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Future Directions for Brain-Machine Interfacing Technology

  • Kyuwan Choi
  • Byoung-Kyong MinEmail author
Chapter
Part of the Trends in Augmentation of Human Performance book series (TAHP, volume 5)

Abstract

Brain-machine interfaces (BMIs) are a communication technology that link humans and artificial devices through brain signals. However, development of BMI technology is currently at an impasse. First, the most commonly used BMI brain signals are principally derived from the primary sensorimotor cortices. However, these signals do not precisely reflect the diverse range of human intentions. In addition, BMI operational protocols often require users to perform mental functions that are not directly related to the goal of their task. Time is ripe to explore novel BMI control signals. Brain correlates of higher cognitive functions involved in deliberate processing of information appear as novel BMI signals with a number of appealing properties. This study suggests techniques for controlling BMIs using human higher cognitive activity in a non-invasive manner, and proposes a novel viable method based on our recent observations. Since the prefrontal cortex constitutes the highest level of the cortical hierarchy dedicated to the representation and execution of actions, these findings open the door to goal-directed intention-recognition BMI technology. This technology may help to rehabilitate or improve the cognitive performance of neurological or psychiatric patients with prefrontal dysfunctions. Cognitive BMIs should be further explored to develop practical applications and therapeutic treatments that improve the quality of life for people with sensorimotor or cognitive impairments.

Keywords

Brain-machine interface Cognition Electroencephalography Prefrontal cortex 

Notes

Acknowledgements

This work was supported by the Basic Science Research Program (grant number 2012R1A1A1038358) and the BK21 Plus program, funded by the Ministry of Science, ICT and Future Planning through the National Research Foundation of Korea. The authors acknowledge the use of text from the prior publications [3, 67, 90, 91, 92]. KC and BKM thank their co-authors for allowing them to use materials from prior joint publications [67, 90].

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Authors and Affiliations

  1. 1.BK21 Plus Global Leader Development Division in Brain EngineeringKorea UniversitySeoulRepublic of Korea
  2. 2.Department of Brain and Cognitive EngineeringKorea UniversitySeoulRepublic of Korea

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