Sommer, W.; Stapor, K.; Kończak, G.; Kotowski, K.; Fabian, P.; Ochab, J.; Bereś, A.; Ślusarczyk, G.
Changepoint Detection in Noisy Data Using a Novel Residuals Permutation-Based Method (RESPERM): Benchmarking and Application to Single Trial ERPs. Brain Sci. 2022, 12, 525.
https://doi.org/10.3390/brainsci12050525
AMA Style
Sommer W, Stapor K, Kończak G, Kotowski K, Fabian P, Ochab J, Bereś A, Ślusarczyk G.
Changepoint Detection in Noisy Data Using a Novel Residuals Permutation-Based Method (RESPERM): Benchmarking and Application to Single Trial ERPs. Brain Sciences. 2022; 12(5):525.
https://doi.org/10.3390/brainsci12050525
Chicago/Turabian Style
Sommer, Werner, Katarzyna Stapor, Grzegorz Kończak, Krzysztof Kotowski, Piotr Fabian, Jeremi Ochab, Anna Bereś, and Grażyna Ślusarczyk.
2022. "Changepoint Detection in Noisy Data Using a Novel Residuals Permutation-Based Method (RESPERM): Benchmarking and Application to Single Trial ERPs" Brain Sciences 12, no. 5: 525.
https://doi.org/10.3390/brainsci12050525
APA Style
Sommer, W., Stapor, K., Kończak, G., Kotowski, K., Fabian, P., Ochab, J., Bereś, A., & Ślusarczyk, G.
(2022). Changepoint Detection in Noisy Data Using a Novel Residuals Permutation-Based Method (RESPERM): Benchmarking and Application to Single Trial ERPs. Brain Sciences, 12(5), 525.
https://doi.org/10.3390/brainsci12050525