SSVEP-based brain–computer interface for music using a low-density EEG system
| dc.contributor.author | Venkatesh, Satvik | |
| dc.contributor.author | Miranda, Eduardo Reck | |
| dc.contributor.author | Braund, Edward | |
| dc.date.accessioned | 2025-05-08T12:11:34Z | |
| dc.date.available | 2022-07-11T00:00:00Z | |
| dc.date.available | 2025-05-08T12:11:34Z | |
| dc.date.issued | 2022-07-11 | |
| dc.identifier.citation | Venkatesh S, Miranda ER, Braund E (2022) 'SSVEP-based brain–computer interface for music using a low-density EEG system', Assistive Technology: The Offical Journal of RESNA, 35 (5), pp.378-388. | en_US |
| dc.identifier.issn | 1040-0435 | |
| dc.identifier.doi | 10.1080/10400435.2022.2084182 | |
| dc.identifier.uri | http://hdl.handle.net/10547/626644 | |
| dc.description.abstract | In this paper, we present a bespoke brain–computer interface (BCI), which was developed for a person with severe motor-impairments, who was previously a Violinist, to allow performing and composing music at home. It uses steady-state visually evoked potential (SSVEP) and adopts a dry, low-density, and wireless electroencephalogram (EEG) headset. In this study, we investigated two parameters: (1) placement of the EEG headset and (2) inter-stimulus distance and found that the former significantly improved the information transfer rate (ITR). To analyze EEG, we adopted canonical correlation analysis (CCA) without weight-calibration. The BCI for musical performance realized a high ITR of 37.59 ± 9.86 bits min−1 and a mean accuracy of 88.89 ± 10.09%. The BCI for musical composition obtained an ITR of 14.91 ± 2.87 bits min−1 and a mean accuracy of 95.83 ± 6.97%. The BCI was successfully deployed to the person with severe motor-impairments. She regularly uses it for musical composition at home, demonstrating how BCIs can be translated from laboratories to real-world scenarios. | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | Taylor and Francis | en_US |
| dc.relation.url | https://www.tandfonline.com/doi/full/10.1080/10400435.2022.2084182 | en_US |
| dc.rights | Green - can archive pre-print and post-print or publisher's version/PDF | |
| dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 International | * |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
| dc.subject | brain-neuronal computer interface | en_US |
| dc.subject | human-computer interaction | en_US |
| dc.subject | signal processing | en_US |
| dc.subject | Subject Categories::G440 Human-computer Interaction | en_US |
| dc.title | SSVEP-based brain–computer interface for music using a low-density EEG system | en_US |
| dc.type | Article | en_US |
| dc.contributor.department | University of Plymouth | en_US |
| dc.identifier.journal | Assistive Technology: The Offical Journal of RESNA | en_US |
| dc.date.updated | 2025-05-08T12:08:43Z | |
| dc.description.note | gold oa | |
| refterms.dateFOA | 2025-05-08T12:11:35Z |


