SVM weight topography shows central and parietal electrodes contribute most to decoding, with eyes-focused participants showing more right-central contribution¶

Claim · All claims
Evidence · 1 study (2 entries) · associational r2 · q2
Currency · newest study 2025 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q2 i? Feature importance analysis of trained SVM weights showed central and parietal areas contributed most to neural representations for face recognition in both ERP and high-alpha decoding. → Guoyang Liu 2025 q2 i? In the high-alpha band, nose-focused participants showed stronger left central-parietal contributions whereas eyes-focused participants showed stronger right central contributions. → Guoyang Liu 2025 (2)

Evidence¶

Guoyang Liu 2025¶

Guoyang Liu, Yueyuan Zheng, Michelle Hei Lam Tsang, Yazhou Zhao & Janet H. Hsiao. (2025). Understanding the role of eye movement pattern and consistency during face recognition through EEG decoding. npj Science of Learning. https://doi.org/10.1038/s41539-025-00316-3

q2 · i? · associational · r2

Feature importance analysis of trained linear-SVM weights for ERP decoding (Fig. 5) and high-alpha decoding (Fig. 6a): "the electrodes in the central and parietal areas contributed the most to the neural representations for face recognition", consistent with widely distributed late ERP responses.

"The results showed that the electrodes in the central and parietal areas contributed the most to the neural representations for face recognition."

Guoyang Liu 2025 (2)¶

Guoyang Liu, Yueyuan Zheng, Michelle Hei Lam Tsang, Yazhou Zhao & Janet H. Hsiao. (2025). Understanding the role of eye movement pattern and consistency during face recognition through EEG decoding. npj Science of Learning. https://doi.org/10.1038/s41539-025-00316-3

q2 · i? · associational · r2

Group-wise topography maps of high-alpha SVM weights (Fig. 6b, c): "in eyes-fo cused participants, electrodes in the right central region contribut ed more", which the authors link to prior findings that right-parietal alpha power reflects task-focused attention.

"in participants in the nose-focused gro up, electrodes in the left central and parietal region contributed mo re to the neural representation for face recognition, whereas in eyes-fo cused participants, electrodes in the right central region contribut ed more."

Discussion¶

Related Claims¶

• A more eye-focused eye movement pattern is associated with higher EEG decoding accuracy for old vs new faces in the high-alpha band — related
• ERP and alpha-band EEG signals decode old vs new faces above chance, with the high-alpha band more discriminative than the low-alpha band — related
• Random Forest and SHAP analyses identify Gamma and Beta band power at temporal and frontal channels (T3, P3, F7, T4, T4, F8, F7) as the most critical features for EEG familiarity classification — related