A paper by Ryohei Fukuma, a specially-appointed lecturer in our laboratory, has been published in Communications biology.
Title of the paper: ‘Fast, accurate, and interpretable decoding of electrocorticographic signals using dynamic mode decomposition’.
A method has been developed for extracting features from multi-channel time series signals using dynamic mode decomposition, making the features easy to use in machine learning. The accuracy of brain information decoding has been improved and the computational speed is fast enough to be applied online.
Intern Yuya Ikegawa’s Dr Iwata’s paper was also published in the Journal of Neural Engineering.
Title of the paper: ‘Text and image generation from intracranial electroencephalography using an embedding space for text and images’.
Congratulations to Dr Fukuma and Ikegawa-san!