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.
| Original language | English |
|---|---|
| Pages (from-to) | 378-388 |
| Number of pages | 11 |
| Journal | Assistive Technology: The Offical Journal of RESNA |
| Volume | 35 |
| Issue number | 5 |
| DOIs | |
| Publication status | Published - 11 Jul 2022 |
Keywords
- Artificial Intelligence
- Signal Processing
- brain-neuronal computer interface
- human-computer interaction
- brain–computer interface (BCI)
- musical composition
- dry electroencephalogram (EEG)
- computer music
- musical performance
ASJC Scopus subject areas
- Physical Therapy, Sports Therapy and Rehabilitation
- Rehabilitation
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