Penelope Bekiari

EDUCATION

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PhD (Electroacoustic Composition and Affective Technologies)

National and Kapodistrian University of Athens (2024–Present)

Physiological computing, human–computer interaction (HCI), biomusic,

neuro-responsive musical systems, affective technologies, and adaptive performance

environments.

MMus (Electroacoustic Composition) (Distinction) - University of Manchester

Electroacoustic composition, interactive media, live electronics, and computer music.

MA (Music Technology and Contemporary Musical Practices) (Distinction)- National and Kapodistrian University of Athens

Music technology, sound synthesis, digital audio systems, and contemporary

compositional practice.

BA (Theatre Studies)

National and Kapodistrian University of AthensPerformance studies, dramaturgy, theatre history, aesthetics, and interdisciplinary

artistic practice.

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TECHNICAL & RESEARCH SKILLS

Algorithmic Sensing & AI: Machine Learning Workflows, Time-Series Analysis, Signal

Processing, Feature Extraction, Pattern Recognition, Python, C++, MATLAB, Data Processing

Pipelines. HCI & Inclusive Design: Human–Computer Interaction (HCI), Accessible and Inclusive

Technology Design, Co-Design Methodologies, Participatory Research, User Studies and

Evaluation, Affective & Physiological Computing.

Sensor & Wearable Integration: Real-Time Interactive Systems, Biosignal Processing, EEG

Acquisition (Muse 2), Lab Streaming Layer (LSL), Heart Rate Monitoring, Galvanic Skin

Response (GSR), Motion and Inertial Sensing Technologies (Accelerometer & Gyroscope),

Arduino, OSC Protocols, Max/MSP.

 

RELEVANT RESEARCH EXPERIENCE

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Doctoral Researcher

National and Kapodistrian University of Athens (2024–Present)

● Develop neuro-responsive musical systems integrating physiological computing, signal

processing, and adaptive musical behaviour.

● Design and implement computational workflows for sensor analysis, feature extraction,

pattern recognition, and real-time interaction using Python and Max/MSP.

● Key Project – Hyponoia: Neuro-Responsive Musical Intelligence Framework

(2025–Present): Developed a device-independent framework connecting EEG sensor

systems to performance platforms in real-time. Built neuro-affective classification models

translating neural activity into high-level musical states to drive AI-assisted generative

soundscapes.

● Key Project – Vocal Improvisation and Embodied Soundscape Study (2025–2026):

Designed and conducted controlled physiological studies with 30 participants, utilising

statistical analyses to investigate musical behaviour and neural oscillations.

Research Member

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Laboratory of Music Acoustics and Technology (LabMAT)

(2024–Present)

● Contribute to interdisciplinary research in music technology, physiological computing,

HCI, and collaborative projects involving sound analysis and physiological sensing.

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​PUBLICATIONS & ACADEMIC PRESENTATIONS

● Bekiari, P. (2026). From Soundscape Memory to Neuro-Responsive Performance:

Embodiment, Artistic Research and the Measured Body. ARTOEND Vol.1, NAPAT

Foundation.

●Bekiari, P. (2026). Hyponoia: An Affective Computing System for Augmented Musical

Performance – A Case Study. International Computer Music Conference, Hamburg.

● Bekiari, P. (2026). BioKybérnēsis: Case Studies on Biomusic and Bioaesthetics in

Contemporary Sound Performance. Sound and Music Computing Conference, Zagreb,

Croatia.

● Bekiari, P. (2025). Biomusic and Affective Technology with Penelope Bekiari. Art

Research Methods course, Ionian University, Corfu, Greece. (Invited Speaker).

● Bekiari, P. (2024). Resonant Echoes: Acoustic Heritage and Cultural Memory in the Mani

Peninsula. 7th Conference on Acoustic Ecology, Athens & Ancient Epidaurus.

● Bekiari, P. (2023). Revisiting Andromeda of Euripides with Biofeedback. Advances in

Data Science & AI Conference, University of Manchester, Manchester.

● Bekiari, P. (2023). Andromeda for Flute and Biofeedback: Biosensors in Immersive

Music Performance. Sound and Music Computing Conference, Porto, Portugal.

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● Bekiari, P. (Under Review). Embodied Feedback as Musical Practice: Biofeedback and

the Reconfiguration of Musicianship in Electroacoustic Performance. Journal of New

Music Research.

● Bekiari, P., & Klados, M. (Under Review). Hyponoia: Embodied Aesthetics in

Neuro-Computational Performance. Computer Music Journal.

 

MUSICAL EXPERIENCE & ARTISTIC EVENTS

Active performer, composer, and sound artist utilizing interactive and real-time electronic

systems.

 

Selected Performances:

Permeable Signal at Sonorities Festival & PROCESSES

Festival (2025–2026)

Rhoē No. 1 for piano and live bio-electronics at BEAST & Athens

Contemporary Music Festival (2024–2025)

Synopsis selected to represent HELMCA at CIME/ICEM, France (2024).

 

Artist in Residence: Greek National Opera (2024–2025), funded by the European

Commission.

 

TEACHING & ACADEMIC CONTRIBUTIONS

Lecturer (Electroacoustic Composition and Live Electronics)

National and Kapodistrian University of Athens (2023–2026)

Contributed to the development of student research skills and the assessment of student

knowledge in computer music, live electronics, and interactive systems.

 

Lecturer (Music Technology)

Metropolitan College / University of East London (2022–2026)

Delivered curriculum focused on digital audio systems and music technology, supporting undergraduate academic development.

 

Academic Supervisor & Contributor

Hong Kong University of Science and Technology

Provided academic supervision and supported conference activities, demonstrating experience

in mentoring and supervising research projects.

 

Workshop Facilitator

Athens International Children’s Film Festival

Designed and facilitated interactive workshops ("Exploring Sound Effects in Cinema") for

diverse, younger audiences, directly fulfilling the desirable criteria for facilitating accessible and

interactive workshops.

 

AWARDS AND GRANTS

Art of Neuroscience 2025 Prize (Netherlands Institute for Neuroscience)

Awarded for Rustle, an immersive electroacoustic performance. The system utilises

Python-based machine learning to categorise affective states from real-time biosignals

(EEG and heart rate), dynamically driving audio-visual layers.

Research Grant (Martinos Foundation)

Funded doctoral research in neuro-responsive musical systems, including the

procurement of an EEG wearable device utilised extensively in experimental studies.

 

PROFESSIONAL DEVELOPMENT

MSc in Deep Learning and Artificial Intelligence (Part-Time, Distance Learning)

University of West Attica

Advanced application of artificial intelligence, machine learning workflows, and data

classification models.