Prediction of Muscle Activities from Electrocorticograms in Primary Motor Cortex of Primates
Electrocorticography (ECoG) has drawn attention as an effective recording approach for brain-machine interfaces (BMI). Previous studies have succeeded in classifying movement intention and predicting hand trajectories from ECoG. Despite such successes, however, there still remains considerable work...
محفوظ في:
| المؤلفون الرئيسيون: | , , , , , , |
|---|---|
| التنسيق: | Artigo |
| اللغة: | الإنجليزية |
| منشور في: |
2012
|
| الوصول للمادة أونلاين: | https://doi.org/10.1371/journal.pone.0047992 https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0047992&type=printable |
| الوسوم: |
إضافة وسم
لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!
|
| _version_ | 1838844110458322944 |
|---|---|
| access_facet | Acesso Aberto |
| author | Duk Shin Hidenori Watanabe Hiroyuki Kambara Atsushi Nambu Tadashi Isa Yukio Nishimura Yasuharu Koike |
| author_facet | Duk Shin Hidenori Watanabe Hiroyuki Kambara Atsushi Nambu Tadashi Isa Yukio Nishimura Yasuharu Koike |
| cited_by_count_is | 65 |
| collection | OpenAlex |
| description | Electrocorticography (ECoG) has drawn attention as an effective recording approach for brain-machine interfaces (BMI). Previous studies have succeeded in classifying movement intention and predicting hand trajectories from ECoG. Despite such successes, however, there still remains considerable work for the realization of ECoG-based BMIs as neuroprosthetics. We developed a method to predict multiple muscle activities from ECoG measurements. We also verified that ECoG signals are effective for predicting muscle activities in time varying series when performing sequential movements. ECoG signals were band-pass filtered into separate sensorimotor rhythm bands, z-score normalized, and smoothed with a Gaussian filter. We used sparse linear regression to find the best fit between frequency bands of ECoG and electromyographic activity. The best average correlation coefficient and the normalized root-mean-square error were 0.92±0.06 and 0.06±0.10, respectively, in the flexor digitorum profundus finger muscle. The δ (1.5∼4Hz) and γ2 (50∼90Hz) bands contributed significantly more strongly than other frequency bands (P<0.001). These results demonstrate the feasibility of predicting muscle activity from ECoG signals in an online fashion. |
| format | Artigo |
| frbr_group_id_str | doi-10.1371/journal.pone.0047992 |
| id | openalex-W2000598049 |
| institution | Tokyo Institute of Technology |
| issn_str | 1932-6203 |
| issue_str | 10 |
| journal_title_str | PLoS ONE |
| language | eng |
| publishDate | 2012 |
| publisher_str | Public Library of Science |
| spellingShingle | Prediction of Muscle Activities from Electrocorticograms in Primary Motor Cortex of Primates Duk Shin Hidenori Watanabe Hiroyuki Kambara Atsushi Nambu Tadashi Isa Yukio Nishimura Yasuharu Koike |
| title | Prediction of Muscle Activities from Electrocorticograms in Primary Motor Cortex of Primates |
| title_full | Prediction of Muscle Activities from Electrocorticograms in Primary Motor Cortex of Primates |
| title_fullStr | Prediction of Muscle Activities from Electrocorticograms in Primary Motor Cortex of Primates |
| title_full_unstemmed | Prediction of Muscle Activities from Electrocorticograms in Primary Motor Cortex of Primates |
| title_short | Prediction of Muscle Activities from Electrocorticograms in Primary Motor Cortex of Primates |
| topic_facet | Electrocorticography Sensorimotor rhythm Electromyography Motor cortex Primary motor cortex Pattern recognition (psychology) Sensorimotor cortex Electroencephalography Computer science Brain–computer interface Artificial intelligence Physical medicine and rehabilitation Neuroscience Medicine Biology Stimulation |
| url | https://doi.org/10.1371/journal.pone.0047992 https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0047992&type=printable |
| volume_str | 7 |