Machine Learning Methods for Electroencephalogram-Based Age Prediction. - Grant Sinha; Nabil Belacel; Zhiyang Gu; Sam M. Doesburg; George Medvedev; Urs Ribary; Vasily A. Vakorin; Pengcheng Xi (SENSORS, 2023)

Byte-Pair Encoding for Classifying Routine Clinical Electroencephalograms in Adults Over the Lifespan. - Mykola Klymenko; Sam M. Doesburg; George Medvedev; Pengcheng Xi; Urs Ribary; Vasily A. Vakorin (IEEE J. Biomed. Health Informatics, 2023)

Biological age from electroencephalographic activity could be decoupled from a gradual stochastic accumulation of pathologies over adulthood. - Ostap Kalapun; Alexander Moiseev; George Medvedev; Sam M. Doesburg; Pengcheng Xi; Sylvain Moreno; Urs Ribary; Vasily A. Vakorin (BIBM, 2024)

Enhancing Brain Age Prediction: A Generative AI Approach for EEG Machine Learning Models. - Zara Cook; Grant Sinha; Jack Wang 007; Chengzong Zhao; Nabil Belacel; Sam M. Doesburg; George Medvedev; Urs Ribary; Vasily A. Vakorin; Pengcheng Xi (I2MTC, 2024)

Decoding Brain Age: A Self-Supervised Graph Neural Network Framework for EEG Analysis. - Zara Cook; Chengzong Zhao; Livia Murray; Jivan Kesan; Nabil Belacel; Sam M. Doesburg; George Medvedev; Vasily A. Vakorin; Pengcheng Xi (IEEE SENSORS, 2024)

Leveraging Large Language Models and Fuzzy Clustering for EEG Report Analysis. - Chengzong Zhao; Zara Cook; Livia Murray; Jivan Kesan; Nabil Belacel; Sam M. Doesburg; George Medvedev; Vasily A. Vakorin; Pengcheng Xi (IEEE SENSORS, 2024)