A sensory feedback was employed for the present work to remap brain signals into sensory information. In particular, sensorimotor rhythms associated with motor imagery were measured as a mean to interact with an extended reality (XR) environment. The aim for such a neurofeedback was to let the user become aware of his/her ability to imagine a movement. A brain-computer interface based on motor imagery was thus implemented by using a consumer-grade electroencephalograph and by taking into account wearable and portable feedback actuators. Visual and vibrotactile sensory feedback modalities were used simultaneously to provide an engaging multimodal feedback in XR. Both a non-immersive and an immersive version of the system were considered and compared. Preliminary validation was carried out with four healthy subjects participating in a total of four sessions on different days. Experiments were conducted according to a wide-spread synchronous paradigm in which an application provides the timing for the motor imagery tasks. Performance was compared in terms of classification accuracy. Overall, subjects preferred the immersive neurofeedback because it allowed higher concentration during experiments, but there was not enough evidence to prove its actual effectiveness and mean classification accuracy resulted about 65%. Meanwhile, classification accuracy resulted higher with the non-immersive neurofeedback, notably it reached about 75%. Future experiments could extend this comparison to more subjects and more sessions, due to the relevance of possible applications in rehabilitation. Moreover, the immersive XR implementation could be improved to provide a greater sense of embodiment.
|Title of host publication||International Conference on Extended Reality|
|Editors||Lucio Tommaso De Paolis, Pasquale Arpaia, Marco Sacco|
|Publisher||Springer Nature Switzerland AG|
|Number of pages||12|
|Publication status||Published (in print/issue) - 28 Aug 2022|
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
Bibliographical noteFunding Information:
Acknowledgement. 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 Giovanni D’Errico and Stefania Di Rienzo for supporting system design and data analyses.
© 2022, Springer Nature Switzerland AG.
- Brain computer interface
- Extended Reality
- Motor Imagery
- Brain-computer interface
- Extended reality
- Motor imagery