Name
Abstract | ||
|---|---|---|
Haptic guidance is a motor training procedure in which the subject is physically guided through an ideal motion by an haptic interface. It is especially valuable for repetitive training, and more effective when coupled with additional sensory feedback, e.g. the visual modality. The advantage of additional feedback modalities may stem from the learner's increased active participation and engagement. Here, we test this hypothesis by analyzing the learners' brain state during haptic training. Specifically, we focus on the sensorimotor rhythms (SMR), since they have been previously associated with the level of engagement directed towards a motor task. We conducted a circle-drawing haptic training, where subjects were asked to memorise the guided trajectory while their electroencephalogram (EEG) was recorded. During the experiment, only the haptic modality was maintained (i.e. unimodal) by keeping the task workspace visually hidden. Results show a clear trend: subjects who exhibited a performance improvement, were characterized by a stronger desynchronization of the beta rhythms over the contralateral hemisphere. This is in agreement with recent studies showing that contralateral beta rhythms changes are associated with motor skills retention. Moreover, under the assumption that SMR are indeed a marker of engagement, our results represent accumulating evidence that active participation is crucial for haptic training. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/SMC.2018.00098 | 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC) |
Keywords | Field | DocType |
Haptic guidance,motor learning,engagement,sensorimotor rhythm (SMR),electroencephalogram (EEG) | Modalities,Neural correlates of consciousness,Motor learning,Computer science,Motor skill,Artificial intelligence,Physical medicine and rehabilitation,Sensory system,Rhythm,Haptic technology,Electroencephalography,Machine learning | Conference |
ISSN | ISBN | Citations |
1062-922X | 978-1-5386-6651-7 | 0 |
PageRank | References | Authors |
0.34 | 2 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Asuka Takai | 1 | 0 | 0.68 |
| Diletta Rivela | 2 | 0 | 0.34 |
| Giuseppe Lisi | 3 | 2 | 0.75 |
| Tomoyuki Noda | 4 | 90 | 14.35 |
| Tatsuya Teramae | 5 | 46 | 8.32 |
| Hiroshi Imamizu | 6 | 39 | 6.50 |
| Jun Morimoto | 7 | 296 | 24.17 |