Improving the Performance of Individually Calibrated SSVEP-BCI by Task- Discriminant Component Analysis

Liu, Bingchuan; Chen, Xiaogang; Shi, Nanlin; Wang, Yijun; Gao, Shangkai; Gao, Xiaorong

doi:10.1109/tnsre.2021.3114340

Cited by 104 publications

(78 citation statements)

References 42 publications

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“…However, calibration-free approaches need a long stimulation duration to achieve high accuracy, leading to low ITR. To increase ITR when calibration data can be obtained, the SSVEP-based BCI system can use calibration-based approaches to construct specific templates and spatial filters for the subjects, e.g., extended CCA (eCCA) [11], modified eCCA (m-eCCA) [12], L1-regularized multiway CCA (L1MCCA) [13], task-related component analysis (TRCA) [14], task-discriminant component analysis [15]. These approaches improve the performance of SSVEP.…”

Section: Introductionmentioning

confidence: 99%

Overview of the winning approaches in BCI Controlled Robot Contest in World Robot Contest 2021: Calibration-free SSVEP

Bian

2022

Brain Science Advances

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Recently, steady-state visual evoked potential (SSVEP) has become one of the most popular electroencephalography paradigms due to its high information transfer rate. Several approaches have been proposed to improve the performance of SSVEP. The calibration-free scenario is significant in SSVEP-based brain–computer interface systems, where the subject is the first time to use the system. The participating teams proposed several effective calibration-free algorithm frameworks in the SSVEP competition (calibration-free) of the BCI Controlled Robot Contest in World Robot Contest 2021. This paper introduces the approaches used in the algorithms of the top five teams in the final. The results of the five subjects in the final proved the effectiveness of the approaches. This paper discusses the effectiveness of each approach in improving the system performance in the calibration-free scenario and gives suggestions on how to use these approaches in a real-world system.

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Section: Introductionmentioning

confidence: 99%

Overview of the winning approaches in BCI Controlled Robot Contest in World Robot Contest 2021: Calibration-free SSVEP

Bian

2022

Brain Science Advances

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“…Since SSVEP-based BCI has the advantages of high communication rate and no or less training [28]- [30], this study adopted SSVEP-based BCI to send examinees' commands. A 9-target SSVEP-BCI was realized in this study.…”

Section: Discussionmentioning

confidence: 99%

Development of a Brain-Computer Interface-Based Symbol Digit Modalities Test and Validation in Healthy Elderly Volunteers and Stroke Patients

Chen

Gao

2022

IEEE Trans. Neural Syst. Rehabil. Eng.

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Standard cognitive assessment tools often involve motor or verbal responses, making them impossible for severely motor-disabled individuals. Brain-computer interfaces (BCIs) are expected to help severely motor-impaired individuals to perform cognitive assessment because BCIs can circumvent motor and verbal requirements. Currently, the field of research to develop cognitive tasks based on BCI is still in its nascent stage and needs further development. This study explored the possibility of developing a BCI version of symbol digit modalities test (BCI-SDMT). Steady-state visual evoked potential (SSVEP) was adopted to build the BCI and a 9-target SSVEP-BCI was realized to send examinees' responses.A training-free algorithm (i.e., filter bank canonical correlation analysis) was used for SSVEP identification. Thus, examinees are able to start the proposed BCI-SDMT immediately. Eighty-nine healthy elderly volunteers and 9 stroke patients were enrolled to validate the technical feasibility of the developed BCI-SDMT. For all participants, the average recognition accuracies of the developed BCI and BCI-SDMT were 93.89 ± 8.48% and 92.58 ± 10.52%, respectively, were considerably above the chance level (i.e., 11.11%). These results indicated that both healthy elderly volunteers and stroke patients could elicit sufficient SSVEPs to control the BCI. Furthermore, patient use of the developed BCI-SDMT was unaffected by the presence of motor impairment. They could understand instructions, pair numbers with specific symbols, and send commands using the BCI. The proposed BCI-SDMT can be used as a

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“…The evolution of the BCI systems also enhances our understanding of frequency recognition methods in SSVEP-BCI. For the frequency recognition, continuous efforts have been focused to improve the classification performance, such as extended CCA [13], task-related component analysis (TRCA) [6], multi-stimulus taskrelated component analysis (msTRCA) [14], and taskdiscriminant component analysis (TDCA) [15]. These efforts improved the ITR of the system by enhancing the classification accuracy and reducing the required selection time, both of which are important in the calculation of ITR, apart from the number of stimuli.…”

Section: Introductionmentioning

confidence: 99%

A Spectrally-Dense Encoding Method for Designing a High-Speed SSVEP-BCI With 120 Stimuli

Chen

Liu

Wang

et al. 2022

IEEE Trans. Neural Syst. Rehabil. Eng.

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The practical functionality of a braincomputer interface (BCI) is critically affected by the number of stimuli, especially for steady-state visual evoked potential based BCI (SSVEP-BCI), which shows promise for the implementation of a multi-target system for real-world applications. Joint frequency-phase modulation (JFPM) is an effective and widely used method in modulating SSVEPs. However, the ability of JFPM to implement an SSVEP-BCI system with a large number of stimuli, e.g., over 100 stimuli, remains unclear. To address this issue, a spectrally-dense JPFM (sJFPM) method is proposed to encode a broad array of stimuli, which modulates the lowand medium-frequency SSVEPs with a frequency interval of 0.1 Hz and triples the number of stimuli in conventional SSVEP-BCI to 120. To validate the effectiveness of the proposed 120-target BCI system, an offline experiment and a subsequent online experiment testing 18 healthy subjects in total were conducted. The offline experiment verified the feasibility of using sJFPM in designing an SSVEP-BCI system with 120 stimuli. Furthermore, the online experiment demonstrated that the proposed system achieved an average performance of 92.47±1.83% in online accuracy and 213.23 ± 6.60 bits/min in online information transfer rate (ITR), where more than 75% of the subjects attained the accuracy above 90% and the ITR above 200 bits/min. This present study demonstrates the effectiveness of sJFPM in elevating the number of stimuli to more than 100 and extends our understanding of encoding a large number of stimuli by means of finer frequency division.

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Improving the Performance of Individually Calibrated SSVEP-BCI by Task- Discriminant Component Analysis

Cited by 104 publications

References 42 publications

Overview of the winning approaches in BCI Controlled Robot Contest in World Robot Contest 2021: Calibration-free SSVEP

Overview of the winning approaches in BCI Controlled Robot Contest in World Robot Contest 2021: Calibration-free SSVEP

Development of a Brain-Computer Interface-Based Symbol Digit Modalities Test and Validation in Healthy Elderly Volunteers and Stroke Patients

A Spectrally-Dense Encoding Method for Designing a High-Speed SSVEP-BCI With 120 Stimuli

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