Simulation Results¶

In this simulation, the SSVEP templates generated by the proposed stimulus-stimulus method (without using calibration data of target stimuli) is compared with

SSVEP templates without using calibration data of target stimuli
- Sine-cosine signals (CCA [1])
- SSVEP templates generated by the superposition theory based SSVEP signal model (tlCCA [2])
SSVEP templates using calibration data of target stimuli
- Averaged signals (eCCA [3])

Comparisons of classification accuracy and ITR of porposed method and other methods without using calibration data of target stimuli¶

https://github.com/pikipity/Stimulus-stimulus_transfer_based_on_time-frequency-joint_representation_SSVEP_BCIs/blob/main/ITR_Acc_Summary/ITR_summary_nocalibration_random_8.png?raw=true

https://github.com/pikipity/Stimulus-stimulus_transfer_based_on_time-frequency-joint_representation_SSVEP_BCIs/blob/main/ITR_Acc_Summary/Acc_summary_nocalibration_random_8.png?raw=true

Comparisons of classification accuracy and ITR of porposed method and method using calibration data of target stimuli¶

Individual maximum ITR¶

https://github.com/pikipity/Stimulus-stimulus_transfer_based_on_time-frequency-joint_representation_SSVEP_BCIs/blob/main/ITR_Acc_Summary/ITR_max_compare_all_8.png?raw=true

References¶

1. Lin, C. Zhang, W. Wu, and X. Gao, “Frequency recognition based on canonical correlation analysis for SSVEP-based BCIs,” IEEE Trans. Biomed. Eng., vol. 53, no. 12, pp. 2610-2614, 2006, doi: 10.1109/TBME.2006.886577.
1. 1. Wong, Z. Wang, A. C. Rosa, C. L. P. Chen, T.-P. Jung, Y. Hu, and F. Wan, “Transferring subject-specific knowledge across stimulus frequencies in SSVEP-based BCIs,” IEEE Trans. Automat. Sci. Eng., vol. 18, no. 2, pp. 552-563, 2021, doi: 10.1109/TASE.2021.3054741.
1. Nakanishi, Y. Wang, Y.-T. Wang, Y. Mitsukura, and T.-P. Jung, “A high-speed brain speller using steady-state visual evoked potentials,” Int. J. Neur. Syst., vol. 24, no. 06, p. 1450019, 2014, doi: 10.1142/S0129065714500191.