Items where Author is "Chan, Y.L."

Ho, C.S. and Chan, Y.L. and Tan, T.W. and Tay, G.W. and Tang, T.B. (2022) Improving the diagnostic accuracy for major depressive disorder using machine learning algorithms integrating clinical and near-infrared spectroscopy data. Journal of Psychiatric Research, 147. pp. 194-202.

Lim, L.G. and Ung, W.C. and Chan, Y.L. and Lu, C.-K. and Funane, T. and Kiguchi, M. and Tang, T.B. (2021) Optimizing mental workload estimation by detecting baseline state using vector phase analysis approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 29. pp. 597-606.

Chan, Y.L. and Ung, W.C. and Lim, L.G. and Lu, C.-K. and Kiguchi, M. and Tang, T.B. (2020) Automated Thresholding Method for fNIRS-Based Functional Connectivity Analysis: Validation with a Case Study on Alzheimer's Disease. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 28 (8). pp. 1691-1701.

Chan, Y.L. and Ung, W.C. and Lim, L.G. and Lu, C.-K. and Kiguchi, M. and Tang, T.B. (2020) Automated Thresholding Method for fNIRS-Based Functional Connectivity Analysis: Validation with a Case Study on Alzheimer's Disease. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 28 (8). pp. 1691-1701.

Chan, Y.L. and Boon Tang, T. (2020) Characterizing Functional Near Infrared Spectroscopy (fNIRS)-based Connectivity as Cost-effective Small World Network using Orthogonal Minimal Spanning Trees (OMSTs). In: UNSPECIFIED.

Lim, L.G. and Ung, W.C. and Chan, Y.L. and Lu, C.-K. and Sutoko, S. and Funane, T. and Kiguchi, M. and Tang, T.B. (2020) A unified analytical framework with multiple fNIRS features for mental workload assessment in the prefrontal cortex. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 28 (11). pp. 2367-2376.

Lim, L.G. and Ung, W.C. and Chan, Y.L. and Lu, C.-K. and Sutoko, S. and Funane, T. and Kiguchi, M. and Tang, T.B. (2020) A unified analytical framework with multiple fNIRS features for mental workload assessment in the prefrontal cortex. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 28 (11). pp. 2367-2376.