SSVEP-based Brain-computer Interface for Music using a Low-density EEG System
dc.contributor.author | Venkatesh, S | |
dc.contributor.author | Miranda, Eduardo | |
dc.contributor.author | Braund, Edward | |
dc.date.accessioned | 2022-07-01T14:42:41Z | |
dc.date.available | 2022-07-01T14:42:41Z | |
dc.date.issued | 2022-06-17 | |
dc.identifier.issn | 1040-0435 | |
dc.identifier.issn | 1949-3614 | |
dc.identifier.uri | http://hdl.handle.net/10026.1/19380 | |
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. | |
dc.format.extent | 378-388 | |
dc.format.medium | Print-Electronic | |
dc.language | en | |
dc.language.iso | eng | |
dc.publisher | Taylor and Francis | |
dc.subject | brain-computer interface (BCI) | |
dc.subject | computer music | |
dc.subject | dry electroencephalogram (EEG) | |
dc.subject | musical composition | |
dc.subject | musical performance | |
dc.title | SSVEP-based Brain-computer Interface for Music using a Low-density EEG System | |
dc.type | journal-article | |
dc.type | Journal Article | |
plymouth.author-url | https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000823657600001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=11bb513d99f797142bcfeffcc58ea008 | |
plymouth.issue | 5 | |
plymouth.volume | 35 | |
plymouth.publication-status | Published | |
plymouth.journal | Assistive Technology | |
dc.identifier.doi | 10.1080/10400435.2022.2084182 | |
plymouth.organisational-group | /Plymouth | |
plymouth.organisational-group | /Plymouth/Faculty of Arts, Humanities and Business | |
plymouth.organisational-group | /Plymouth/Users by role | |
plymouth.organisational-group | /Plymouth/Users by role/Academics | |
dc.publisher.place | United States | |
dcterms.dateAccepted | 2022-05-21 | |
dc.rights.embargodate | 2022-7-2 | |
dc.identifier.eissn | 1949-3614 | |
dc.rights.embargoperiod | Not known | |
rioxxterms.versionofrecord | 10.1080/10400435.2022.2084182 | |
rioxxterms.licenseref.uri | http://www.rioxx.net/licenses/all-rights-reserved | |
rioxxterms.type | Journal Article/Review |