# Cross-Dataset Transfer

Electrode caps differ in channel count and layout across laboratories, so recordings do not even share an observation space, and cross-dataset EEG decoding has stayed limited. hTSMNet places a linear map generated from electrode coordinates in front of the decoding backbone, projecting any montage onto 15 fixed scalp anchors; the map has no subject-specific parameters, so unseen subjects and datasets align zero-shot. On a leave-one-dataset-out motor-imagery benchmark of three datasets and 172 subjects it matches the strongest comparator at strict zero-shot and reaches the highest accuracy on every dataset once a few calibration trials refit only the final classifier.

Electrode caps differ in channel count and layout across laboratories, so recordings do not even share an observation space, and cross-dataset EEG decoding has stayed limited. hTSMNet places a linear map generated from electrode coordinates in front of the decoding backbone, projecting any montage onto 15 fixed scalp anchors; the map has no subject-specific parameters, so unseen subjects and datasets align zero-shot. On a leave-one-dataset-out motor-imagery benchmark of three datasets and 172 subjects it matches the strongest comparator at strict zero-shot and reaches the highest accuracy on every dataset once a few calibration trials refit only the final classifier.

When a target occupies only a small part of the frame, whole-image quality scores are dominated by background, and public benchmarks carry no target-level human opinion scores. BiCG-Net expands the target box into a nested patch sequence, reads it with 11 frozen image quality models, and subtracts two readings from the same image to cancel the target term and teacher bias by construction. On CSIQ and LIVEC the student reaches PLCCs of 0.9009 and 0.9133 against human MOS, above both the best single teacher and the teacher consensus; frozen and transferred zero-shot from CSIQ to LIVEC it reaches 0.8416, 0.1947 above consensus.

When a target occupies only a small part of the frame, whole-image quality scores are dominated by background, and public benchmarks carry no target-level human opinion scores. BiCG-Net expands the target box into a nested patch sequence, reads it with 11 frozen image quality models, and subtracts two readings from the same image to cancel the target term and teacher bias by construction. On CSIQ and LIVEC the student reaches PLCCs of 0.9009 and 0.9133 against human MOS, above both the best single teacher and the teacher consensus; frozen and transferred zero-shot from CSIQ to LIVEC it reaches 0.8416, 0.1947 above consensus.

Wearable activity recognition has been tied to a fixed sensor channel count, so moving to a new dataset meant redesigning the input layer. UniHAR projects heterogeneous sensor layouts into a shared feature space through channel-agnostic per-channel descriptors, then tokenizes at three temporal scales and mixes each scale with FFT-based filtering. One backbone reaches 96.35%, 94.45%, 96.44% and 99.23% accuracy on UCI-HAR, Opportunity, Daphnet and PAMAP2 with 0.7–0.9M parameters.

Wearable activity recognition has been tied to a fixed sensor channel count, so moving to a new dataset meant redesigning the input layer. UniHAR projects heterogeneous sensor layouts into a shared feature space through channel-agnostic per-channel descriptors, then tokenizes at three temporal scales and mixes each scale with FFT-based filtering. One backbone reaches 96.35%, 94.45%, 96.44% and 99.23% accuracy on UCI-HAR, Opportunity, Daphnet and PAMAP2 with 0.7–0.9M parameters.

Addressing the difficulty of characterising depression in Alzheimer's disease (AD), this study built an explainable multimodal framework integrating linguistic and acoustic features from elicited picture-description speech, used a source-informed cross-dataset transfer strategy to compensate for the absence of directly observed depression labels in the target AD cohort, identified two distinct speech sub-phenotypes via unsupervised clustering, found that one cluster showed mathematical proximity (cosine similarity in a shared latent space) to the depression centroid of a depression-labelled source corpus, then validated cluster separability with a proxy supervised classification task in which the retained multimodal artificial neural network achieved 92.

Addressing the difficulty of characterising depression in Alzheimer's disease (AD), this study built an explainable multimodal framework integrating linguistic and acoustic features from elicited picture-description speech, used a source-informed cross-dataset transfer strategy to compensate for the absence of directly observed depression labels in the target AD cohort, identified two distinct speech sub-phenotypes via unsupervised clustering, found that one cluster showed mathematical proximity (cosine similarity in a shared latent space) to the depression centroid of a depression-labelled source corpus, then validated cluster separability with a proxy supervised classification task in which the retained multimodal artificial neural network achieved 92.