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. The primary goal is to develop explainable spatiotemporal causal discovery frameworks for time-series data such as EEG, leveraging prior knowledge of the driving stimuli. Specifically, the project aims
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electroencephalogram (EEG) and potentially other modalities (e.g., functional near-infrared spectroscopy (fNIRS)) to improve decoding accuracy and user-specific adaptation. Key research areas include: EEG signal
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for intelligent brain-computer interfaces? We are offering a PhD position in analog/mixed-signal CMOS circuit design for EEG and wearable sensor interfaces, as part of a pioneering project focused on assistive
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machines by interpreting brain activity (e.g., EEG signals) in real time using energy-efficient neuromorphic hardware. You will work on the design and hardware realization of Spiking Neural Networks (SNNs