Our lab conducts studies to understand the key components, including neural information dynamics, that play a role in cognitive and motor control. For instance, we have developed a model of speed-accuracy tradeoff and perceptual decision-making using neural signals obtained from fMRI and EEG. Additionally, we employ simultaneous EEG-TMS recordings to reveal causal relationships between our behavior and neural activity.

Through the use of psychophysical measurements, brain imaging, and neurophysiological approaches, we aim to clarify why top athletes, musicians, and other skilled performers are able to produce exceptional skills. This project will further our understanding of neural plasticity.

We test the trustworthiness of Explainable AI (XAI) based on a causal approach via the human body. Using biological signals such as cortical neural activity and muscle activity obtained during human cognitive or motor tasks, AI (Deep Learning) identifies the most important biological features that play a crucial role in the given tasks. Using this feature extraction, we manipulate human cortical neural activity using non-invasive brain stimulation and manipulate electromyogram signals using the cyber-space during the tasks. This project will extend from laboratory research to clinical research to build a prototype system of XAI.