期刊
SENSORS
卷 21, 期 20, 页码 -出版社
MDPI
DOI: 10.3390/s21206729
关键词
brain-machine interface; frequency band; brain area
资金
- National Research Foundation of Korea (NRF) - Ministry of Science and ICT [NRF-2019M3C7A1031278]
- Ministry of Trade, Industry & Energy (MOTIE, Korea) [20012461]
- Korea Evaluation Institute of Industrial Technology (KEIT) [20012461] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
- National Research Foundation of Korea [2019M3C7A1031278] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
The study compared BMI learning performances using different frequency bands and brain regions, finding that performance was best in gamma frequency band and worst in alpha band. Better performance was also observed in the primary motor cortex, indicating its positive impact on BMI learning performance.
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.
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