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• W1983675348 endingPage "026006" @default.
• W1983675348 startingPage "026006" @default.
• W1983675348 abstract "Studies on the non-invasive brain-machine interface that controls prosthetic devices via movement intentions are at their very early stages. Here, we aimed to estimate three-dimensional arm movements using magnetoencephalography (MEG) signals with high accuracy.Whole-head MEG signals were acquired during three-dimensional reaching movements (center-out paradigm). For movement decoding, we selected 68 MEG channels in motor-related areas, which were band-pass filtered using four subfrequency bands (0.5-8, 9-22, 25-40 and 57-97 Hz). After the filtering, the signals were resampled, and 11 data points preceding the current data point were used as features for estimating velocity. Multiple linear regressions were used to estimate movement velocities. Movement trajectories were calculated by integrating estimated velocities. We evaluated our results by calculating correlation coefficients (r) between real and estimated velocities.Movement velocities could be estimated from the low-frequency MEG signals (0.5-8 Hz) with significant and considerably high accuracy (p <0.001, mean r > 0.7). We also showed that preceding (60-140 ms) MEG signals are important to estimate current movement velocities and the intervals of brain signals of 200-300 ms are sufficient for movement estimation.These results imply that disabled people will be able to control prosthetic devices without surgery in the near future." @default.
• W1983675348 created "2016-06-24" @default.
• W1983675348 creator A5024349349 @default.
• W1983675348 creator A5024751680 @default.
• W1983675348 creator A5041833536 @default.
• W1983675348 date "2013-02-21" @default.
• W1983675348 modified "2023-10-18" @default.
• W1983675348 title "Estimation of the velocity and trajectory of three-dimensional reaching movements from non-invasive magnetoencephalography signals" @default.
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• W1983675348 doi "https://doi.org/10.1088/1741-2560/10/2/026006" @default.
• W1983675348 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/23428826" @default.
• W1983675348 hasPublicationYear "2013" @default.
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