Somatosensory ECoG-based brain–machine interface with electrical stimulation on medial forebrain bundle

Yoon Kyung Cho, Chin Su Koh, Youjin Lee, Minkyung Park, Tae Jun Kim, Hyun Ho Jung, Jin Woo Chang, Sang Beom Jun

Research output: Contribution to journalArticlepeer-review


Brain–machine interface (BMI) provides an alternative route for controlling an external device with one’s intention. For individuals with motor-related disability, the BMI technologies can be used to replace or restore motor functions. Therefore, BMIs for movement restoration generally decode the neural activity from the motor-related brain regions. In this study, however, we designed a BMI system that uses sensory-related neural signals for BMI combined with electrical stimulation for reward. Four-channel electrocorticographic (ECoG) signals were recorded from the whisker-related somatosensory cortex of rats and converted to extract the BMI signals to control the one-dimensional movement of a dot on the screen. At the same time, we used operant conditioning with electrical stimulation on medial forebrain bundle (MFB), which provides a virtual reward to motivate the rat to move the dot towards the desired center region. The BMI task training was performed for 7 days with ECoG recording and MFB stimulation. Animals successfully learned to move the dot location to the desired position using S1BF neural activity. This study successfully demonstrated that it is feasible to utilize the neural signals from the whisker somatosensory cortex for BMI system. In addition, the MFB electrical stimulation is effective for rats to learn the behavioral task for BMI.

Original languageEnglish
Pages (from-to)85-95
Number of pages11
JournalBiomedical Engineering Letters
Issue number1
StatePublished - Feb 2023

Bibliographical note

Funding Information:
This study was supported by Convergent Technology R&D Program for Human Augmentation through the National Research Foundation of Korea (NRF) and Basic Science Research Program through the NRF funded by the Korea government (NRF-2022R1A2C2005062, 2021R1I1A1A0104775012, 2020R1F1A107410413) and also supported by Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No. RS-2022-00155966, Artificial Intelligence Convergence Innovation Human Resources Development (Ewha Womans University)).

Publisher Copyright:
© 2022, The Author(s).


  • Brain plasticity
  • Brain–machine interface
  • Deep brain stimulation
  • Somatosensory cortex
  • Virtual reward


Dive into the research topics of 'Somatosensory ECoG-based brain–machine interface with electrical stimulation on medial forebrain bundle'. Together they form a unique fingerprint.

Cite this