2025
EEGScope
A framework for the real-time acquisition, processing and analysis of EEG data — the core toolkit behind every brain-computer interface experiment in this body of work.
Tommaso Colafiglio is a researcher, lecturer and software developer working at the intersection of Artificial Intelligence, Neuroscience and Computational Music. He designs Brain-Computer Interface systems that decode EEG signals into emotion, cognition and sound — bridging rigorous experimental science with hands-on creative practice as a guitarist, composer and orchestra conductor. He is completing a PhD in Artificial Intelligence at Sapienza University of Rome, teaches Engineering of Intelligent Systems at the Politecnico di Bari, and collaborates with EssilorLuxottica on the neuroscientific evaluation of wearable audio technology.
A framework for the real-time acquisition, processing and analysis of EEG data — the core toolkit behind every brain-computer interface experiment in this body of work.
An interactive neuroadaptive musical instrument: EEG signals shape a neural network's musical output in real time, turning brainwaves directly into sound.
Industrial research collaboration on wearable audio glasses: neuroscientific analysis, experimental protocol design and user evaluation for a hearing-support product used by thousands of people.
An open EEG dataset for emotion research built entirely on low-cost, sparse-electrode consumer devices — designed to make affective brain-computer interface research accessible outside the lab.
Real-time, emotion-driven sound texture synthesis: affective state decoded from EEG continuously reshapes a generative audio engine.
Brain-computer interfaces meet large language models: an emotional-support conversational agent that senses affective state from EEG and adapts its dialogue in response.
A neural polyphonic music generation system: machine learning composes multi-voice music, bridging AI research with training as a composer.
Co-founder and organiser of an international workshop series on wearable devices and brain-computer interfaces for user modelling, held at ACM UMAP.
Combining mental-state recognition with machine learning to support neurorehabilitation — translating EEG-based cognitive and motivational state classification into clinical decision support.
Professional guitarist and composer specialising in computer music composition, trained at the Conservatorio di Musica E. R. Duni, Matera.
Composer of original music for film and multimedia content, working across narrative and interactive formats.
Conducting experience across classical and contemporary ensembles, alongside a Master's in Musicology and Musical Heritage.
Builds BCI-driven instruments — NeuroHarmonium, EmoSynth, NeuralPMG — where brain signals and neural networks become compositional tools.
Real-time audio programming across JUCE, HISE and classic computational-music environments.
Teaching Music Technology and Music History at Italian liceo level since 2018, shaping the next generation of music technologists.
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