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Thesis supervision | Gagandeep Singh: EEG Source Localization: A Machine Learning Approach

EEG source localization is hard. Current state of art is beamforming. We took a simultaneous EEG-fMRI dataset, used fMRI to find true source and measured how well a beamformer can predict the Broadmann area where the activity is. Turned out that it could not (random prediction level). Gagandeep then developed an approach based on autoencoders that achieved higher-than-random classification ability.

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I have worked on various projects in machine learning and computer science, neuroscience and brain-computer interfaces, reinforcement learning and robotics. Currently I am focusing on two things: leading machine learning team at OffWorld Inc. to train robots for space exploration, and continuing the research done as part of my PhD on neuroscience and artificial intelligence.

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