BioX-Bridge: Model Bridging for Unsupervised Cross-Modal Knowledge Transfer across Biosignals
Authors: Chenqi Li
Yu Liu
Timothy Denison
Tingting Zhu
Venue: ICLR 2026 Oral (2026)
Keywords: biosignal
ai for healthcare
humans and ai
unsupervised cross-modal knowledge transfer
Primary area: applications to physical sciences (physics, chemistry, biology, etc.)

Abstract: Biosignals offer valuable insights into the physiological states of the human body. Although biosignal modalities differ in functionality, signal fidelity, sensor comfort, and cost, they are often intercorrelated, reflecting the holistic and interconnected nature of human physiology. This opens up the possibility of performing the same tasks using alternative biosignal modalities, thereby improving the accessibility, usability, and adaptability of health monitoring systems. However, the limited availability of large labeled datasets presents challenges for training models tailored to specific tasks and modalities of interest. Unsupervised cross-modal knowledge transfer offers a promising solution by leveraging knowledge from an existing modality to support model training for a new modality. Existing methods are typically based on knowledge distillation, which requires running a teacher model alongside student model training, resulting in high computational and memory overhead. This challenge is further exacerbated by the recent development of foundation models that demonstrate superior performance and generalization across tasks at the cost of large model sizes. To this end, we explore a new framework for unsupervised cross-modal knowledge transfer of biosignals by training a lightweight bridge network to align the intermediate representations and enable information flow between foundation models and across modalities. Specifically, we introduce an efficient strategy for selecting alignment positions where the bridge should be constructed, along with a flexible prototype network as the bridge architecture. Extensive experiments across multiple biosignal modalities, tasks, and datasets show that BioX-Bridge reduces the number of trainable parameters by 88-99\% while maintaining or even improving transfer performance compared to state-of-the-art methods.

Published as a conference paper at ICLR 2026
BIOX-BRIDGE: MODEL BRIDGING FOR UNSUPERVISED CROSS-MODAL KNOWLEDGE TRANSFER ACROSS BIOSIGNALS
Chenqi Li; Yu Liu," Timothy Denison, Tingting Zhu
Department of Engineering Science
University of Oxford
(chengi 1i, yu 1iu, timothy. denison, tattoonniiiniioo tingting zzu (eng ox ac uk
ABSTRACT
Biosignals offer valuable insights into the physiological states of the human body. Although biosignal modalities differ in functionalityy signal fidelity, sensor comfort, and cost, they are often intercorrelated, reflecting the holistic and interconnected nature of human physiology. This opens up the possibility of performing the same tasks using alternative biosignal modalities, thereby improving the accessibility, usability, and adaptability of health monitoring systems. However, the limited availability of large labeled datasets presents challenges for training models tailored specific tasks and modalities of interest. Unsupervised cross- knowledge transfer offers promising solution by leveraging knowledge from an existing modality. Existing methods
modality support model training for typically based on knowledge distillation, which requires running a teacher model alongside student model training, resulting high computational and memory over- head. This challenge is further exacerbated by the recent development of foundation models that demonstrate superior performance and generalization across tasks at the cost of large model sizes. To this end, we explore new framework for unsupervised cross-modal knowledge transfer of biosignals by training lightweight bridge net- work to align the intermediate representations and enable information flow between foundation models and across modalities. Specifically, we introduce an efficient strategy for selecting alignment positions where the bridge should be constructed, along with a flexible prototype network as the bridge architecture. Extensive exper- iments across multiple biosignal modalities, tasks, and datasets show that BioX- Bridge reduces the number of trainable parameters by 88-99% while maintaining even improving transfer performance compared state-of-the-art methods. Our code is available at: https: / com/ chenqi li/BioX --riigriee
INTRODUCTION
Biosignals, such as electrocardiogram (ECG), electroenccephalogram (EEG), and photoplethysmogra- phy (PPG), provide critical insights into the underlying physiological states of individuals. They are essential tools in modern healthcare and have often been considered the gold standard for diagnostics (Rosenberg & Van Hout 2013 (Stracina al.) (022). the past decade, the advancement of artificial intelligence (Al) has enabled remarkable capabilities in automated diagnostics and monitoring, such as stress assessment (Mentis et al., 2024), sleep stage classification (Mostafa et al.] (019), and arrhythmia detection (Parvaneh et al. 2019). However, many biosignal sensors are not suitable for use outside clinical settings due to factors such as user discomfort, high manufacturing costs, and excessive power consumption.
promising direction to harness the correlations between different biosignal modalities and perform the same tasks using alternative modalities, making health monitoring systems more accessible, practical, and flexible (Wang et al. 2023 Yang et al. 2023). For example, being able to perform the same tasks using single-lead PPG data from wearable smartwatch, instead of relying on 12-lead
"Equal contribution.

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[a] Data Translation
(b) Knowledge Distillation
(c) Brox-Bridge (Ours)
Figure 1:
Comparison of unsupervised cross-modal knowledge transfer methods for biosignals. The red arrow indicates loss computation.
ECGs, would greatly reduce hardware complexity and cost, while enabling continuous, user- sser-friendly monitoring in everyday environments. Unfortunately, training such models requires large-scale labeled datasets, which are often difficult to obtain in biosignal applications due to the high cost and domain-speciff expertise required for data collection and annotation. This highlights the need for effective knowledge transfer between biosignal modalities, leveraging models trained in old or well-established modalities to support the development of models for new underrepresented biosignal modalities.
Unsupervised cross-modal knowledge transfer stands out as practical solution to address the aforementioned needs. Existing methods can be divided into two categories: data translation and knowledge distillation. As illustrated in Figure [a), data translation directly translates data from the new modality to the old modality, enabling the direct reuse of existing models from the old modality (Sarkar & Etemad, 202T). However, the exploration of data translation has been limited a certain pair of modalities, such as PPG and ECG. Figure[[t) illustrates knowledge distillation, which seeks to train a student model for the new modality to mimic the output of a pre-trained teacher model from the old modality (Abbaspourazad et al. 2024b; Zhang et al., 2024). The distillation process is memory intensive, as it requires forward inference with both the student and teacher models, in addition to backpropagation with the student model. The computational burden is further exacerbated by the emergence of large-scale biosignal foundation models (Coppola et al., [024] Jiang et al. 2024; Pillai et al. (025), which are mostly trained on specific modalities and have demonstrated exceptional performance across a wide range of tasks. Although these models offer tremendous performance gains, their use in cross-modal knowledge transfer is hindered by their size, which makes traditional knowledge distillation solutions computationally prohibitive for users without access to high-end GPUs. For example, distilling knowledge from PaPaGei, PPG-based foundation model (Pillai et al. 2025), to the ECG-FM student model (McKeen et al. 2024) on the WESAD dataset (Schmidt et al. 2018), with a batch size of eight, requires more than 32GB of VRAM. Furthermore, because of data sharing regulations and privacy concerns, such a distillation process often needs to be performed locally where the data resides, under low-resource conditions. These constraints call for the development of effective and efficient cross cross-modal transfer framework that can fully leverage the representation capability and embedded knowledge of the foundation models.
To this end, propose BioX-Bridge new framework for unsupervised cross- cross-modal knowledge transfer via model bridging. as illustrated in Figure Tc). The core idea is to construct a bridge that projects intermediate representations from biosignal model another, leveraging the powerful representational capability and the rich embedded knowledge of foundation models] The framework comprises two key components: bridge position selection and bridge architecture design. Specifically, we introduce an efficient two-stage strategy for selecting optimal input and output positions by evaluating the quality and similarity of intermediate representations between two biosignal models. To enable effective projection between nigh-dimensional spaces, we design prototype network composed of a learnable prototype set and low-rank approximation module to compute aggregation weights. Notably, only the bridge network requires training to enable interoperability between models of different modalities. We evaluate the effectiveness of BioX- --ridgg in three biosignal datasets involving different modalities, demonstrating superior efficiency compared to existing methods. Extensive ablation studies further confirm the robustness of the proposed framework under various conditions. Our contributions can be summarized as follows:
Note that the proposed RoxiBBrige framework is compatible with any deep learning-based biosignal models. In this work, we focus primarily biosignal foundation models as a backhone to hetter support ongoing research in this area. Please refer the appendix for ablation studies using different backbones.
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We propose BioX-Bridge, novel unsupervised model bridging framework that enables cross- modal knowledge transfer through information flow between biosignal models.
We introduce key components to support the framework, including an efficient two-stage strategy for selecting bridge positions and prototype network with low-rank approximation for effective high-dimensional projection.
We demonstrate the efficiency of BioX-Bridge through experiments on three biosignal datasets, four modalities, and six transfer directions, demonstrating robustness through comprehensive ablation studies.
2 RELATED WORKS
Unsupervised Cross-modal Knowledge Transfer Existing methods can be divided into two categories: knowledge distillation and data translation.
Knowledge distillation was introduced model compression technique, where a smaller student model learns to mimic a larger and nigh-performing teacher model by matching its output distributions (Hinton et al. 2015). The concept has since been extended to cross-modal knowledge transfer. Early efforts focused on computer vision applications across variety of sensor modalities, such vision to depth images (Garcia et al. 20188) Gupta et al., 2016 Hoffman et al. 20166; Tian et al. 2020), to radio frequency heatmaps (Zhao al. 2018 8), and to sound (Aytar et al. 20 6 Xue et al. (021). The core idea is to leverage unlabeled but semantically aligned data pairs to bridge the modality gap and transfer relevant knowledge the corresponding tasks (Gou al. 2021; Moslemi et al.) 2024). Recent efforts have also investigated cross-modal knowledge distillation for biosignals. For example, Brant-X (Zhang et al. 2024) introduced unified biosignal alignment framework that transfers knowledge from EEG to other biosignal modalities through two-level semantic alignment the teacher
strategy, such that the student model can provide complementary representations model and improve downstream task performance. In another work (Abbaspourazad et al. (024b)) the distillation of knowledge from PPG to accelerometer signals was used to accurately predict physiological states such as heart rate, However, the aforementioned methods require training full-size student model from scratch, which becomes increasingly impractical as model sizes grow, especially for resource-constraine settings.
Data translation aims to achieve unsupervised cross-modal knowledge transfer by directly translating raw data from modality to another. Generative adversarial networks (GAN) (Goodfellow et al. 2020) and their variants (Mirza & Osindero 2014; Zhu et al. 2017) have been widely adopted for modality translation tasks in the visual and signal processing domains (Duan et al., 2021 Sikka et al. 2021; Yang et al. 2020). A recent work (Wang et al.) 2023) leveraged knowledge graphs to learn transformations between independently trained foundation models for proteins, drugs, and text. Nevertheless, reliance on structured knowledge graphs limits their applicability to biosignal scenarios, where such structured relationships are scarce nonexistent. In the biosignal domain, cross-modal translation efforts have been largely limited to translation from PPG to ECG (Sarkar & Etemaad 2021 Zhu et al. 202T). Extending such translation other modalities, such as EEG ECG, remains largely underexplored.
Biosignals Foundation Models Inspired by the recent success of large-scale pre-training in natural language processing (Achiam al., 2023) and computer vision (Dosovitskiy et al.) 2020). the development of foundation models for biosignals has garnered much interest (Han et al. 2024
Lai al.) 2025). Through large-scale self-supervised training on public and private biosignal datasets, several biosignal foundation models have been developed to capture rich and transferable representations, enabling more robust and efficient downstream adaptation. These models span a
variety of modalities, including EEG (Chen et al.) 2025a
2024 Cui et al.] [023)] Jiang et al. 2024
Wang et al. 2024), ECG (Coppola et al.) 2024 Li et al.
2024 McKeen et al.) 2024), PPG CCen
et al. 2025b Pillai et al. 2025 Saha et al. 2025 accelerometer (Abbaspourazad
al. 2024b),
and general- purpose biosignal models Yang et al. 2023). In addition to unimodal models, recent work has explored multimodal foundation models for various applications such health monitoring (Abbaspourazad et al.
2024a Luo et al.) 2024), sleep (Thapa et al., 2024)), and activity recognition
Narayanssamy al.
2024). Despite their strong performance data from modalities seen during
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(a) Training Stage
(b) Inference Stage
WWW
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(c) Bridge Architecture
Legend
New Bios Biosignal
DODDO
Prototype Set
KONMO
Old
i
+
A
B
Adaptation
Weights
Gradient
Backward
Multipicatioon
Bridge. (a) At the training stage, the bridge learns to project intermediate representations from the new modality to the old modality, such that it mimics the output the old modality model. (b) At the inference stage, the bridge has been constructed and enables the flow of information between the two models in order to make predictions on data from the new modality. (c) The bridge consists of low-rank approximation module and a prototype set. The low-rank approximation module generates aggregation weights for the prototype vectors.
Figure 2: Overview of BioX-Bridge.
pre-training, these models struggle with generalization to unseen modalities due to mismatches in input dimensions and data distributions (Liu al. 2024).
We provide further discussion on model stitching and domain adaptation in Appendix
3 METHODS
The core concept of our proposed BioX-Bridee framework is to build a bridging network that facilitates efficient and effective projection between intermediate representations This allows the framework to harness the strong representational power of one model while integrating the task-specific knowledge contained another. We define the problem in Section [.1 and introduce the idea of model bridging in Section 3.2 We detail the position of the bridge, its architecture and
biosignal models.
training in Sections 3.3 3.5
An overview BioX-Briide is presented Figure
3.1 PROBLEM DEFINITION
Assume that we are given an annotated dataset from an old biosignal modality, Doold) (at jold) (old)) y soseyyy
Ve Ie=1 with [D(o))) labeled samples for specific task, and corresponding model, golld) pfald)) where ff plold) is a pre-trained encoder parametrized by followed by task head goo parametrized by w. We also have an un-annotated dataset from new modality, D(new)
{a
(new))
(D ( which shares the same underlying label set with D(ok), We further have a dis- ([eww)
J==1
joint, un- un-annttteed paired dataset (pmir) {(x: (ol)) x
|
|D((m)) The unsupervised cross-modal
knowledge transfer problem aims to obtain a model f, such that f can make predictions on (now)
3.2 MODEL BRIDGING
Let ((al)) be the model for the old modality, parametrized by 0 of T. layers, and let s(new) be the model for the new modality, parametrized by & M layers. The intermediate representations from the m-th layer of the new modality model can then be extracted as:
h (new)
f (x (Bow))
(I)
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where x (new) denotes biosignal time series sample from the new modality. foom denotes the subset of the new modality model consisting of its first m layers, subject to the constraint I S M. himeww lR vm x denotes the intermediate representation from the m-thh layer of the new modality model with Ninew) number of tokens for transformer spatial dimension for CNN and dnew)) token embedding dimension for transformer or number of channels for CNN.
Next, we introduce bridge network to enable the information flow between the new and old modality models by projecting representations from the new modality into the representation space the old modality:
h bub (f
(new)
where by denotes the bridge network parametrized by sb. h ( denotes the projected representation from the new modality to the old modality. Note that the projected representation is designed to mimic the intermediate representation from the I-th layer of the old modality model, defined as hy (old) = f (z!uld), where (oli) is the paired input signal from the old modality, Thus, h ( h h ER N/ xdf are of the same dimension.
Finally, we can obtain predictions using the old modality model starting from the (1 1)-th layer:
olld)
plldd) |8y1
(n(obd)
[n
(old)
o (lold)
bup o flace (zo(now))
=
where m and I are also known as the bridge input and output positions, denotes function composition.
3.3 BRIDGE POSITION SELECTION
There are L x M possible locations where the bridge can be constructed between the layers of the two models. Although a brute-force search would yield the optimal bridge position, it is computationally expensive. In particular, the choice of the bridge position is one of the most influential factors affecting transfer performance, as we will show in ablation studies. To this end, we propose a two-stage strategy for efficient bridge position selection, as illustrated in Figure 3
Stage 1: Bridge Input Position (m) Selection The bridge serves to project new modality represen-
tations to the old modality representation space, enabling the bridged model to mimic the behavior of the old modality model. As the saying "garbage in, garbage out" suggests, it is important to select discriminative new modality representations that can effectively distinguish among the predictions produced by the old modality model, also known as pseudo labels. We propose select the input position of the bridge by linear probing, which has been widely used to evaluate the quality of intermediate representations (Alain & Bengio 2016). The bridge input position selection can be formulated as:
|
arg min me (1.....M) (Dipury)
Σ
Cpoobe
In
(12 (new)
-..).
where (new) plnew) g [x] (new) denotes the i-th sample's intermediate representation from the m-th
layer of the new modality model, and ÿs = .لاهك) ffolll) (x( denotes the pseudo label. Spotte denotes the empirical loss for the linear prober 9n. Note that the pseudo labels are simply the argmax of the teacher logits used in traditional knowledge distillation methods.
Stage 2: Bridge Output Position (1) Selection Since (u byb (neww)) is designed to mimic
h. (old) we can ease the transformation process by selecting h: to be as similar as possible to h (new) We select linear CKA (Komblith al. 2019 well-established measure to find correspondences
between the intermediate representations of neural networks. Let H T (D' Nite (ine) denote the matrix of new modality representations extracted from the m-th layer where the i-th row corresponds to the flattened h (new) Similarly, let H.(old) [x denote the matrix of old modality representations from the 1-th layer. The bridge output position selection be formulated
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arg max CKAttiner (E......L)
(5)
For detailed formulation of CK Alinar please refer the appendix.
Algorithm 1: BioX BioX-Bridge learning procedure
Input:
Old modality model fb
New modality model 1 /(aww)
Layer
Linoar Accis +
pesudo- bebelbeeb
Task head الاوو Paired dataset D(pair)
S
Output: BioX-Bridge ولااكوو flol) bup sdaem (-)
Layer
Lapar
Layer
Layer
Init: Bridge network by {A, B,
>
Bridge Position Selection
(a) Bridge Input Position Selection
Select bridge input position, m, using Eq.
Select bridge output position, L, using Eq.
POYTMOOAA
Bridge Training
Wy -tyer
for epoch <- 1 to Mepocc do
Layer tokd
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for step +- to nstep do
Sample mini-batch
Layer
CKAr
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(new)
1 Dipair)
Compute h (old) = plolt)
(old)
Layer
Layer
ar
Compute h (old)
(lold) bup
p(new)
(new)
(b) Bridge Output Position Selection
gm
a
10
Compute Latign
(old) T (old)
using Eq.
Figure 3: Bridge Position Selection Strategy. For bridge input position, we select the layer from f(new) whose in- termediate representation exhibits the strongest linear association with the pscudolabels. For bridge output position, we select the layer from p(old) whose rep- resentation is most similar to that of the bridge input layer.
11
Update &b w.r.t. gradients using V
12 b Bridge Inference
for step Nistep do Sample mini-batch (ee J/ml
D (inew)
14
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glold) plold) bys @em
plnew)
x
(new)
3.4 BRIDGE ARCHITECTURE
Models of different modalities operate in distinct representational spaces. The bridge network should be sufficiently parametrized to enable the projection and alignment of the two spaces. A naive bridge architecture is full-rank linear layer, but this is prohibitively expensive because of the high-dimensional projection from the new modality to the old modality. For example, using LaBraM
(Jiang et al. 2024) as fl ((now) and HuBERT-ECG. (Coppola et al. 2024) as f(old) the projection
would require Núnew) x (dneww x N7foll) x d(cha)) = 181 x 200 x 93 x 512 x 1.7 billion parameters. To address the challenge of high-dimensional projection, prototype network consists of two modules, prototype set, and a low-rank approximation module. The prototype set, P N,N,N x consisting of NN learnable prototype vectors with embedding dimension doold), introduces the flexibility to incorporate prior knowledge from f(old) To initialize the learnable prototype set, P df we randomly select N, token/fcature maps from h, (old)
propose prototype network. The
of the training set samples. Specifically, (o(od) the intermediate representations from the 1-th layer of the old modality model. The low-rank approximation module, consisting of A E gd xr and B x Niiill, N, reduces the number of trainable parameters through low-rank factorization, while generating aggregation weights for prototype vectors, as illustrated in Figure 2
(oool)) Reshape w/ NN, (Pool (h (new) A B P.
(6)
where Pool(.) denotes a pooling operation along the Ninew) dimension, and Reshapeyyon (+) denotes the reshape operation the specified output dimensions.

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3.5 BRIDGE TRAINING
As the difference between 1 (old) and hy approaches zero, the bridged model yields predictions identical those of the old modality model. Formally:
h (old)
h (old)
plold)
(ht (1(o)))
D y.
Naturally, the training objective for the bridge network is to align the intermediate representations in the I.-th layer
arg min Lalign (1/% (old)) (.ll)) + where Lalign denotes the loss function, such cosine loss and mean absolute error loss. The learning process of BioX-Bi boodgerigee is presented in Algorithm
arg min Lalign (1%ah) (zo s o by *
f
(ze"ee)))
4 EXPERIMENTS
4.1 EXPERIMENTAL SETUPS
Datasets & Tasks & Metrics
We consider the following datasets in the evaluation: (i) WESAD
(Schmidt et al., 2018) wearable stress and affect detection dataset consisting of synchronized data
from a wrist- and chest-worn device, collected from 15 subjects during lab study. We select the ECG and PPG modality for a three-class (baseine/amuusementttrsss classification task. (ii) FOG (Zhang iet al. 2022) is multimodal dataset for detecting freezing of gait in Parkinson's 's Disease. collected from 12 patients. We select the EMG and EEG modality for two-class (normal/FOG) classification task. (iii) ISRUC (Khalighi et al.) (016)) is aleep-staging dataset consisting of synchronized data from a polysomnography, collected from 188 subjects in a laboratory study. We select the EEG and ECG modality for two-class (sleep/wake) classification task. Dataset splits and preprocessing are detailed in the appendix. Given the unbalanced nature of the datasets, we report Balanced Accuracy, F1 F1-Weighted, and F1 F1-Macro, following (Jiang et al. 2024 Pillai et al. 2025).
Backbone Foundation Models For EEG, adopt the base version of the LaBraM architecture with 5.8M parameters (Jiang et al. 2024). For ECG, we adopt the small version of the HuBERT-ECG architecture with 30.4M parameters (Coppola al. 2024). For PPG, we adopt the small version of the PaPaGei architecture with 5.7M parameters (Pillai et al. 2025). For EMG, we adopt Norm Wear with 136. 1M..MM parameters (Luo et al., 2024). Note that LaBraM, HuBERT-ECG, and Norm adopt CNN -on-transformer architecture, while PaPaGei adopts CNN architecture. All models are initialized with the pre-trained weights provided by the original publications. Note that biosignal foundation models are still early in their development, in comparison to foundation models for language and vision. Although current models contain a relatively small number of parameters, our method for efficient cross-modal knowledge transfer would be even more valuable as they scale up. As foundation models scale up, BioX- DooddBridge is efficient in terms of the bridge architecture to help reduce the number of trainable parameters. Furthermore, the bridge position selection strategy is lightweight in comparison to model training and scales linearly with the number of layers of the foundation models.
Baselines We compare our method with the following baselines evaluated on Dinew); (i) Random denotes a model that produces predictions at random. (ii) CardioGAN uses GAN to synthesize ECG from PPG (Sarkar& & Etemaa] 202T), and we translate the new modality data (PPG) to the old modality (ECG) for evaluation. (iii) KD (Hinton et al.) 2015) is the baseline of knowledge distillation. (iv) KD>contrast (Abbaspourazad et al.] (024b) a variant of knowledge distillation with contrast loss (Zhang et al., 2024). (v) Oracle denotes the absolute best performance that can be achieved, which is simply the performance of the old modality model using old modality data. Please refer to the appendix for implementation details and further discussions.
NNote: While alignment can be performed at any layer between the /-th and L-th layers, we empirically find that performing alignment at the L-th (i.c.. final) layer yields better transfer performance. We believe this a result of error propagation. For aligument at the (-th layer, small alignment error would grow as it propagates to the final layer. For alignment at the I-th layer, the error growth will be reflected in the alignment loss, enabling the bridge to take that into account and yield hetter downstream performance.
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Table Unsupervised cross-modal knowledge transfer performance on ISRUC, FOG, and WESAD. Oracle reflects supervised performance using the old modality only, also known as the teacher in knowledge distillation, whereas baselines and BioX-Bridde report performance on the new modality. Results are reported as mean across five seeds; standard deviations can be found in the appendix due to space constraints. The metrics are: Balanced Accuracy (BAcc), FI-M (F1-Macro), F1-W (F1 (F1-Weighted), Trainable Parameters (Params). Input indicates the data modality serving as the model's 's input during evaluation on the test set. The best unsupervised result is indicated in bold.
ISRUC
EEG (Old) + ECG (New)
ECG (Old) + EEG (New)
Methods
Input
BAcc 1
FI-M
FI-W
Params
Input
BAce
FI-M
F1-W
Params
Random
50.00
46.48
53.52
50.00
46.48
53.52
KD
ECG
60.24
61.01
72.96
30.4M
EEG
62.24
63.69
75.27
5.8M
KD-Contrast
ECG
60.66
56,56
63.57
30.4M
EEG
65,92
62.91
70.27
5.8M
BioX-Bridge
ECG
60.11
61.20
74.02
1.8M
EEG
62.55
64.37
76.42
0.2M
Oracle (Sagervied)
EEG
80.13
82.06
87.19
ECG
63.54
65.54
76.86
FOG
EEG (Old)
EMG (New)
EMG (Old)
EEG (New)
Methods
Input
BAce
F1-M
FL-W
Params
Input
BAce
F1-M
F1-W
Params
Random
50.00
49.99
50.01
50.00
49.99
50.01
KD
EMG
68.64
67.62
67.78
136.1M
EEG
68.03
67.73
67.75
5.8M
KD-Contrast
EMG
72.21
71.95
71.95
136.1M
EEG
68.51
67.95
67.90
5.8M
-Bridge
EMG
72.24
72.12
72.16
1.2M
EEG
68.04
68.22
68.24
0.7M
Oracle (Supervised)
EEG
72.15
72.14
72.20
EMG
87.55
87,58
87.60
WESAD
ECG (Old) + PPG (New)
PPG (Old) - ECG (New)
Methods
Input
BAce
F1-M
F1-W1
Params
Input
BA
F1-M
F1-W 1
Params
Rundom
33.33
31.29
35.38
33.33
31.29
35,38
CardioGAN
PPG
39.32
19.63
20.33
28.2M
KD
PPG
47.86
43.08
45.75
5.7M
ECG
47.03
46.36
60.29
30.4M
KDDContrast
PPG
45.31
42.75
47.20
5.7M
FCG
50.85
49.31
63.72
30.4M
BioX- X-Briidgee
PPG
49.57
42.28
47.44
0.2M
ECG
52.02
52.62
65.12
0.4M
Oracle (Supervisad)
ECG
49.47
51.05
62.48
PPG
62.96
60.97
74.52
4.2 UNSUPERVISED CROSS-MODAL KNOWLEDGE TRANSFER PERFORMANCE
Experiment results on the ISRUC, FOG, and WESAD dataset are presented in Table We observe that BioX-Bridge significantly reduces the number of trainable parameters by 87.9-99. 1% and continues to achieve performance comparable to or better than that of the baseline methods. For example, for WESAD (PPG - FCG), BioX-Bridge requires merely 1.3% of trainable parameters while outperforming the baseline methods by around 1 1-2% across all metrics.
We also observe that the knowledge transfer performance gap compared to the oracles varies between datasets and knowledge transfer directions. For example, on the ISRUC dataset, we observe approxi- mately 20% balanced accuracy gap between Biox-Bridge (60. 11%) and the supervised EEG oracle (8%) for BEG > ECG, but only 1% gap between -Biddee (62.55%) and the supervised ECG oracle (6%)54%) for ECG EEG. From this, we can draw a few observations: (1) Based on oracle results, the ISRUC task is more difficult for ECG (63.54%) than for EEG (80.13%). which is reasonable, as EEG has been considered the gold standard for sleep. (2) Our unsupervised training with BioX-Bridge can effectively produce an ECG model (60. (60.11%) that performs similarly to the supervised ECG oracle (63.54%) despite the absence of labeled data. (3) There a 20% gap between BioXeeridge (60. 1 1 () and the supervised EEG Oracle (80.13%) because the baselines and Dorider use the ECG as input, which is less physiologically relevant modality for sleep than the EEG used by the supervised EEG oracle. (4) Unsupervised modal knowledge transfer per- formance using EEG (62.55%) is also constrained by the performance of the ECG teacher (63.54%), consequence inherent to knowledge transfer itself. Therefore, our unsupervised training produces
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relatively weaker EEG model (62.55%) compared to supervised EEG training (80.13%) since transferring from a weaker ECG model (63.54%).
On another note, BioX- Bridge and KD-Contrast achieved higher balanced accuracy than Oracle on several occasions, respectively. This is possible because balanced accuracy simply an average of recall across classes. Higher balanced aceuracy scores and lower F1 scores reflect that knowledge transfer methods achieved better recall but worse precision than Oracle.
4.3 ABLATION STUDIES
We conduct ablation studies on the WESAD dataset and the direction of knowledge transfer (PPG ECG). Additional results are presented in Appendix D.6
Bridge Rank and Prototype Set We study the impact different hyperparameters for the prototype network in Figures 4a and 4b A performance drop is observed when the approximation rank and prototype set size are too small or too large, likely due to under-/over-paramtteiiic network. In particular, the performance peaks at around 0.75M parameters in both cases.
the bridge
We reduce the size of the paired dataset for bridge training. We observe in Figure 4c that the transfer performance slowly decays by around 2% at 20% dataset size, showcasing the robustness of the bridge under the low-data regime.
Dataset Size
64
0.5M .OM 1.5M Number Parameters
24
0.5M COM 1.5M Number of Parameters
54
Galanced Accuracy
52
Balanced Accuracy
522
Balanced Accuracy
52
50
50
50
48
48
48
46
46
46
44
44
40
120
40
60
100
(a)
(b) Np
(c) (D(patr)
%
Figure 4: Bridge Training Ablation. Blue: Balanced Accuracy. Red: Number of Parameters. We vary (a) bridge rank, (b) number of prototypes, and (c) pair dataset size to understand the robustness of BioX- and its performance under low-data regime.
Bridge Position Selection To show that the bridge position selection strategy proposed in Section 3.3 is effective, we compare the unsupervised performance of cross-modal knowledge transfer at various positions Table For "Fixed", combination of the first, middle, and last layers of the old and new modality models. The results are the average over all nine positions in five seeds. breakdown of the result each of the nine predefined positions is available in Appendix D.5
train nine bridges in predefined positions, which is
Foundation Model We further analyze the impact of using different foundation models for cross- modal knowledge transfer. In Table 3] we replace the HuBERT-ECG foundation model (Coppola et al. 2024) with ECG-FM (McKeen et al., 2024). Notably, due to the large number of trainable parameters (90M), the knowledge distillation methods with ECG-FM could only be performed with batch size of 4 on V100 GPU. As result, training for more than 50 epochs requires 6.5 hours for knowledge distillation methods and 1.9 hours for BioX-Bridge. Moreover, the performance gap between knowledge distillation methods and BioX-Bridge is much pronounced 10-17%.
Table 2: Bridge Position Ablation. Com- parison of different bridge position lection strategies with respect to BioX- Bridge. "Fixed" represents the average of 9 predefined positions, combining the first, middle, and last layers for both the input and output positions.
Table 3: Foundation Model Ablation. We compare the transfer performance by replacing the ECG foundation model HuBERT-ECG with ECG-FM.
Methods
Input
BAce
FI-M
F1-W + Params |
Random
33.33
31.29
35.38
KD
ECG ECG
48.44
45,84
54.18
90.8M 90.8M
KD-Contrast
43.06
42.94
54.21
Methods
BAcc
F1-M f
F1-W
BioX-Bridge
ECG
58.80
57.11
72.12
0.11M
Fixed
48.34
46.83
58.37
BioX-Bridge
52.02
52.62
65.12
Oracle
PPG
62.96
60.97
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5 CONCLUSION
We present HioX-Bridge as efficient framework for unsupervised cross-modal knowledge transfer across biosignals. To address the challenges of oigh-dimensional projection between biosignal foundation models, we design prototype- transformations between representation spaces. Our proposed two-stage bridge position selection strategy further identifies connection points that enable more effective alignment of intermediate rep- resentations. Through extensive experiments diverse biosignal datasets and tasks, demonstrated that BioX- -Bodgee achieves performance comparable to or superior to that of state-of-the-rr methods while drastically reducing the number of trainable parameters. This work highlights the potential of model bridging as a powerful alternative to conventional cross-modal knowledge transfer techniques, offering pathway more accessible, adaptable, modality-annostic, agnosticc and resource-efficiens biosignal applications in real-world settings, where computing resources and labelled data are often limited.
architecture for parameter-effiieen learning of
ETHICAL AND REPRODUCIBILITY STATEMENT
This study makes use of datasets involving human subjects (ISRUC, WESAD, and FOG). All datasets employed are publicly available, and we follow the usage terms and ethical guidelines specified by the original data providers. No new data were collected for this work, and all analyses were conducted on de- ie-identified, previously published datasets.
To ensure reproducibility, we provide detailed descriptions of our experimental setups, including data preprocessing steps, model architectures, hyperparameters, and training procedures, in Section and Appendix D Our code available at: https:: thubb cwt. -iiiiiiiiiiiiii-e
ACKNOWLEDGEMENTS
The work of Chenqi Li supported by the Cyril and Phillis Long Scholarship The Queen's 's College in partnership with the Clarendon Fund. The work of Tim Denison was funded by the Royal Academy of Engineering and supported by the NIHR Oxford Health Biomedical Research Centre (N1HR203316). The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care. The Royal Academy of Engincering supported the work of Tingting Zhu under the Research Fellowship scheme.
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APPENDIX
TABLE OF CONTENTS
Method Details: Linear CKA
14
Limitations and Future Work
15
Additional Discussion on Related Works and Baselines
15
C.1 Model Stitching
15
C.2 Domain Adaptation and Generalization
15
3 CardioGAN for additional modalities
16
D
Experiment Details
16
D.I Sctup and Implementation Details
16
D.I.I Dataset Preprocessing
16
D.1.2 Dataset Split
16
D.1.3 BioX BioX-Bridge and Bascline Implementation Details
16
D.2
Main Results with Standard Deviations
17
D.3
Additional Ablation Studies
17
D.3.1
BioX-Bridge Framework with Traditional Biosignal Models
17
D.3.2
Impact of Foundation Model Size
19
D.4
Bridge Position Selection Results
20
D.5
Bridge Position Ablation Results
23
D.6
Brigge Rank, Prototype Set, Dataset Size Ablation Results
23
D.7 Bridge Architecture Ablation
25
D.8 Bridge with Reshuifled Subjects
25
A METHOD DETAILS: LINEAR CKA
mn Let (llow) E RIDiiin X. Nihew ((ew) denote the matrix of new modality representations extracted from
the m-th layer, where the i-th row corresponds to the flattened (nnew), Similarly, let H(
[Dirii x الاi denote the matrix of old modality representations from the (-th layer. The CKAAtincar
operator introduced in Eq. iis formulated as follows (Korbblihh et al. 2019
HSIC
(nnw)) (new) H (old)
CKA A linear H (new) ((ld))
HSIC (Bow)
(new)
HSIC( (ll) H (old)
where
(r (new)
H (old)
Dipair) 2 trace (K,HK,H)
and K, Hinnww
H (neww)
K. ((old)
(H(ola)
H
Dipair) 11
HSIC is the Hilbert-Schmidt Independence Criterion. H is the centering matrix. Note that while this formulation the entire H(new) and H( to compute similarity between representations of the old and new modalities, it is also possible to improve efficiency by computing similarity using only a subset of their rows.
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B LIMITATIONS AND FUTURE WORK
Limitations: Although BioX-Bridgee greatly reduces training computational requirements and im- proves the efficiency of cross-modal knowledge transfer, it depends on the availability of pre-trained models for each biosignal modality, an assumption that may not hold for emerging or underexplored biosignals. Furthermore, depending on the position of the bridge, the inference time of the bridged model could be longer.
Whether the unsupervised cross-modal knowledge transfer method should be task- agnostic is interesting next step to explore. A task-agnostic method would offer better generality, making it suitable for multi-task scenarios. However, such approaches can potentially limit task- specific performance compared task-specific methods. Investigating other date-free and task- agnostic bridge position selection strategies would further improve the flexibility of BioX-Bridge for different application scenarios. In addition, existing unsupervised cross-modal knowledge transfer frameworks rely on the availability of paired data, which is more difficult to collect than unimodal data. Future work focusing on transfer using unpaired data would extend applications to any combination of modalities. Future works can also explore BioX- Bridge for datasets with more than two modalities and investigate how BioX-Bridge generalizes across datasets that share the same modalities, allowing a deeper understanding of modality interactions and dataset-specife adaptations (Zhang et al. 2022b
Future Work:
Liu et al. 2023 Fang et al. 2024
C ADDITIONAL DISCUSSION ON RELATED WORKS AND BASELINES
C.I MODEL STITCHING
Model stitching combines subsets of layers from two or more models to create hybrid one, which was initially introduced as a metric to compare intermediate representations across neural networks by examining their compatibility through network recombination (Bansal et al. 2021 Csiszárik et al. 2021; Lene& & Vedaldi 2015; Moschella et al. 2023). Most of the work focused mainly on stitching adjacent layers from models of varying sizes within the same architecture family. More recently, it has been revisited
general strategy for leveraging families of pretrained models to construct scalable neural networks that can accommodate diverse deployment constraints (He et al.) 2024 Pan al. 2023 Yang et al., 2022). For the stitching layer, simple 1x1 convolution layer is often sufficient for stitching, under the assumption that the intermediate representations are similar and that the token counts (in transformers) or spatial dimensions (in convolutional neural networks, CNNs) are aligned. However, this assumption breaks down when models attempt to stitch across different modalities, where intermediate representations differ both semantics and dimensionality.
C.2 DOMAIN ADAPTATION AND GENERALIZATION
Domain adaptation methods are typically homogeneous, where they focus on adapting a single model from a source domain to a target domain (Wilson & Cook 2020). Existing foundation models are unimodal and expect fixed input shape, but biosignals are heterogeneous, whereby they contain different numbers of channels, are sampled at different frequencies, and are segmented with different window sizes. For example, ECG-FM only accepts 12-lead, 5-second ECG segments at a sampling frequency of 250Hz, and cannot work on 6-channel, 30-second EEG segments at sampling frequency of 200Hz. This means that homogeneous unsupervised domain adaptation methods will fail to work in our setting.
Heterogeneous domain adaptation methods are more suited for our setup, where they acknowledge the difference in modalities between the source and target data. Heterogeneous unsupervised domain adaptation assumes access to a labelled source domain and an unlabelled target domain for knowledge transfer (Yang et al.) (2025] Liu et al. 2020), but setting assumes access data from unlabelled source and target domains. Therefore, heterogeneous domain adaptation methods are not suitable for the unsupervised+paired scenario in this work.
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C.3 CARDIOGAN FOR ADDITIONAL MODALITIES
In T we present CardioGAN as a baseline for WESAD ECG - PPG. We omit CardioGAN as baseline for the remaining knowledge transfer directions because the extension of data translation to other modalities is non-trivial. CardioGAN was originally designed for synthesizing ECG from PPG, both of which are single leads. However, biosignals such as EEG, EMG, and ECG have multiple channels, and extending the generation to multiple channels using multi-channel input is non-trivial. The authors CardioGAN mention that generating multi-lead ECGs is one of the future works. Additionally, to the best of our knowledge, prior study has shown any benchmarks for translating between EEG and ECG and vice versa.
D EXPERIMENT DETAILS
D.1 SETUP AND IMPLEMENTATION DETAILS
D.1.1 DATASET PREPROCESSING
Each foundation model specifies its preprocessing pipeline, and we follow these procedures accord- ingly. If notch or bandpass filter has already been applied to the dataset, we skip that step during preprocessing.
WESAD this dataset, ECG signals are sampled at 700Hz and PPG signals at 64Hz. For ECG, which uses HuBERT-ECG its foundation model, we first downsample to 500Hz, apply a finite impulse response (FIR) bandpass filter between 0.05-47Hz, resample to 100Hz, and then perform channel-wise Z-score normalization. For PPG, which uses PaPaGei its foundation model, we upsample the signals to 125Hz, apply a 4th-order Chebyshev bandpass filter between 0.5-12Hz, and normalize using z-score normalization. Finally, all recordings segmented into 60-second windows with a 5-second step size.
FOG In this dataset, both EEG and EMG signals were collected 1000Hz, downsampled to 500Hz with a notch and bandpass filter already applied. For EEG, which uses LaBraM as its foundation model, we downsample 200Hz and convert the unit 0. For EMG, which uses NormWear as its foundation model, we downsample to 130Hz and normalize using z-score normalization. All recordings are segmented into 3-second windows with a sliding step size of 0.3 seconds.
ISRUC In this dataset, EEG signals are sampled at 200Hz with notch and bandpass filter already applied, while PPG signals are also sampled 200Hz with notch filter applied. For EEG, which uses LaBraM as its foundation model, no resampling is required; we only convert the unit to O. 0.ImV. V. For ECG, which uses HuBERT-ECG as its foundation model, we upsample to 500Hz, apply a bandpass filter between 0.05-47Hz, resample to 100Hz, and normalize using z-score normalization.
D.1.2 DATASET SPLIT
Dataset split is summarized in Figure Al The datasets contain synchronized data from the old and new modalities. We perform a subject-wise split for WESAD and ISRUC and sample-wise split for FOG (Zhang al. 2022) to obtain four subsets Diold) Dinew) D(val) and D(pair), at ratio of 33%, 22%, 11%, and 33%, respectively. We use old modality data from Diold) to train the linear prober glo (ool) New modality data from Dinew) is used evaluate bridge performance in an unseen set. All data from Dipuir) and D(val) are used to train and help select hyperparameters.
1.1.3 BIOX-BRIDGE AND BASELINE IMPLEMENTATION DETAILS
BioX-Bridge Implementation Details To prepare pf and (.old) for evaluation, we adapt the pre-trained foundation models for the classification tasks using Diold), we apply mean pooling to the last-layer representations and add linear layer for classification. The weights of the foundation model are frozen, and the linear layer is trained for more than 50 epochs. We select learning rate of le-4 for LaBraM, le-4 for PaPaGei, le-4 for NormWeat and le-5 for LUBERT-ECG, as suggested in the original publications. To select the input position of the bridge, we logistic regression with L2 regularization as the linear prober. We select the layer with the best Fl macro score over five-fold cross-validation. We train the bridge over 50 epochs with cosine embedding loss on Dipair)
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(
D(pair)
D
D (new)
1888
88888
8
888
SAAA&
&
8888888
88888
8 8
88888
8888888
88888
8 8888888807
=33%
=33%
=11%
=22%
Train (aka)
Unused
Unsupervised Training
Hyperparameter Selection
Evaluate
Unused
New modality data
Old modality data
Figure Al: Illustration Dataset Split. The dataset is divided into four subject-independent subsets. We first the old modality data from D(old) to train the linear prober gلو²d) for experiment setup, followed by unsupervised training on D(pai), The subsets D(val) and D(new) are used for hyperparameter selection and testing, respectively.
All experiments are conducted on V100 GPUs with 32GB VRAM. The training time for single run ranges from 10 minutes to 4 hours, depending on the dataset and the backbone foundation model.
To select bridge rank and number prototypes, we perform a grid search over r = {4, 8, 16, 32) and N = {50, 100, 150, 200, 250, 300 For compatibility across various network architectures without modifying the forward functions, we retrieve and replace intermediate representations using forward hooks and forward pre-hooks Please see the source code for detailed implementation.
Baseline Implementation Details For the Random baseline, we simulate a model that randomly assigns labels to samples uniformly across all classes. For CardioGAN, we adopt the pre-trained weights provided by the original publication. We do not perform any fine-tuning as CardioGAN has been trained with WESAD as one of its datasets. We adopt the preprocessing code provided by CardioGAN to prepare the PPG. For each recording, we generate the corresponding ECG using sliding window of 4 seconds and step size of 4 seconds. The generated ECG recording is then preprocessed following the HuBERT-ECG pipeline described Section D.1.I
For knowledge distillation (KD), append linear layer to map prean-pooled representations from the last layer the foundation model to classwise probabilities. We then fine-tune the entire foundation model with learning rate of le-4 for LaBraM, 1e-4 for PaPaGei. le-4 for Norm Wear, and le-5 for HuBERT-ECG, as suggested the original publications. For contrastive knowledge distillation (KD-Contrast), layer of the new modality foundation model to those of the old modality foundation model. During inference, we adopt the linear prober from the old modality foundation model to produce classwise probabilities. The temperature for the InfoNCE loss is selected as 0.04 following (Abbaspourazad al. 2024b).
append linear layer map mean-pooled representations from the last
D.2 MAIN RESULTS WITH STANDARD DEVIATIONS
Tables
only report the average performance for five seeds due to space constraints. In Table we present the full results with standard deviation.
3
A5
D.3 ADDITIONAL ABLATION STUDIES
D.3.1 BIOX-BRIDGE FRAMEWORK WITH TRADITIONAL BIOSIGNAL MODELS
All results presented main body focused on building a bridge between biosignal foundation models to better support ongoing research in this area. To show that BioX-Bridge is also compatible
Py Torch documentation:
forward hook
forward pre-hook
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Table Al: Unsupervised cross-modal knowledge transfer performance ISRUC. Results are reported as meantistd (%) across five seeds. Knowledge transfer direction is indicated as (old modality > new modality).
(a) ISRUC (EEG - ECG)
Input Modality
Balanced
F1
Trainable Parameters
Methods
Accuracy t
Macro
Weighted
Random
50.00
46.48
53.52
KD
ECG
60.24
11.01
72.96-10.68
30.4M
KD-Contrast
ECG
60.66 1.48
56.56#1.98
63.57=22.41
30.4M
BioX-Bridde
ECG
1..11=1.11
61.20++1.39
74.02:10.95
1.8M
Oracle
EEG
80.13
82.06
87.19
(b) ISRUC (ECG - EEG)
Input Modality
Balanced Accuracy
F1 Macro
F1 Weighted 1
Trainable
Methods
Parameters
Random
50.00
46.48
53.52
KD
EEG
62.24
63.69-4-.00
75.27 1.27-11.22
5.8M
KD-Contrast
EEG
65.92
62.91
1..2
5.8M
BioX-Bridge
EEG
60.55:+2.10 64.37-12.65
76.42:1
0.2M
Oracle
ECG
63.54
65.54
76.86
Table A2: Unsupervised cross- modal knowledge transfer performance on FOG. Results are reported meantstt (%) across five seeds. Knowledge transfer direction is indicated as (old modality new modality).
(a) FOG (EEG EMG)
Input Modality
Balanced Accuracy t
F1
F1 Weighted
Trainable Parameters +
Methods
Macro f
Random
50.00
49.99
50.01
KD
EMG
68.6443.79
67.62+5.77
67.78+5.61
136. IM
KD-Contrast
EMG
72.71
71.9542.53
71.95:2.57
136. IM
BioX-Bridge
EMG
72.24
72.12:1.600
72.16:10.60
1.2M
Oracle
EEG
72.15
72.14
72.20
(b) FOG (EMG > EEG)
Input Modality
Balanced Accuracy
F1 Weighted 1
Trainable
Methods
Macro
Parameters
Random
50.00
49.99
50.01
KD
EEG
68.03-12.22
67.7342.63
67.75+22.71
5.8M
KD-Contrast
EEG
68.51+12.08
67.95-1..98
67.90
5.8M
BioX-Bridge
EEG
68.04 14.22--1..00
68.24 1.81
0.7M
Oracle
EMG
87.55
87.58
87.60
with traditional biosignal models, replace HuBERT-ECG with pre-trained ECG model, ECG- DualNet (Rohr et al. 2022). in Table/6 We notice that BioX- BioX-Bridge continues to outperform the baseline methods while significantly reducing the number of trainable parameters
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Table A3: Unsupervised cross-modal knowledge transfer performance on WESAD. Results are reported as std (%) across five seeds. Knowledge transfer direction is indicated as (old modality > new modality).
(a) WESAD (ECG + PPG)
Input Modality
Balanced Accuracy
F1 Macro
Trainable Parameters
Methods
Weighted
Random
33.33
31.29
35.38
CardioGAN
PPG
39.32
19.63
20.33
KD
PPG
2..66=2.36
43.08+3.005
45.75=+3.90
5.7M
KD-Contrast
PPG
45.31=5.00
42.75+3.68
47.20+22.67
5.7M
BioX-Bor -roddBe
PPG
49.57=5.51
42.28+3.22
47.4449.18
0.2M
Oracle
ECG
49.47
51.05
62.48
(b) WESAD (PPG - ECG)
Input Modality
Balanced Accuracy
F1 Macro f
F1 Weighted
Trainable
Methods
Parameters
Random
33.33
31.29
35.38
KD
ECG
1.603=1.60
46.36:11.98
60.29+2.51
30.4M
KD-Contrast
ECG
50.85+3.61
49.3113.13
63.72
30.4M
BioX-Bridge
ECG
52.02:10.80
52.62.1..36
65.12:1..11
0.4M
Oracle
PPG
62.96
60.97
74.52
Table A4: Bridge Position Ablation. WESAD (PPG - ECG). We study the effectiveness of the
bridge selection strategy.
Input Modality
Balanced Accuracy 1
F1 Macro
F1 Weighted
Methods
t
BioX-Bridge @ Fixed BioX-Bridge @ Selected
ECG ECG
48.34433.28 52.02-1..80
46.83++6.16
58.37=7.36 65.12-0.91
52.62:10.36
Table A5: Foundation Model Ablation. WESAD (PPG ECG). We replace HuBERT-ECG with
another ECG foundation model, ECG-FM.
Input Modality
Balanced Accuracy t
F1 Macro
F1 Weighted
Trainable Parameters
Methods
Random
33.33
31.29
35.38
KD
ECG
48 48.4448807
45.84+8.80
18.
90.8M 90.8M
KD-Contrast
BCG
43.06
42.9443.88
54.21+8.81
BioX- Boodeerdgge
ECG
58.80+1.000
07.11
72.12:1..2
0.11M
Oracle
PPG
62.96
60.97
74.52
D.3.2 IMPACT OF FOUNDATION MODEL SIZE
The HubERT-ECG family of models is available in three sizes, with varying numbers of parameters. We replace the small version of HuBERT-ECC (30M) with base (93M) and large (183M) versions, and report both baseline and our results in Table We observe that the best transfer performance for BioX-Bridge is achieved using the large version of HuBERT-ECG, thanks the more powerful
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Table A6; Traditional Model Ablation. WESAD (PPG ECG). We replace HuBERT-ECG with
traditional pre-trained model ECG-DualNC
Input Modality
Balanced Accuracy
F1
F1 Weighted
Trainable Parameters
Methods
f
Maero f
Random
33.33
31.29
35.38
KD
ECG
22.78=2.15
51.8613.00
64.81
8. 8.2M
KD-Contrast
ECG
49.7943.31
50.40++3.78
59.84+3.558
8.2M
BioX-Bridge
ECG
56.10
53.25:+1.54
67.60-11.92
0.4M
Oracle
PPG
62.96
60.97
74.52
representational capabilities. Overall, using base and large model yields better transfer performance than small model for KD and the best transfer performance using the small model, potentially as a result of overparametrization and limited training dataset for fine-tuning the larger models.
The same is not true for KD-Contrast, which observes
Table A7: Foundation Model Size Ablation. WESAD (PPG ECG). We replace the small version of HuBERT-ECG (30M) with base (93M) and large (183M).
Input Modality
Model Size
Balanced Accuracy
F1
F1 Weighted
Trainable Parameters
Methods
Macro t
Random
33.33
31.29
35.38
KD
ECG
Small
1..0
46.36
60.29:2.51
30.4M
KD
ECG
Base
48. 48.99+1.23
50.38
61.52+2.33
93.1M
KD
ECG
Large
5..06
49.11
61.33
188.6M
KD-Contrast
ECG
Small
50.85++3.61
49.31++.113
63.72+3322
30.4M
KD-Contrast
ECG
Base
48.24442.73
4..92+4.36
57.79+6.229
93.1M
KD-Contrast
ECG
Large
49.81++3.88
48.55
62.18+5.80
188.6M
BioX-Bridge
ECG
Small
52.02-10.80
52.62
65.12
0.4M
BioX-Bridge
ECG
Base
52.46:10.97
50,4442.29
64.69
0.5M
BioX-Bridge
ECG
Large
54.9411.777
53.233.0.3
67.90
0.8M
Oracle
PPG
62.96
60.97
74.52
D.4 BRIDGE POSITION SELECTION RESULTS
In Figure A2 A4 we provide the layer-wise results for the bridge position selection strategy proposed
in Section 3.3 including the pseudo label linear probing for bridge input position selection, and CKA similarity for bridge output position selection.
is observed that the linear probing performance of pseudo labels using representations from different layer m of f flnew) varies quite significantll. The range for the linear probing performance across different layer is as low 2% for FOG EEG + EMG and high as 14% for WESAD ECG + PPG. Therefore, the difference in input intermediate representations across different layers varies between datasets and transfer directions. However, this difference is not solely result of dataset characteristics, but also likely result of the model architecture. In general, linear probing performance tends to increase as we retrieve representations from deeper layers transformers (LaBraM, NormWear HuBERT-ECG), and the same does not apply CNN (PaPaGei), where we observed better linear probing performance in the shallower layers. A possible explanation for this difference lies in how the two architectures organize information across different layers. For CNN-based models, earlier layers capture fine- -eee-grinnd and low-level features, while the later layers capture coarse and hegh-level features with larger receptive fields. The lower-level features might
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2.75
F1-Macro (%)
72
2.50
2.25
71
2.00
CKA
70
1.75
1.50
69
1.25
2
4
6
8
2
4
6
8
10
12
HuBERT- EECGR Layer
LaBraM Layer
(a) EEG [LaBraM] + ECG [HuBERT-ECG]
68
2.60
F1-Macro (%)
66
2.55
64
22.50
2.45
62
2.40
0 2.35
58
2.30
2.25
2
4
6
10
12
2
4
5
6 7 8
LaBraM Layer
HuBERT-ECG Layer
(b) ECG [HuBERT-ECG]] - EEG [LaBraM]
Figure A2: ISRUC bridge position selection. Knowledge transfer direction is indicated (old modality [f new modality [)) The layer selected highlighted using red dashed lines. (left) Bridge input position selection, where select the layer from f(mew) whose intermediate representation exhibits the strongest linear association with the pseudolabels. (right) Bridge output position selection, where select the layer from ffald) whose representation most similar that of the selected bridge input layer.
be more informative for the task. Unlike CNN, transformer-bast models have access to the same receptive field across all layers. As the model continues to refine and reorganize the representations across layers, latter representation yields better linear probing performance.
For given layer m from ffrew) as we vary layer I from f(ald) we do not observe clear trend for the similarity of the representations between the two layers the datasets and transfer directions studied. Note that the absolute CKA similarity, on the scale of 1e-2, is low across all datasets, where CKA ranges between and 1. This expected as the two models are trained on data from different modalities, and it is the relative difference in similarity between layer / and given layer m that is of interest for bridge output position selection. Define the coefficient of variation (CV) for each transfer direction
V EL (a 12)
CV
100 P
1 100.
where e is the CKA similarity between the representations of the layer m of f<new) and the layer of (old) and μ and denote the mean and standard deviation of [ respectively. The CV is as low as 2.05 for WESAD ECG - PPG and as high as 30.72 for FOG EEG EMG.
We further analyze the impact of the reduced dataset size on the bridge position selection results. Figure A5 shows the results of the bridge position selection WESAD when using 50% of the data compared to Figure A4 Comparing these two figures, we observe very similar trends for the linear probing and CKA similarity plots. For PPG - ECG, the same bridge input and output layers are selected. For ECG - PPG, the bridge input layer differs by one layer while the bridge output layer is the same. This suggests that the bridge position selection strategy is relatively stable with respect to the dataset used.
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3.00
F1-Macro (%)
2.50
2.00
CKA
1.50
60
1.00
0.50
4
10
12
2
10
12
NormWear Layer
LaBraM Layer
(a) EEG [LaBraM]
EMG [NormWen)
2.00
F1-Macro (%)
67
CKA CKA (1e-2)
66
1.50
64
1.00
63
0.50
62
61
2
4
6
8
10
12
2
4
6
8
10 12
LaBraM Layer
NormWear Layer
(b) EMG (NorWWear) -> EEG [LaBraM]
Figure A3: FOG bridge position selection. Knowledge transfer direction is indicated (old modality [foodd]] + new modality [)( The layer selected is highlighted using red dashed lines. (left) Bridge input position selection, where we select the layer from f(uew) whose intermediate representation exhibits the strongest linear association with the pscudolabels. (right) Bridge output position selection, where we select the layer from f(ll)) whose representation is most similar to that of the selected bridge input layer.
9.00
73
MW
F1-Macro (%)
8.00
72
7.00
6.00
69
5.00
4
6
2 6 1012141618
HuBERT-ECG Layer
PaPaGel Layer
(a) PPG [PaPaGei]
ECG [HuBERT-ECG]
42
3.85
40
11-Maco (%)
3.880
34
3.75
32
3.70
28
3.65
6
10 14 16 18
2
4
5
6 8
PaPaGel Layer
HuBERT-ECG Layer
(b) ECG [HuBERT-ECG] >> PPG [PaPaGei]
Figure A4: WESAD bridge position selection. Knowledge transfer direction is indicated as (old modality [s(old] new modality ( The layer selected is highlighted using red dashed lines. (left) Bridge input position selection, where we select the layer from flnew) whose intermediate representation exhibits the strongest linear association with the pseudolabels. (right) Bridge output position selection, where we select the layer from ff of the selected bridge input layer.
glold)) whose representation is most similar to that
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9.00
MN
F1-Macro (%)
CKA (1e-2)
8.00
7.00
6.00
5.00
2
4
5
6 8
2
6 8 1012141618
HuBERT-ECG Layer
PaPaGei Layer
(a) PPG [PaPaGei] + ECG [HuBERT-ECG]
4.60
40
4.55
38
4.50
Macro
34
4.45
32
δ 4.40
L. 30
4.35
28
2
4 6
10
12 14 16 18
2
4
5
6
8
PaPaGei Layer
HuBERT-ECG Layer
(b) ECG [HUBERT-ECG] PPG [PaPaGei]
Figure A5: WESAD bridge position selection with 50% data. Knowledge transfer direction is indicated as (old modality [ + new modality ()) The layer selected is highlighted using red dashed lines. (left) Bridge input position selection, where we select the layer from S ((new) whose intermediate representation exhibits the strongest linear association with the pseudolabels, (right) Bridge output position selection, where we select the layer from ((old) whose representation is most similar to that of the selected bridge input layer.
D.5 BRIDGE POSITION ABLATION RESULTS
To better understand the impact of bridge position selection, provide breakdown of the per- formance at each of the nine predefined positions from Table in the form of heatmaps in Figure
A6
In terms of the bridge input layer, we observe that selecting the first layer yields the worst performance, while the middle and the last layer yield better results. This trend resonates with the linear probing results presented in Figure A4 showing that the proposed bridge input position selection strategy reasonable. For the bridge output layer, no particular bridge output layers consistently yields the best performance, it varies for different bridge input layer. The selection of bridge input layer appears have more impact on the downstream performance, which is reasonable, as the bridge input layer determines the quality and semantic richness of the intermediate representation that the bridge can leverage. In contrast, the bridge output layer primarily affects how the transformed features are reintegrated, leading to more nuanced variations.
D.6 BRIDGE RANK, PROTOTYPE SET, DATASET SIZE ABLATION RESULTS
We conduct additional ablation studies ISRUC (ECG EEG) and FOG (EEG EMG). Figures A9 A10 show that the bridge is relatively robust to the choice of bridge rank r, the number of prototypes Np. and reduce the size of the training dataset [D(rair) %, maintaining stable performance in different parameter settings. Interestingly, we observe sharp drop in performance when the size of the training dataset is reduced to 20% on FOG, but a relatively small performance drop on WESAD and ISRUC
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12
Balanced Accuracy (%)
Bridge Input Layer
62
63.63
64. 64.14
65.15
60
56
Bridge Output Layer
Figure A6: Bridge nine predefined positions for ISRUC ECG EEG. The results are averaged over five seeds. For reference, the proposed bridge position selection strategy selected input layer 12 and output layer 1 for 62.55% balanced accuracy.
74
12
75.26
72
Bridge Input Layer
balanced Accuracy
70
68
69.22
70.73
73:57
64
68.40
65.89
59.96
62
6
12
Bridge Output Layer
Figure A7: Bridge at nine predefined positions for FOG EEG + EMG. The results are averaged over five seeds. For reference, the proposed bridge position selection strategy selected input layer 4 and output layer 12 for 72.24% balanced accuracy.
52
50.06
48.99
47.76
Balanced Accuracy (%)
Bridge Imput Layer
50
51.00
52.55
43.33
48
1
49.73
46.69
44.19
44
18
Bridge Output Layer
Figure A8: Bridge at nine predefined positions for WESAD (PPG + ECG). The results are averaged over five seeds. For reference, the proposed bridge position selection strategy selected input layer 7 and output layer 8 for 52.02% balanced accuracy.
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66
Number Parameters
66
Number Perameteess
Balanced Accuracy (%)
66
65
0.2M 0.4M 0 GMM
65
0.2M 0. JM4M 0. BM
66
Balanced Accurag
64
Balances Accurag
64
64
63
63
63
62
62
62
61
61
60
60
60
59
59
59
58
24
32 40
45
58
100
200
300
58
20
40
60
80
100
(a)
(b) Np
(c) (P(se))
Figure A9: Bridge Training Ablation ISRUC (ECG EEG). Blue: Balanced Accuracy. Red:
Number of Parameters. We vary (a) bridge rank, (b) number of prototypes, and (c) pair dataset size to
understand the robustness of BioX-Bridge and its performance under low-data regime.
Belanced Accuracy (%6
Number Paramoters
Balanced Accuracy (%)
Number of Paramelers
Balanced Accuracy (%)
70
TMM 2.0M
70
TOM 2.0M
70
65
65
65
60
60
60
55
66
55
384
512
100
200
300
40
60
80
100
(a) r
(b) Np
(pair)
Figure A10: Bridge Training Ablation FOG (EEG EMG). Blue:
Balanced Accuracy. Red: Number of Parameters. We vary (a) bridge rank, (b) number of prototypes, and (c) pair dataset size to
understand the robustness of BioX-Bridge and its performance under low-data regime.
D.7 BRIDGE ARCHITECTURE ABLATION
We conducted an additional experiment replacing the proposed bridge network (BioX-Bridge) with fully connected layer (FC-Bridge) for WESAD (PPG ECG). From Table A8 it is observed that FC-Bridge achieves worse transfer performance (47.21%) and requires more parameters (15.4M) compared to BioX-Bridge (52.62% and 0,4M). This highlights the necessity of structured, low-rank bridge architecture rather than a naive fully connected alternative.
Table A8: Bridge architecture ablation. WESAD (PPG - ECG). We replace the proposed bridge network (BioX-Bridge) with fully connected layer (FC-Bridge).
Input Modality
Balanced Accuracy
F1
F1 Weighted
Trainable Parameters
Methods
1
1
Macro
Random
33.33
31.29
35.38
KD
ECG
47.03=11.60
46.36
60.29:12.51
30.4M
KD-Contrast
ECG
50.85++3.61
49.31
63.72+3.22
30.4M
BioX- -Biidg
ECG
52.02++0.80 52.62-10.36
65.12
0.4M
FC-Bridge
ECG
48.59 47.21
61.75+1..90
15.4M
Oracle
PPG
62.96
60.97
74.52
D.8 BRIDGE WITH RESHUFFLED SUBJECTS
To examine how BioX-Bridge performance varies when considering different subjects, we reshuffle the subjects and repeat the dataset splitting procedure. Then, re-run the experiments for WESAD PPG - ECG using the same hyperparameters as before.
Table A9 presents the results for the reshuffled split. We see that BioX-BBride continues outperform the baselines while significantly reducing the trainable parameters. Interestingly, we see that for the reshuffled split, KD-Contrast performs worse than KD), which is not the case for the original split.
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observe that BioX- -Brdde achieves similar balanced accuracy (52.02% vs 54, 14%) and F1-macro (52.62% vs 49.17%). However, there is significant drop in wi-whighted (65.12% vs 52.18%). A similar drop is observed for the knowledge distillation baselines. The drop in Fl-weighted can be explained by the fact that the Oracle (PPG) the teacher for knowledge distillation, also observed significant drop in Fl-weighted (74.52% vs 53.71%).
When comparing the two splits (original split Table A3b
reshuffled split Table A9
Overall, that there are performance differences when considering different subjects, and is largely result of differences in performance of the Oracle models. This is consequence inherent to the knowledge transfer problem itself.
Table A9; Bridge with reshuffled subject. WESAD (PPG + ECG). We reshuffle the subjects and repeat the dataset splitting procedure.
Input Modality
Balanced Accuracy
F1 Weighted 1
Trainable Parameters
Methods
Macro
Random
33.33
31.33
35.33
KD
ECG
48.31:14.10
42.53:15.41
49.67:122.33
30.4M
KD-Contrast
ECG
45.95
41.52-+-2.38
42.20+H5.06
30.4M
BioX-
ECG
5..13
49.17-
5..18
0.4M
Oracle
PPG
65.92
54.74
53.71
Oracle
ECG
55.93
51.74
62.92
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Reviews:
[{"review_id": "YypZ924atj", "reviewer": "Reviewer_Yb6i", "summary": "The authors are focused on the problem of cross-modal transfer between biosignals, broadly trying to distill knowledge from one modality into another. They introduce their model BioX-Bridge, which attempts to learn a transformation from intermediate representations between two frozen foundation models. They argue that this is a more parameter efficient way of doing cross-modal transfer than previous methods. The model works as following: (1) use linear probing to find the layer in your new modality model that maintains good probing performance on labels from the old modality model. (2) select the layer in the old modality model that you want to conduct the transfer from by maximizing CKA similarity to the layer identified in the previous step. (3) train a two-module network that predicts representations from the selected old modality layer given representations from the selected new modality layer. They conduct experiments across three different datasets and compare to other baselines. They show that BioX-Bridge does comparably or better than baselines, and make the point that training parameters are much less than the baselines.", "questions": "- I'm confused by some details of the bridge architecture. It seems like the bridge is learned per-token-- e.g., as mentioned in section 3.4 if the bridge were a linear map there would be $N_{m}^{new} \\times d_{m}^{new} \\times N_{l}^{old} \\times d_{l}^{old}$ parameters, not $d_{m}^{new} \\times d_{l}^{old}$ parameters. Why this choice of mapping? This seems like an unnecessary constraint too, since the input time series may not be naturally chunked into sequence inputs. Did you all try learning a mapping that isn't token specific, e.g. a simple MLP? \n- I would like a better understanding of how much the bridge selection process affects the performance. Table 2 kind of gets at this, but since it's averaged over 9 different combinations it's hard to interpret. Maybe just separate it out, and also show the linear probe performance for the new model layer for each of them.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "- Authors do a good job of discussing prior paper and contextualizing their paper in the broader literature. Figures are also clear and informative.\n- The problem is well-motivated and has clear relevance to practical applications, in particular when discussing parameter efficiency.\n- Experiments show a nice diversity of modality choices (EEG/ECG, EEG/EMG, ECG/PPG)\n- Authors show a good number of different ablations and baseline comparisons.", "weaknesses": "- A key part of the method is the choice of bridge layers. To choose the bridge layer in the new modality, the authors say they find the layer that best linearly represents pseudo labels from the old modality. My understanding is that the pseudo labels used are in fact the downstream task labels. I wonder if this gives their method an advantage over other baseline models that should be discussed. That is, the parameter efficiency of BioX-Bridge is because mappings are learned only between two carefully chosen layers. However, if this choice process relies on the use of downstream task labels, this means the bridge positions are not task-agnostic and that the efficiency comes from having access to the downstream labels. Are the other baseline methods (KD, KD-Contrast, etc) more task agnostic? It seems like these points should be discussed and clarified.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "zVgjhWDghi", "reviewer": "Reviewer_iQEx", "summary": "The paper presents a parameter-efficient framework for unsupervised cross-modal knowledge transfer across heterogeneous modalities. The proposed BioX-Bridge trains a lightweight “bridge” network to align intermediate representations between pre-trained foundation models from different biosignal modalities (e.g., ECG, EEG, PPG, EMG), enabling knowledge transfer without requiring labeled data from the target modality. The framework comprises two key components: a two-stage strategy for selecting the optimal bridge positions (in the source and target network) and a prototype-based architecture with low-rank approximation for efficient high-dimensional projection. The authors evaluate BioX-Bridge across three datasets (WESAD, FOG, ISRUC) with four modalities and six transfer directions, showing that it achieves performance comparable to/better than knowledge distillation baselines while significantly reducing trainable parameters.", "questions": "- See weakness\n\n- If the goal is to align representations between layer m (target modality) and layer l (source modality), why propagate both to the final layer for alignment (Eq. 7)? It seems more direct to use $h^{(old)}_l$ as the pseudo label and align to the prediction/output of $h^{(new)}_m$  This design choice needs clarification—does propagating to the final layer improve alignment?\n\n- The ablation study wrt the bridge placement needs more discussion and detail. On average, choosing a fixed position doesn’t seem to lead to a substantial performance degradation. Are the performances of the “fixed” positions consistent across datasets/modalities/models? Are some combinations better than others?\n\n- How does the bridge position selection strategy perform when the foundation models have very different architectures? The current evaluation uses mostly transformer variants, but biosignal models span can diverse architectures (and modalities).\n\n- Minor comment: I found the notations of “old” and “new” a tad confusing. I would suggest the authors consider using “source/target” for the different models (this is also how I refer to it in the comments)", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "- Leveraging large foundation models for efficient cross-modal knowledge transfer without the computational overhead of “standard” knowledge distillation.\n- The design is straightforward and allows flexibility, incorporating different foundation models as backbones.\n- The paper is well-organized, well-motivated, and easy to follow.", "weaknesses": "- W1: Performance improvements. I agree with the authors' motivation that there are domains/modalities where data is not readily available for (pre-)training large models, in which case approaches like BioX-Bridge can be useful. However, in this particular case, it seems that performance degrades when using BioX-Bridge compared to both source/target oracles. Looking at Table 1, in all but one case, applying BXB leads to worse performance than training a model directly on the “target” modality. This is not explicitly discussed in the paper, so I am comparing across the table (eg. ECG (New) 60.11 vs ECG (Oracle) 63.54). This opens a practical question if, at least for these applications, there is a need for such bridging, and if there is, will it lead to benefits. \n\n- W2. The authors state (L355) \" our method for efficient cross-modal knowledge transfer would be even more valuable as they scale up”. While this may be true in terms of the bridge architecture, in general, I find this counterintuitive in how the bridge in/out placements are chosen. As it is, stage 1, involves exhaustive search of the appropriate layer: This would become really expensive with larger models and more sophisticated probes. Therefore, BXB might not scale well overall.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "WS2K9uynGg", "reviewer": "Reviewer_mvhb", "summary": "This paper researches in the domain of multimodal biosignals, investigating how to transform the knowledge across different biosignal foundation models/modalities while being parameter efficient. Towards this end, the paper proposes to train a lightweight bridge network to align representations across modalities. Specifically, the authors investigated (1) How to select which intermediate representations to use for the construction of the bridge network; (2) Using prototype network with low-rank approximation to guide the learning of the bridge network. The proposed architecture, BioX-Bridge, is validated on three multimodal biosignal datasets, showing better or comparable transfer performance while significantly reducing the parameters needed.", "questions": "1. I am particularly curious about the distribution of the elements computed before the argmin inside equation [4] and [5]. Specifically, how different are the input intermediate representations across different layers for each dataset? How different are the similarity of representation in each layer $l \\in \\[1, ..., L\\]$ for a given layer m? Also, how would the value of m and l differ when considering the same dataset but different sets of samples (e.g. would it be better if the selection of m and l are data-dependent)?\n\n2. Does the performance differ a lot when considering different subjects?", "limitations": "", "rating": 8, "confidence": 4, "soundness": 4, "presentation": 4, "contribution": 3, "strengths": "1. The paper investigated an important problem of multimodal biosignal knowledge transfer.\n2. Novel idea, the bridge architecture is nicely designed with the use of (1) pseudo labels; (2) linear CKA. The bridge architecture also uses a prototype network, that increases the flexibility of incorporating previous knowledges.\n3. Detailed experimental design. Solid selection of existing backbone models for each modality and baseline architectures. The model performs decently well considering its parameter size.\n4. Insightful ablation experiments. Especially insightful to see how the total amount of prototypes would affect the performance, and how the designed architecture is robust to total number of samples.", "weaknesses": "1. The experimental design can be more comprehensive. The authors applied the proposed architecture on a total of three datasets, in each one, they consider 2 transfer directions. The paper lacks an analysis on how the model would perform in situations where the dataset contains more than 2 modalities, for example, check the datasets [1] and experiments presented in [2, 3]. Also, it is unclear how the proposed bridge architecture is specific to each dataset - an interesting experiment to consider is to consider two datasets that share the same multimodality e.g. ECG and EEG, and investigate how the bridge encoder differ for two datasets.\n\n[1] Zhang, Xiang, Ziyuan Zhao, Theodoros Tsiligkaridis, and Marinka Zitnik. \"Self-supervised contrastive pre-training for time series via time-frequency consistency.\" Advances in neural information processing systems 35 (2022): 3988-4003.\n\n[2] Liu, Ran, Ellen L. Zippi, Hadi Pouransari, Chris Sandino, Jingping Nie, Hanlin Goh, Erdrin Azemi, and Ali Moin. \"Frequency-aware masked autoencoders for multimodal pretraining on biosignals.\" arXiv preprint arXiv:2309.05927 (2023).\n\n[3] Fang, Ching, Christopher Sandino, Behrooz Mahasseni, Juri Minxha, Hadi Pouransari, Erdrin Azemi, Ali Moin, and Ellen Zippi. \"Promoting cross-modal representations to improve multimodal foundation models for physiological signals.\" arXiv preprint arXiv:2410.16424 (2024).\n\n2. Completeness of existing experimental results. The paper lacks a more detailed explanation of how the prototypes are selected. Also, for the ablation experiments, the ablation is only applied on the WESAD dataset. Are the conclusions consistent across different datasets?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Oral)