The integration of neuroscience with artificial intelligence is transforming the way we study and understand the human brain. Machine Learning (ML) and Deep Learning (DL) offer powerful methods for making sense of the vast and complex data generated by modern brain research, from neuroimaging and electrophysiological recordings to behavioral measurements. These technologies enable more accurate diagnoses, earlier detection of neurological disorders, and deeper insights into how the brain functions in health and disease.
This lecture explores how ML and DL are applied in key areas of brain research, including neuroimaging analysis, brain–computer interfaces, and cognitive modeling. It introduces practical approaches for working with EEG, fMRI, MEG, and multimodal datasets, focusing on feature extraction, pattern discovery, and end-to-end deep learning models. Neural network architectures such as convolutional and recurrent networks are discussed in the context of brain state classification, disease prediction (ex. Autistic Spectrum Disorders), and the study of functional connectivity.
Finally, key challenges are addressed, including signal quality, data variability, model interpretability, scalability, and ethical concerns. The design, validation, and clinical deployment of AI models are discussed with a focus on how these methods can provide researchers and clinicians with clear, actionable information about brain performance, disease progression, and personalized interventions, supporting the development of precision neuroscience and improved brain health.
Prof. Oana Geman
Senior Teaching Fellow at Division of Data Science and Artificial Intelligence, Computer Science and Engineering, Chalmers University of Technology and University of Gothenburg, Sweden
Associate Professor at University of Suceava, Romania
Research Field: Signal Processing, Data Science, Machine Learning, AI
Dr. Oana Geman ((IEEE SM ‘18) is Medical Bioengineer and PhD in Electronics and Telecommunication and a post-doctoral researcher in Computer Science. She is currently a Senior Teaching Fellow (Professor) at Division of Data Science and Artificial Intelligence, Computer Science and Engineering, Chalmers University of Technology and University of Gothenburg, Sweden and an Associate Professor at University of Suceava, Romania. She obtained Habilitation in Electronics and Telecommunication Field in 2018. Within the past five years she published 10 books, has published over 100 articles in ISI Web of Science journals, with FI over 40), and her various works have been cited over 4000 times. She served as Chair of many Internationals Conferences or Organizer, Session Chair and member in Program or Technical Committees and a Senior Member IEEE. She has been a director or a member in 10 national and international grants. She was considered three years in the row by Stanford - Elsevier, in the top 2% of the highest cited scientists. Her current research interests include: non-invasive measurements of biomedical signals, wireless sensors, signal processing, and processing information by way of Artificial Intelligence such as nonlinear dynamics analysis, stochastic networks and neuro-fuzzy methods, classification and prediction, Data-Mining, Deep Learning, Intelligent Systems etc. She is a reviewer and editor of many top journals, including IEEE Transactions, IEEE ACCESS, AIHC Journal, IOT Journal, Sensors, Springer Nature Journals etc.
Personal pages: https://www.chalmers.se/en/persons/geman/
https://www.gu.se/om-universitetet/hitta-person/oanageman
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