Enhancing SSVEP Identification With Less Individual Calibration Data Using Periodically Repeated Component Analysis
OBJECTIVE: Spatial filtering and template matching-based steady-state visually evoked potentials (SSVEP) identification methods usually underperform in SSVEP identification with small-sample-size calibration data, especially when a single trial of data is available for each stimulation frequency.
METHODS: In contrast to the state-of-the-art task-related component analysis (TRCA)-based methods, which construct spatial filters and SSVEP templates based on the inter-trial task-related components in SSVEP, this study proposes a method called periodically repeated component analysis (PRCA), which constructs spatial filters to maximize the reproducibility across periods and constructs synthetic SSVEP templates by replicating the periodically repeated components (PRCs). We also introduced PRCs into two improved variants of TRCA. Performance evaluation was conducted in a self-collected 16-target dataset, a public 40-target dataset, and an online experiment.
RESULTS: The proposed methods show significant performance improvements with less training data and can achieve comparable performance to the baseline methods with 5 trials by using 2 or 3 training trials. Using a single trial of calibration data for each frequency, the PRCA-based methods achieved the highest average accuracies of over 95% and 90% with a data length of 1 s and maximum average information transfer rates (ITR) of 198.8±57.3 bits/min and 191.2±48.1 bits/min for the two datasets, respectively. Averaged online accuracy of 94.00 ± 7.35% and ITR of 139.73±21.04 bits/min were achieved with 0.5-s calibration data per frequency.
SIGNIFICANCE: Our results demonstrate the effectiveness and robustness of PRCA-based methods for SSVEP identification with reduced calibration effort and suggest its potential for practical applications in SSVEP-BCIs.
| Medienart: |
E-Artikel |
|---|
| Erscheinungsjahr: |
2024 |
|---|---|
| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:71 |
|---|---|
| Enthalten in: |
IEEE transactions on bio-medical engineering - 71(2024), 4 vom: 15. März, Seite 1319-1331 |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Ke, Yufeng [VerfasserIn] |
|---|
| Links: |
|---|
| Themen: |
|---|
| Anmerkungen: |
Date Completed 21.03.2024 Date Revised 21.03.2024 published: Print-Electronic Citation Status MEDLINE |
|---|
| doi: |
10.1109/TBME.2023.3333435 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
NLM364634855 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | NLM364634855 | ||
| 003 | DE-627 | ||
| 005 | 20240321235620.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 231226s2024 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1109/TBME.2023.3333435 |2 doi | |
| 028 | 5 | 2 | |a pubmed24n1338.xml |
| 035 | |a (DE-627)NLM364634855 | ||
| 035 | |a (NLM)37971909 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Ke, Yufeng |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Enhancing SSVEP Identification With Less Individual Calibration Data Using Periodically Repeated Component Analysis |
| 264 | 1 | |c 2024 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ƒaComputermedien |b c |2 rdamedia | ||
| 338 | |a ƒa Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a Date Completed 21.03.2024 | ||
| 500 | |a Date Revised 21.03.2024 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a OBJECTIVE: Spatial filtering and template matching-based steady-state visually evoked potentials (SSVEP) identification methods usually underperform in SSVEP identification with small-sample-size calibration data, especially when a single trial of data is available for each stimulation frequency | ||
| 520 | |a METHODS: In contrast to the state-of-the-art task-related component analysis (TRCA)-based methods, which construct spatial filters and SSVEP templates based on the inter-trial task-related components in SSVEP, this study proposes a method called periodically repeated component analysis (PRCA), which constructs spatial filters to maximize the reproducibility across periods and constructs synthetic SSVEP templates by replicating the periodically repeated components (PRCs). We also introduced PRCs into two improved variants of TRCA. Performance evaluation was conducted in a self-collected 16-target dataset, a public 40-target dataset, and an online experiment | ||
| 520 | |a RESULTS: The proposed methods show significant performance improvements with less training data and can achieve comparable performance to the baseline methods with 5 trials by using 2 or 3 training trials. Using a single trial of calibration data for each frequency, the PRCA-based methods achieved the highest average accuracies of over 95% and 90% with a data length of 1 s and maximum average information transfer rates (ITR) of 198.8±57.3 bits/min and 191.2±48.1 bits/min for the two datasets, respectively. Averaged online accuracy of 94.00 ± 7.35% and ITR of 139.73±21.04 bits/min were achieved with 0.5-s calibration data per frequency | ||
| 520 | |a SIGNIFICANCE: Our results demonstrate the effectiveness and robustness of PRCA-based methods for SSVEP identification with reduced calibration effort and suggest its potential for practical applications in SSVEP-BCIs | ||
| 650 | 4 | |a Journal Article | |
| 700 | 1 | |a Liu, Shuang |e verfasserin |4 aut | |
| 700 | 1 | |a Ming, Dong |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t IEEE transactions on bio-medical engineering |d 1963 |g 71(2024), 4 vom: 15. März, Seite 1319-1331 |w (DE-627)NLM000003913 |x 1558-2531 |7 nnns |
| 773 | 1 | 8 | |g volume:71 |g year:2024 |g number:4 |g day:15 |g month:03 |g pages:1319-1331 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.1109/TBME.2023.3333435 |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a GBV_NLM | ||
| 951 | |a AR | ||
| 952 | |d 71 |j 2024 |e 4 |b 15 |c 03 |h 1319-1331 | ||