The Influence of Frequency Bands and Brain Region on ECoG-Based BMI Learning Performance

Sensors

Jeongeun Sim¹  Youngjong Kwak¹  Jinsick Park¹  Dongpyo Jang¹  Seokbeen Lim¹  Wongyu Jung² 

[1] Department of Biomedical Engineering, Hanyang University, Seoul 04763, Korea;Department of Electronics and Computer Engineering, Hanyang University, Seoul 04763, Korea;

关键词: brain–machine interface;    frequency band;    brain area;   

DOI  :  10.3390/s21206729

来源: DOAJ

【 摘 要 】

Numerous brain–machine interface (BMI) studies have shown that various frequency bands (alpha, beta, and gamma bands) can be utilized in BMI experiments and modulated as neural information for machine control after several BMI learning trial sessions. In addition to frequency range as a neural feature, various areas of the brain, such as the motor cortex or parietal cortex, have been selected as BMI target brain regions. However, although the selection of target frequency and brain region appears to be crucial in obtaining optimal BMI performance, the direct comparison of BMI learning performance as it relates to various brain regions and frequency bands has not been examined in detail. In this study, ECoG-based BMI learning performances were compared using alpha, beta, and gamma bands, respectively, in a single rodent model. Brain area dependence of learning performance was also evaluated in the frontal cortex, the motor cortex, and the parietal cortex. The findings indicated that BMI learning performance was best in the case of the gamma frequency band and worst in the alpha band (one-way ANOVA, F = 4.41, p < 0.05). In brain area dependence experiments, better BMI learning performance appears to be shown in the primary motor cortex (one-way ANOVA, F = 4.36, p < 0.05). In the frontal cortex, two out of four animals failed to learn the feeding tube control even after a maximum of 10 sessions. In conclusion, the findings reported in this study suggest that the selection of target frequency and brain region should be carefully considered when planning BMI protocols and for performing optimized BMI.

【 授权许可】

Unknown