Across-subject offline decoding of motor imagery from MEG and EEG
| العنوان: | Across-subject offline decoding of motor imagery from MEG and EEG |
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| المؤلفون: | Hanna-Leena Halme, Lauri Parkkonen |
| المساهمون: | Department of Neuroscience and Biomedical Engineering, Aalto-yliopisto, Aalto University |
| المصدر: | Scientific Reports, Vol 8, Iss 1, Pp 1-12 (2018) Scientific Reports |
| بيانات النشر: | Nature Publishing Group, 2018. |
| سنة النشر: | 2018 |
| مصطلحات موضوعية: | Adult, Male, 030506 rehabilitation, Imagery, Psychotherapy, Computer science, Movement, Speech recognition, lcsh:Medicine, 02 engineering and technology, Electroencephalography, Article, 03 medical and health sciences, Passive movements, 0302 clinical medicine, Motor imagery, 0202 electrical engineering, electronic engineering, information engineering, medicine, Humans, General, lcsh:Science, Brain–computer interface, Multidisciplinary, medicine.diagnostic_test, business.industry, lcsh:R, 3112 Neurosciences, Magnetoencephalography, Pattern recognition, Neurofeedback, Neurophysiology, Hand, Publisher Correction, Brain-Computer Interfaces, Calibration, Female, 020201 artificial intelligence & image processing, lcsh:Q, Artificial intelligence, 0305 other medical science, business, Algorithms, 030217 neurology & neurosurgery, Decoding methods |
| الوصف: | Long calibration time hinders the feasibility of brain-computer interfaces (BCI). If other subjects’ data were used for training the classifier, BCI-based neurofeedback practice could start without the initial calibration. Here, we compare methods for inter-subject decoding of left- vs. right-hand motor imagery (MI) from MEG and EEG.Six methods were tested on data involving MEG and EEG measurements of healthy participants. Only subjects with good within-subject accuracies were selected for inter-subject decoding. Three methods were based on the Common Spatial Patterns (CSP) algorithm, and three others on logistic regression with l1 - or l2,1 -norm regularization. The decoding accuracy was evaluated using 1) MI and 2) passive movements (PM) for training, separately for MEG and EEG.When the classifier was trained by MI, the best accuracies across subjects (mean 70.6% for MEG, 67.7% for EEG) were obtained using multi-task learning (MTL) with logistic regression and l2,1-norm regularization. MEG yielded slightly better average accuracies than EEG. When PM were used for training, none of the inter-subject methods yielded above chance level (58.7%) accuracy.In conclusion, MTL and training with other subject’s MI is efficient for inter-subject decoding of MI. Passive movements of other subjects are likely suboptimal for training the MI classifiers. |
| وصف الملف: | application/pdf |
| اللغة: | English |
| تدمد: | 2045-2322 |
| الوصول الحر: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::61a8899394d6eab1f227d7bc3c4184a1Test http://link.springer.com/article/10.1038/s41598-018-28295-zTest |
| حقوق: | OPEN |
| رقم الانضمام: | edsair.doi.dedup.....61a8899394d6eab1f227d7bc3c4184a1 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 20452322 |
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