IEEE EMBS: Evolution of Digital Signal Processing Techniques for Analyzing EEG

#biomedical #engineering #EEG #machine #learning #cnn
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The evolution of analyzing EEG signals that span time domain, frequency domain, and frequency-time domain will be covered. How neural oscillators arise from neural activity will be discussed to motivate the need to find features located in time and frequency. To conclude, an example using a state of the art Convolutional Neural Network method for classifying EEG signals will be presented.


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  • Ottawa, Ontario
  • Canada
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Dr. Lou Pino

Topic:

Evolution of Digital Signal Processing Techniques for Analyzing EEG

Biography:

Lou Pino, PhD, has 15 years of digital health experience innovating health information technology, medical devices and products - recognized with 11 patents. He currently leads the Data Science team at Neurovine to develop algorithms to help concussion patients recover. The team applies machine learning and statistical analysis of EEG and other physiological signals. Previously he has led the development of algorithms for wearable devices including EEG headband (Muse) and heart rate variability signals from a Smartwatch. He pioneered the use of Big Data in the study of neuroscience biomarkers. He received funding from the Alzheimer’s Society of Canada to develop and validate a cognitive assessment mobile APP. His Ph.D. work focused on classification of electrophysiological signals using machine learning techniques.