A multimodal sensory feedback was exploited in the present study to improve the detection of neurological phenomena associated with motor imagery. At this aim, visual and haptic feedback were simultaneously delivered to the user of a brain-computer interface. The motor imagery-based brain-computer interface was built by using a wearable and portable electroencephalograph with only eight dry electrodes, a haptic suit, and a purposely implemented virtual reality application. Preliminary experiments were carried out with six subjects participating in five sessions on different days. The subjects were randomly divided into “control group” and “neurofeedback group”. The former performed pure motor imagery without receiving any feedback, while the latter received multimodal feedback as a response to their imaginative act. Results of a cross validation showed that at most 61% of classification accuracy was achieved in performing the pure motor imagination. On the contrary, subjects of the “neurofeedback group” achieved up to 82% mean accuracy, with a peak of 91% in one of the sessions. However, no improvement in pure motor imagery was observed, either when practicing with pure motor imagery or with feedback.
|Title of host publication||2022 IEEE International Workshop on Metrology for Extended Reality, Artificial Intelligence and Neural Engineering, MetroXRAINE 2022 - Proceedings|
|Number of pages||6|
|ISBN (Electronic)||978-1-6654-8574-6, 978-1-6654-8573-9|
|Publication status||Published (in print/issue) - 5 Dec 2022|
|Event||IEEE International Conference on Metrology for Extended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE) - Rome, Italy|
Duration: 26 Oct 2022 → 28 Oct 2022
|Conference||IEEE International Conference on Metrology for Extended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE)|
|Period||26/10/22 → 28/10/22|
Bibliographical noteFunding Information:
This work was carried out as part of the "ICT for Health" project, which was financially supported by the Italian Ministry of Education, University and Research (MIUR), under the initiative ‘Departments of Excellence’ (Italian Budget Law no. 232/2016), through an excellence grant awarded to the Department of Information Technology and Electrical Engineering of the University of Naples Federico II, Naples, Italy. The authors thank also thank Emanuele Cirillo, Stefania Di Rienzo, and Bianca Sorvillo for supporting the experiments.
© 2022 IEEE.
- brain-computer interface
- motor imagery
- extended reality
- dry electrodes
- dry elecrtrodes