4 papers
SplitSEE: A Splittable Self-supervised Framework for Single-Channel EEG Representation Learning
Rikuto Kotoge, Zheng Chen, Tasuku Kimura +4
While end-to-end multi-channel electroencephalography (EEG) learning approaches have shown significant promise, their applicability is often constrained in neurological diagnostics…
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…