1 citations · 1 across the 6 of their papers we have counts for
6 papers
Visual Semantic Decoding of Electrocorticography from Video Stimuli using End-to-End Deep Learning
Stella Ho, Joel Villalobos, Joseph West +7
ECoG-based visual semantic decoding enables inference of semantic interpretation of visual perception from complex, noisy brain activity. This study examines the feasibility of vis…
Intrinsic frequency distribution characterises neural dynamics
Ryohei Fukuma, Yoshinobu Kawahara, Okito Yamashita +3
Decomposing multivariate time series with certain basic dynamics is crucial for understanding, predicting and controlling nonlinear spatiotemporally dynamic systems such as the bra…
Wirelessly transmitted subthalamic nucleus signals predict endogenous pain levels in Parkinson's disease patients
Abdi Reza, Takufumi Yanagisawa, Naoki Tani +5
Parkinson disease (PD) patients experience pain fluctuations that significantly reduce their quality of life. Despite the vast knowledge of the subthalamic nucleus (STN) role in PD…
Brain-aligning of semantic vectors improves neural decoding of visual stimuli
Shirin Vafaei, Ryohei Fukuma, Takufumi Yanagisawa +10
The development of algorithms to accurately decode neural information has long been a research focus in the field of neuroscience. Brain decoding typically involves training machin…
Fast, accurate, and interpretable decoding of electrocorticographic signals using dynamic mode decomposition
Ryohei Fukuma, Kei Majima, Yoshinobu Kawahara +4
Dynamic mode (DM) decomposition decomposes spatiotemporal signals into basic oscillatory components (DMs). DMs can improve the accuracy of neural decoding when used with the nonlin…
Voluntary control of semantic neural representations by imagery with conflicting visual stimulation
Ryohei Fukuma, Takufumi Yanagisawa, Shinji Nishimoto +10
Neural representations of visual perception are affected by mental imagery and attention. Although attention is known to modulate neural representations, it is unknown how imagery…