om the side walk for 30 + 30 meters;
- a resting time is given to the volunteer;
- 1 minute baseline recording;
- the volunteer crosses the street two times to return to the starting point;
- a resting time is given to the volunteer.
The data recorded through Shimmer3 EMG and Shimmer3 GSR+ (http://www.shimmersensing.com/) are: electromyographic (EMG) signal, galvanic skin response (GSR) signal, temperature, blood pressure, inertial signals.
The devices are reported in the figures on the left side, with the volunteer's permission.
The project purpose is to analyse if there is a difference in a subject physiological response while he/she is listening to a heterogeneous audio playlist. This playlist is composed by tracks labelled as stressful or relaxing.
The experimental protocol is described in the following:
- 7 stressful and 7 relaxing audio tracks are randomly selected from a playlist;
- 150 seconds baseline recording;
- the volunteer listen to a 60 seconds track;
- 15 seconds are given to the volunteer to judge the track as stressful or relaxing.
The last two points are repeated for all the selected tracks.
The data recorded through Shimmer3 GSR+ (http://www.shimmersensing.com/) are: heart rate and GSR.
The project purpose is to analyse the subjective response of volunteers exposed to a heterogeneous experimental paradigm through means of electroencephalography.
The data are collected through the Unicorn Hybrid Black (g.tec medical engineering GmbH) using it with dry electrodes to assess the possibility of having more comfortable devices for electroencephalographic signal recording.
CONCLUDED PROJECTS
Master Thesis
Degree in Computer Science
- Progettazione, implementazione e analisi di un framework sperimentale per lo studio della percezione di meme misogini, con focalizzazione sulle risposte fisiologiche, Lisa Cocchia.
- Classificazione tramite tecniche di deep learning di segnali EEG derivanti da esperimenti di moto immaginato nell'ambito della Brain Computer Interface, Luca Ferri.
- Un sistema multimodale di riconoscimento delle emozioni nella musica, Riccardo Locatelli.
- Inner speech recognition from EEG signals, Elisa Cazzaniga.
- A fine-tuned playlist recommender system based on emotions, Mattia Marchi.
- Emotion recognition with neural network attention based model, Elia Fabbris.
- Progettazione di un'infrastruttura per un sistema di interazione tra veicoli a guida autonoma e pedoni, Dario Gerosa.
- Analisi degli stati affettivi in situazioni e soggetti eterogenei facendo uso delle loro risposte fisiologiche, Alessandra Grossi.
- Studio delle tecniche di validazione per applicazioni di realtà aumentata, Gabriella Magli.
- Multimodal emotion analysis, Alessandro Mangone.
- Sexist memes detection with multi-task learning, Stefano Capra.
- Emotion/sentiment recognition and classification of audio files from soundtracks, Danilo Deponti.
- Measuring human affective state for personalized and adaptive sound experience, Marta Giltri.
- Processing and analysis of electrophysiological data, Aurora Saibene.
Degree in Theory and Technology of Communication
- Misoginia e costrutti sociali: analisi della percezione dei meme misogini in relazione alle credenze di base di un individuo, Marco Cervelli.
- Pedestrian and autonomous vehicle interaction: towards affective crossing, Stefano Dessena.
- A preliminary study for a musis recommender application based on human emotions, Sara Gerelli.
- Comunicazione digitale per il benessere psicologico giovanile: il caso di C’è Da Fare ETS, Annachiara Izzi.
- Listening to audio tracks with different devices: an analysis with physiological parameters and subjective responses, Deborah Micaela Padilla Vaca.
- Misoginia e multimedialità: un’analisi sul ruolo dei contenuti visivi e testuali nei meme misogini tramite tracciamento oculare, Emma Salvadori.
- La musica come stimolo per la memoria: un’analisi sul legame tra melodie, emozioni e memoria, Clara Ventura.
- L'impatto dell'utilizzo della tecnologia sulla didattica universitaria in Italia durante il periodo di lockdown da Covid-19: uno studio bibliografico, Nicholas Celesia.
- Generazione di una playlist basata sulle emozioni: fasi per lo sviluppo di un sistema di raccomandazione, Roberto Crotti.
- Riconoscimento delle emozioni indotte da stimoli musicali tramite acquisizioni di segnali fisiologici, Rocco Lena.
- Studio dell’interessantezza dei video brevi come spunto creativo nella produzione audiovisiva, Claudia Rabaioli.
- Didattica accessibile: sviluppo di un'interfaccia web per educare e sensibilizzare i bambini sul tema della sostenibilità, Maria Rachele De Battista.
- Brain assessment: neural signal analyses, BCI applications and ethical issues, Mirko Caglioni.
- Speech Emotion Recognition: tecnologie e questionari come strumenti per la valutazione delle emozioni nella popolazione anziana, Alice Cattelan.
- Analisi della misoginia online: la misoginia nei meme italiani, Matteo Parisi.
- Personality, emotions and physiological responses to relaxing sounds, David Rota.
- Rilevamento e monitoraggio dei dati fisiologici da stimoli audio per il riconoscimento delle emozioni, Sonia Secco.
- Didattica a distanza: correlazioni tra le caratteristiche visive della lezioni e le rivisualizzazioni degli studenti, Alex Zanardini.
- Clustering di brani musicali non supervisionato: raggruppamenti per emozione suscitata e genere musicale, Thomas Bellardone.
- The MAMI project - designing web architecture to disclose the problem of online misogyny, Sofia Bellotti.
- La distanza sociale nell'epoca del COVID-19: un esperimento in ambiente digitale, Andrea D'Amato.
- Picture-in-picture e voice-over: uno studio sperimentale su due tipologie di DaD a confronto durante l'emergenza da COVID-19, Alessandro Di Crescenzo.
- Affective Computing e Marketing: il ruolo delle emozioni nella pubblicità italiana ai tempi del Covid-19, Monica Ayman Boctor Mikhail.
- Positive Affective Computing: studio di un approccio ecologico alla dislessia, Roberta Maria Randazzo.
- Podcast marketing e nostalgia mediale, uno studio di caso sul podcast "Compact Disk", Francesco Vasques.
- Rilevamento e analisi multimediale di meme misogini presenti in rete: l'influenza del contenuto testuale, Annalisa Bachir.
- Analisi della correlazione tra emozioni, creatività e interessantezza dei video pubblicati sui social network, Filippo Borgia.
- Affective computing: analisi teorica e studio sulla relazione tra personalità e stato affettivo, Roberta Di Mauro.
- Rilevamento e analisi multimediale di meme misogini presenti in rete: l'influenza del contenuto pittorico, Francesca Frigerio.
- Studio della risposta fisiologica per l'analisi comportamentale dei pedoni, Elena Maria Manisera.
- Correlazione di dati fisiologici e comportamentali con lo stato affettivo, Eleonora Pizzi.
- Beyond mouse and keyboard: implementation of eye and face tracking for low cost pc interaction, Alessandro Rosti.
- Risposta fisiologica come indicatore di stato d'animo durante la lettura di testi in differenti condizioni psicologiche e comportamentali, Mattia Severgnini.
- Valutazione della qualità delle immagini subacquee: analisi soggettiva e oggettiva, Matteo Angeli.
- Multimodal classification of sexist content in memes, using textual and visual features, Gaia Campisi.
- Automatic detection of sexism on the web: visual analysis and multimodal classification, Monica Mantovani.
- Analisi della creatività e della interessantezza dei video nei social network, Valeria Strano.
Degree in Artificial Intelligence for Science ad Technology
Undergraduate Thesis
Degree in Computer Science
- Analisi Comparativa ed Elaborazione di Segnali Fisiologici Acquisiti da Due Diversi Dispositivi Wearable: Empatica Embrace e Shimmer3 GSR+, Filippo Besana.
- Analisi della Qualità e Affidabilità di Segnali Fisiologici Acquisiti con Dispositivi Indossabili, Marco Corbetta.
- Tecnologie per il Riconoscimento Emotivo: Applicazioni e Sfide dell'Affective Computing, Paolo Mascheroni.
- Verso Sistemi SER Affidabili: Analisi critica delle prestazioni e Sfide Multilingua, Riccardo Mattia.
- Riconoscimento delle emozioni in conversazioni naturali: etichettatura e analisi delle tracce audio, Lorenzo Mauro.
- Studio e approfondimento della metrica Gap Statistic per la valutazione interna di risultati di clustering non-supervisionato, Elisa Merigo.
- Analisi audio delle videolezioni erogate durante la pandemia di COVID 19, Luca Oricchio.
- A detailed study on UMAP for dimensionality reduction, Giulio Riggio.
- Analisi e validazione di metriche interne per il clustering, Andrea Spagnolo.
- Techniques for EEG signal denoising, Xin Hui Chen.
- Riconoscimento sincerità e sarcasmo nel parlato, Davide Vanoli.
- Riconoscimento delle emozioni dal parlato basato sul genere, Mattia Rigolli.
- Applicazioni di tecniche di OCR su contenuti memetici, Cesare Bagnolini.
- Piattaforma MAMI: sviluppo di una web application per la raccolta e classificazione di meme misogini, Gabriele De Mattia.
- Ottimizzazione di immagini subacquee con MATLAB, Alessio Gui.
- Analisi dati inerziali e fisiologici con sensori indossabili, Andrea Gusmara.
- Electroencephalographic signal processing and analysis for Brain Computer Interfaces, Gabriele Esposito.
- Classificazione delle emozioni attraverso dati fisiologici, Luca Ferri.
- Riconoscimento delle emozioni attraverso dati EEG, Giuseppe Magazzù.
- Classificazione multimodale di contenuti sessisti, Michele Stellacci.
- Analisi dei dati fisiologici nell'ambito di un attraversamento pedonale, Gianluca Toffanin.
- Tecniche di rimozione artefatti per segnali EEG, Gianluca Tremolada.
- Tecniche di deep learning per segnali audio, Daniele Valmacco.
- Studio della risposta fisiologica per l'analisi del comportamento dei pedoni, Lisa Cocchia.
- Audio denoising using deep convolutional autoencoder models, Andrea Coppola.
- Multi-task learning per il riconoscimento di pubblicità sessite, Silvia Di Resta.
- Analisi di segnali EMG nel contesto di un attraversamento pedonale, Andrea Monzani.
- Analisi dati fisiologici in risposta a stimoli audio, Samuele Pasi.
- Preprocessing e analisi di dati psicofisiologici nel contesto di un esperimento di psicologia cognitiva, Marco Pellegrino.
- Sviluppo di un'applicazione Android che correla dati eye tracker e segnali fisiologici nello scenario di lettura di un testo, Claudio Rustioni.
- Analisi di segnali EEG tramite convoluzione wavelet, Lorenzo Sasso.
- Il territorio attraverso le immagini, Giulia Tognon.
- Analisi di interessantezza dei video, Tommaso Vitali.
- Misogyny in images, Ilaria Erba.
- Machine learning for audio stimuli, Matteo Frigerio.
- Correlation between physiological signals and states of stress-relax induced by video 1, Dario Gerosa.
- Correlation between physiological signals and states of stress-relax induced by video 2, Marta Giltri.
- Machine learning for social media data analysis, Alessandro Mangone.
- How does the emotion induced by an image influence image quality assessment?, Giorgio Pilotti.
- Image processing for underwater images, Federico Raiti.
Other Degrees
- Riconoscimento delle emozioni nel parlato italiano, Alberto Milella (Degree in Statistical and Economic Sciences).
- Valutazione della qualità delle immagini subacquee mediante un approccio psico-visuale, Siria Diaco (Degree in Statistical and Economic Sciences).