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AssigneeBoard of Regents, The University of Texas System
InventorPrasanna Kolar
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The invention discloses an integrated non-intrusive, safe and user-friendly electroencephalography (EEG) system capable of classifying signals generated from both Event Related Potential (ERP) based steady-state visually evoked potential (SSVEP) and pure cognition, leveraging Riemannian Geometry-based signal classification algorithms for precise command generation. The system seamlessly combines SSVEP-based visual stimuli with cognition-based EEG signals to provide a comprehensive interface for brain-computer interaction (BCI) applications. Riemannian Geometry techniques are employed for robust signal classification and efficient command generation, enhancing the system's accuracy and reliability.
A brain computer interface (BCI) configured to translate event related potentials (ERP) in electroencephalograph (EEG) wave forms into commands executable by an assistance device(s) or system(s) comprising a user interface coupled to an electroencephalograph (EEG) decoder that is configured to receive and analyze EPR in EEG wave forms and producing a command signal.
An electroencephalograph (EEG) decoder comprising (i) an input module coupled to (ii) a fast Fourier transform (FFT) module which is operably coupled to (iii) a wave band analysis module that generates a command to be sent to one or more device or system.
A mobility system comprising:
Dependencies are inferred from the claim text.