Automating Stimulation Frequency Selection for SSVEP-Based Brain-Computer Interfaces
Brain–computer interfaces (BCIs) based on steady-state visually evoked potentials (SSVEPs) are inexpensive and do not require user training. However, the highly personalized reaction to visual stimulation is an obstacle to the wider application of this technique, as it can be ineffective, tiring, or...
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| Published in | Algorithms Vol. 16; no. 11; p. 502 |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Basel
MDPI AG
01.10.2023
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1999-4893 1999-4893 |
| DOI | 10.3390/a16110502 |
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| Abstract | Brain–computer interfaces (BCIs) based on steady-state visually evoked potentials (SSVEPs) are inexpensive and do not require user training. However, the highly personalized reaction to visual stimulation is an obstacle to the wider application of this technique, as it can be ineffective, tiring, or even harmful at certain frequencies. In our experimental study, we proposed a new approach to the selection of optimal frequencies of photostimulation. By using a custom photostimulation device, we covered a frequency range from 5 to 25 Hz with 1 Hz increments, recording the subjects’ brainwave activity (EEG) and analyzing the signal-to-noise ratio (SNR) changes at the corresponding frequencies. The proposed set of SNR-based coefficients and the discomfort index, determined by the ratio of theta and beta rhythms in the EEG signal, enables the automation of obtaining the recommended stimulation frequencies for use in SSVEP-based BCIs. |
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| AbstractList | Brain–computer interfaces (BCIs) based on steady-state visually evoked potentials (SSVEPs) are inexpensive and do not require user training. However, the highly personalized reaction to visual stimulation is an obstacle to the wider application of this technique, as it can be ineffective, tiring, or even harmful at certain frequencies. In our experimental study, we proposed a new approach to the selection of optimal frequencies of photostimulation. By using a custom photostimulation device, we covered a frequency range from 5 to 25 Hz with 1 Hz increments, recording the subjects’ brainwave activity (EEG) and analyzing the signal-to-noise ratio (SNR) changes at the corresponding frequencies. The proposed set of SNR-based coefficients and the discomfort index, determined by the ratio of theta and beta rhythms in the EEG signal, enables the automation of obtaining the recommended stimulation frequencies for use in SSVEP-based BCIs. |
| Audience | Academic |
| Author | Bakaev, Maxim Razumnikova, Olga Gerasimov, Anton Kozin, Alexey Pashkov, Anton |
| Author_xml | – sequence: 1 givenname: Alexey orcidid: 0009-0008-2242-2649 surname: Kozin fullname: Kozin, Alexey – sequence: 2 givenname: Anton surname: Gerasimov fullname: Gerasimov, Anton – sequence: 3 givenname: Maxim orcidid: 0000-0002-1889-0692 surname: Bakaev fullname: Bakaev, Maxim – sequence: 4 givenname: Anton orcidid: 0000-0002-2403-3136 surname: Pashkov fullname: Pashkov, Anton – sequence: 5 givenname: Olga orcidid: 0000-0002-7831-9404 surname: Razumnikova fullname: Razumnikova, Olga |
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| SubjectTerms | Accuracy Algorithms Analysis Brain research brain–computer interface Calibration Communication Consciousness discomfort index Electroencephalography Frequency ranges frequency selection algorithm Human-computer interface personal response Power Signal to noise ratio Spinal cord injuries steady-state visually evoked potentials Stimulation Usability User experience User interface User satisfaction User training visual stimulation |
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| Title | Automating Stimulation Frequency Selection for SSVEP-Based Brain-Computer Interfaces |
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