<i>De Novo</i>Brain-Computer Interfacing Deforms Manifold of Populational Neural Activity Patterns in Human Cerebral Cortex

Iwama, Seitaro; Zhang, Yichi; Ushiba, Junichi

doi:10.1523/eneuro.0145-22.2022

Cited by 1 publication

(1 citation statement)

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“…The remote effect, which is the influence of manipulating a single region on nontargeted areas within a functional network, has recently been discussed in the fields of noninvasive brain stimulation and neurofeedback, highlighting the necessity of considering whole‐brain reorganization (Bergmann & Hartwigsen, 2020 ; DeCharms et al, 2005 ; Kvamme et al, 2022 ). In the sensorimotor domain, large‐scale sensorimotor networks can be manipulated by targeting the SM1 activity (Corsi et al, 2020 ; Hayashi et al, 2020 ; Iwama, Zhang, & Ushiba, 2022 ; Kasahara et al, 2015 ; Wander et al, 2013 ). Specifically, SMR signals, frequently employed as the online readout of corticomotor excitability, are found around the bilateral SM1 and SMA, and voluntary SMR regulation via BCI operation may contribute to the functional reorganization of the sensorimotor network comprising these regions (Ramos‐Murguialday et al, 2013 ).…”

Section: Introductionmentioning

confidence: 99%

EEG decoding with spatiotemporal convolutional neural network for visualization and closed‐loop control of sensorimotor activities: A simultaneous EEG‐fMRI study

Iwama,

Tsuchimoto,

Mizuguchi

et al. 2024

Human Brain Mapping

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Closed‐loop neurofeedback training utilizes neural signals such as scalp electroencephalograms (EEG) to manipulate specific neural activities and the associated behavioral performance. A spatiotemporal filter for high‐density whole‐head scalp EEG using a convolutional neural network can overcome the ambiguity of the signaling source because each EEG signal includes information on the remote regions. We simultaneously acquired EEG and functional magnetic resonance images in humans during the brain‐computer interface (BCI) based neurofeedback training and compared the reconstructed and modeled hemodynamic responses of the sensorimotor network. Filters constructed with a convolutional neural network captured activities in the targeted network with spatial precision and specificity superior to those of the EEG signals preprocessed with standard pipelines used in BCI‐based neurofeedback paradigms. The middle layers of the trained model were examined to characterize the neuronal oscillatory features that contributed to the reconstruction. Analysis of the layers for spatial convolution revealed the contribution of distributed cortical circuitries to reconstruction, including the frontoparietal and sensorimotor areas, and those of temporal convolution layers that successfully reconstructed the hemodynamic response function. Employing a spatiotemporal filter and leveraging the electrophysiological signatures of the sensorimotor excitability identified in our middle layer analysis would contribute to the development of a further effective neurofeedback intervention.

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