| 摘要 |
A method of calibrating a direct neural interface system comprising the steps of: a. Acquiring electrophysiological signals electrophysiological signals representative of a neuronal activity of a subject's brain over a plurality of observation time windows and representing them in the form of a N+1-way tensor (<u>X</u>), N being greater or equal to two, called an observation tensor; b. Acquiring data indicative of a voluntary action performed by said subject during each of said observation time windows, and organizing them in a vector or tensor (y), called an output vector or tensor; and c. Determining a (multi-way) regression function of said output vector or tensor on said observation tensor; wherein said step c. includes performing multilinear decomposition of said observation tensor on a "score" vector (t), having a dimension equal to the number of said observation time windows, and N "weight" vectors (w1, w2, w3), characterized in that said "weights" vectors are chosen such as to maximize the covariance between said "score" vector and said output vector or tensor subject to a sparsity-promoting constraint or penalty. A method of operating a direct neural interface system for interfacing a subject's brain (B) to an external device (ED), said method comprising such a calibration step. |
