OBJECTIVES: The primary objective was to test whether motor imagery (MI) questionnaires can be used to detect BCI 'illiterate'. The second objective was to test how different MI paradigms, with and without the physical presence of the goal of an action, influence a BCI classifier. METHODS: Kinaesthetic (KI) and visual (VI) motor imagery questionnaires were administered to 30 healthy volunteers. Their EEG was recorded during a cue-based, simple imagery (SI) and goal oriented imagery (GOI). RESULTS: The strongest correlation (Pearson r(2)=0.53, p=1.6e-5) was found between KI and SI, followed by a moderate correlation between KI and GOI (r(2)=0.33, p=0.001) and a weak correlation between VI and SI (r(2)=0.21, p=0.022) and VI and GOI (r(2)=0.17, p=0.05). Classification accuracy was similar for SI (71.1 ± 7.8%) and GOI (70.5 ± 5.9%) though corresponding classification features differed in 70% participants. Compared to SI, GOI improved the classification accuracy in 'poor' imagers while reducing the classification accuracy in 'very good' imagers. CONCLUSION: The KI score could potentially be a useful tool to predict the performance of a MI based BCI. The physical presence of the object of an action facilitates motor imagination in 'poor' able-bodied imagers. SIGNIFICANCE: Although this study shows results on able-bodied people, its general conclusions should be transferable to BCI based on MI for assisted rehabilitation of the upper extremities in patients.
OBJECTIVES: The primary objective was to test whether motor imagery (MI) questionnaires can be used to detect BCI 'illiterate'. The second objective was to test how different MI paradigms, with and without the physical presence of the goal of an action, influence a BCI classifier. METHODS: Kinaesthetic (KI) and visual (VI) motor imagery questionnaires were administered to 30 healthy volunteers. Their EEG was recorded during a cue-based, simple imagery (SI) and goal oriented imagery (GOI). RESULTS: The strongest correlation (Pearson r(2)=0.53, p=1.6e-5) was found between KI and SI, followed by a moderate correlation between KI and GOI (r(2)=0.33, p=0.001) and a weak correlation between VI and SI (r(2)=0.21, p=0.022) and VI and GOI (r(2)=0.17, p=0.05). Classification accuracy was similar for SI (71.1 ± 7.8%) and GOI (70.5 ± 5.9%) though corresponding classification features differed in 70% participants. Compared to SI, GOI improved the classification accuracy in 'poor' imagers while reducing the classification accuracy in 'very good' imagers. CONCLUSION: The KI score could potentially be a useful tool to predict the performance of a MI based BCI. The physical presence of the object of an action facilitates motor imagination in 'poor' able-bodied imagers. SIGNIFICANCE: Although this study shows results on able-bodied people, its general conclusions should be transferable to BCI based on MI for assisted rehabilitation of the upper extremities in patients.
Authors: Sam Darvishi; Alireza Gharabaghi; Michael C Ridding; Derek Abbott; Mathias Baumert Journal: IEEE J Transl Eng Health Med Date: 2018-11-09 Impact factor: 3.316
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