no code implementations • 22 Mar 2024 • Shirin Vafaei, Ryohei Fukuma, Huixiang Yang, Haruhiko Kishima, Takufumi Yanagisawa
To address this issue, we propose a representation learning framework, termed brain-grounding of semantic vectors, which fine-tunes pretrained feature vectors to better align with the neural representation of visual stimuli in the human brain.
no code implementations • 31 Oct 2023 • Ryohei Fukuma, Kei Majima, Yoshinobu Kawahara, Okito Yamashita, Yoshiyuki Shiraishi, Haruhiko Kishima, Takufumi Yanagisawa
DMs can improve the accuracy of neural decoding when used with the nonlinear Grassmann kernel, compared to conventional power features.
no code implementations • 7 Nov 2021 • Ryohei Fukuma, Takufumi Yanagisawa, Shinji Nishimoto, Hidenori Sugano, Kentaro Tamura, Shota Yamamoto, Yasushi Iimura, Yuya Fujita, Satoru Oshino, Naoki Tani, Naoko Koide-Majima, Yukiyasu Kamitani, Haruhiko Kishima
The successful control of the feedback images demonstrated that the semantic vector inferred from electrocorticograms became closer to the vector of the imagined category, even while watching images from different categories.