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Across-subject offline decoding of motor imagery from MEG and EEG
- Source :
- Scientific Reports, Vol 8, Iss 1, Pp 1-12 (2018), Scientific Reports
- Publication Year :
- 2018
- Publisher :
- Nature Publishing Group, 2018.
-
Abstract
- 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.
- Subjects :
- 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
Subjects
Details
- Language :
- English
- ISSN :
- 20452322
- Volume :
- 8
- Issue :
- 1
- Database :
- OpenAIRE
- Journal :
- Scientific Reports
- Accession number :
- edsair.doi.dedup.....61a8899394d6eab1f227d7bc3c4184a1