SplashNet: Split‑and‑Share Encoders for Accurate and Efficient Typing with Surface Electromyography
Authors: Nima Ryan Hadidi
Jason Chan
Ebrahim Feghhi
Jonathan Kao
Venue: NeurIPS 2025 poster ()
Keywords: sEMG
EMG
electromyography
typing
keystroke
keyboard
qwerty
emg2qwerty
BCI
HCI
neuromotor
Primary area: neuroscience_and_cognitive_science
TL;DR: SplashNet adds Rolling Time Normalization, Aggressive Channel Masking, and a Split‑and‑Share bilateral encoder to sEMG typing, slashing the emg2qwerty baseline’s zero‑shot and fine‑tuned CERs by 31 % and 21 % respectively with half the parameters.

Abstract: Surface electromyography (sEMG) at the wrists could enable natural, keyboard‑free text entry, yet the state‑of‑the‑art emg2qwerty baseline still misrecognizes 51.8\% of characters zero‑shot on unseen users and 7.0\% after user‑specific fine‑tuning. We trace much of these errors to mismatched cross‑user signal statistics, fragile reliance on high‑order feature dependencies, and the absence of architectural inductive biases aligned with the bilateral nature of typing. To address these issues, we introduce three simple modifications: (i) Rolling Time Normalization which adaptively aligns input distributions across users; (ii) Aggressive Channel Masking, which encourages reliance on low‑order feature combinations more likely to generalize across users; and (iii) a Split‑and‑Share encoder that processes each hand independently with weight‑shared streams to reflect the bilateral symmetry of the neuromuscular system. 
Combined with a five‑fold reduction in spectral resolution (33$\rightarrow$6 frequency bands), these components yield a compact Split-and-Share model, SplashNet‑mini, which uses only ¼ the parameters and 0.6× the FLOPs of the baseline while reducing character error rate (CER) to 36.4\% zero‑shot and 5.9\% after fine‑tuning. An upscaled variant, SplashNet (½ parameters, 1.15× FLOPs of the baseline), further lowers error to 35.7\% and 5.5\%, representing 31\% and 21\% relative improvements in the zero-shot and finetuned settings, respectively. SplashNet therefore establishes a new state-of-the-art without requiring additional data.

SplashNet: Split-and-Share Encoders for Accurate and Efficient Typing with Surface Electromyography
Nima Hadidi
Jason Chan
Ebrahim Feghhi Jonathan C. Kao Kao
University of California, Los Angeles nhadidi@g ucla.
Abstract
Surface electromyography (sEMG) at the wrists could enable natural, keyboard-free text entry, yet the state-of-the-an emg2querty baseline still misrecognizes 51.8% of characters zero-shot on unseen users and 7.0% after user-specific fine-tuning. We trace much of these errors to mismatched cross-user signal statisties, fragile reliance on high-order feature dependencies, and the absence of architectural inductive biases aligned with the bilateral nature of typing. To address these issues, we introduce three simple modifications: (i) Rolling Time Normalization which adaptively aligns input distributions across users; (ii) Aggressive Channel Masking, which encourages reliance on low-order feature combinations more likely generalize across users; and (iii) Split-and- Sh-aare encoder that processes each hand independently with weight-shared streams to reflect the bilateral symmetry of the neuromuscular system. Combined with five-fold reduction in spectral resolution (33-+6 frequency bands), these components yield a compact Split- and-Share model, SplashNet-mini, which uses only 1/4 the parameters and 0.6x the FLOPs of the baseline while reducing character error rate (CER) to 36.4% zero-shot and 5.9% after fine-tuning. An upscaled variant, SplashNet (1/2 parameters, 1.15x FLOPs of the baseline), further lowers eπrоr to 35.7% and 5.5%, representing 31% and 21% relative improvements in the zero-shot and finetuned settings, respectively. SplashNet therefore establishes new statc-of-the-ar without requiring additional data.
Introduction
Translating neuromuscular signals into typed text is a novel but rapidly developing area at the intersection of human-computer interaction and machine learning. The emg2qwerty dataset (14) is the first large-scale benchmark specifically for wrist EMG-based touch typing. It contains over 346 hours of data from 108 users typing sentences while wearing electrode bands on each wrist. This dataset was motivated by the promise of wrist EMG as an avways-available input modality for AR/VR glasses and other scenarios where traditional keyboards are impractical. In parallel, related benchmarks like emg2pose have been developed for EMG-based hand pose estimation (13). reflecting a broad interest neuromotor interfaces for controlling virtual objects, robots, or text entry. Early explorations of EMG for text input date back at least two decades: for example, Yu et al. (22) demonstrated an "EMG keyboard" that allowed forearm amputee input characters via muscle signals. These efforts were limited in scale and accuracy, but they proved the principle that muscle activity alone can convey typing information. Today, with much larger datasets and deep learning models, the accuracy of EMG-based typing has greatly improved (2) 13), making it viable research direction for assistive technology and next-generation user interfaces.
Neuroscience Interdepartmental Program, Department of Electrical and Computer Engincering.
3 Department of Computer Science. Code and checkpoints available at gi www.hh..../.aaaeeeeeee wwwp.//hhhhdd.tttlllsslsleeeeeee
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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User
User 2
b
User 3
User
User 5
Without Rolling Time Normalization
Original Unaugmented PSTHs
Input Features
Augmented View
Time relative to keystroke (s) With Rolling Time Normalization
Augmented View 2
Figure 1: a) Top row: Peri-stimulus time histograms (PSTHs) for the "e" key with (top) and without (bottom) RTN for two users. Each PSTH shows the spectral features derived from the left hand, with spectrograms from the electrodes concatenated together. RTN mitigates the significant differences in across-user feature scale and bias. b) Top Row: PSTHs for the "e" key from 3 training users. Note that some of User 4's features show similar patterns to User 3, while others show similar patterns to User 5. ACM isolates small feature combinations, which are more often shared across users.
Although promising, EMG interfaces face critical challenges in generalization. EMG interfaces suffer from substantial domain shift across users and sessions: anatomy, electrode placement, fatigue, and day-to-day physiology all alter the signal for the same action (4) The emg2querty benchmark probes this shift with (i) a zero-shot test, where one trains many users and tests on new one, and (ii) personalization test that finetunes on a few target-user samples. Their baseline ASR-style system (CNN on spectrograms + LM decoding) misrecognized 51.8% of characters zero-shot, but finetuning cut errors to approximately 7%, showing that most residual errors are systematic, user- specific variations. Yet even large training pools struggle: Sivakumar et al. (14) estimate O(103) users are needed for robust out-of-the-bon performance, a trend echoed by CTRL-Labs' gains from massive data (2).
While scaling may significantly increase the performance of EMG interfaces, collecting these large- scale datasets is tmme-cossuingg and human- resource expensive. We instead present a simpler and complementary path towards improving sEMG generalization. Our key contribution is to first identify limitations in sEMG data that form obstacles to zero-shot generalization. We then propose simple, causal, and computationally inexpensive modifications address these limitations, resulting in models that are more invariant to user/session differences. Our rationale for pursuing causal and computationally inexpensive modifications is to enable sEMG decoders work on-device, an important consideration for future wrist EMG devices.
Insights into improving sEMG decoding
We summarily make three insights, and subsequently, three simple modifications sEMG decoding that substantially improve zero-shot and finetuning performance while reducing computational costs.
Insight 1: sEMG features should be causally normalized per session. sEMG amplitudes routinely differ by an order of magnitude across participants or sessions (3 g). Figure [p highlights this disparity: the same channels exhibit both higher baselines and greater variance for User than for User 1. Hence, EMG signals should obviously be normalized between users. However, the method of normalization critical. Sivakumar al. (4 apply batch normalization that computes mean and variance over the bani-batchh time, and per-electrode frequency bins. Because each mini-batch mixes data from many users and sessions, these statisties fail place features from different users/sessions in common space during training and remain fixed at inference. We instead employ Rolling Time Normalization (RTN), a causal z-scoring of every input feature that updates its mean and variance online. Though simple, this mitigates the scale and shift differences across users, visualized in Figure Tp. Further, RTN requires no calibration and adds virtually no latency for causal, real-time inference. We empirically find this significantly improves zero-shot and finetuned performance.

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a
b
Character Prediction Layer
Left Hand Encoder
Shared Mapping
Shared Weights
Right
Hand Encoder
Figure 2: a) The bilateral structure of keyboard typing. b) The Split-and ani--arnd- macro-architecture.
Insight 2: sEMG feature subsets may be conserved across users. Through visualizing sEMG activity, we empirically find that while two typists rarely share identical high-dimensional EMG signatures for the same keystroke, they often share subsets of electrodes frequency bands that behave similarly. representative example is shown in Figure [b, where we present the PSTHs of sEMG activity from three users. While the full of features looks fairly different across users, randomly selected subsets of features have greater chance of being similar between some users (Augmented View 1 and 2 in Figure TT)). Decoding strategies that hinge on these low-order combinations therefore stand a better chance of transferring across users. While there are many ways to encourage this, we focus on substantially modifying the hyperparameters of dataset augmentation to achieve this. incurring no test-time computational expense. In particular, we do Aggressive Channel Masking (ACM) that on average zeros out more than half of the electrodes and removes broad spectral chunks from the rest. ACM encourages the model to rely smaller, more universal feature sets (Figure [p), which empirically improves zero-shot generalization. We emphasize that this 'low-order simi larity' perspective is working hypothesis rather than definitive statement about the structure of EMG representations: the empirical improvements we observe are consistent with this interpretation but do not prove it.
Insight 3: an inductive bias for bilateral typing. Keyboard typing is fundamentally bilateral: each keystroke is driven solely by the muscles of its own wrist, while the neuromuscular map- ping from activation finger motion almost mirror-symmetric across hands. We therefore propose Split-and-Share architecture that accounts for these two facts in the model. Separate, but weight-shured, subnets encode left- and right-hand sEMG streams in parallel, converging only at the final linear layer where a single vocabulary-lewe softmax decodes the character (Figure 2 This enforces hand-specific locality, but prevents the encoder from entangling spurious cross-hand correlations. Further, re-use of parameters reduces resource demands: the compact Split-and-Shen model uses 0.25x the parameters and approximately 60% of the FLOPs of a conventional joint-hand encoder while matching zhoo accuracy.
Summarily, our new, lightweight architecture built on simple modifications from these three insights reduces zero-shot generalization by 31% and finetuning performance by 21% over the prior state-of- the-art in Sivakumar et al. (14). Together, we both increase performance while reducing computational cost, providing a more feasible path towards on-device, performant computation for sEMG interfaces.
3 Related Works
Per-Session Normalization in EMG: The EMG and brain-computer interface (BCI) literature com- monly normalize input signals prior decoding account for variability. For instance, prior EMG studies expressed amplitudes as a percentage of a reference contraction (maximum voluntary contrac- tion) or applied z-score normalization based on calibration recording (15). These normalizations enable comparisons across muscles and subjects (3) 8). and several works have normalized EMG features per session for machine learning pipelines (15)
20 16 (6. BCI literature, Willett et al. (19) show that continuously updating z-score statistics on spiking features within each session essential for their high-performance speech neuroprosthesis, and Jarosiewicz et al. (5) used online bias correction to improve point-and-click BCI. As described in Section 2, an important insight is not whether sEMG activity should be normalized, but how it normalized. We ultimately find RTN significantly outperforms batch normalization.
3

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Masking-Bass Baakengg-Bssed Regularization for sEMG: SpecAugment, first introduced for speech recognition, has recently been ported to EMG decoding pipelines (22 (4). The version used by Sivakumar et al. (14) is relatively mild: on average only ~ 6% of inputs are masked per training sample, with a given electrode's s spectrogram never being masked across more than 24% of the frequencies. ACM corresponds to hyperparameters that are intentionally harsher, with at least half of the electrodes completely blanked most mini-batches and ~ 55% of input features masked on average. This encourages the model to infer keystrokes from small, overlapping subsets of channels, discouraging brittle reliance on the full spatiotemporal feature pattern.
The concept of processing multiple information streams independently before later integration has been established in Automatic Speech Recognition (ASR), where multi-stream systems effectively handle speech at various resolutions or from multiple arrays ( Similarly, Sign Language Recognition (SLR), previous works have implemented separate processing streams for each hand (7) Leveraging anatomical symmetry through weight sharing has shown success in pose estimation (1 and SLR (17). In the domain of EMMG-base hand- pose recognition, Salter et al. (33) combined data from both hands into unified dataset for single- model training, though this addresses an inherently unimanual task. For bimanual keystroke decoding, past methods have processed signals from both hands jointly (4 18 DD. Our Sadit-and-Share architecture keeps the two wrists separate to respect biomechanics, yet ties the weights, effectively training single-hand encoder with data from both hands. To our knowledge, this has not been explored in EMG keyboard decoding.
Multi-stream architectures and weight sharing:
Methods
4.1 Reduced Spectral Granularity
Let (e(() denote the raw 2 kHz EMG signal from electrode c. Following Sivakumar al. 14 we first form a oog-power spectrogram
Se f) = log (ITFFT())))) + 10-9),
1 33,
using a 64-point FFT (33 linearly spaced bins up to kHz) and a hop size of 16. We instead aggregate the 33 bins into six broader, roughly log-spaced bands, using the same frequency ranges in (2). We refer to this aggregation as reduced spectral granularity (RSG).
B1 = [31.25, 62.5], B3 = [62.5, 125], B3 = [125, 250].
B₁ [250, 375]. B3 [375, 687.5), Bg [687.5, 1000] Hz.
For each electrode the reduced spectrogram obtained by
(t b)
Σ f),, FEB,
b 6.
Hence, we reduce the spectral dimensionality of each electrode from 33 to 6. This represents an over five-fold reduction in features, and empirically equals or improves performance. This also leads SpecAugment to mask significantly more frequencies, even prior to ACM.
4.2 Rolling Time Normalization
Sivakumar et al. (14) normalizes each electrode's spectrogram with batch-norm over the entire mini-batch, frequency, and time axes. This collapses all features recorded on an electrode into single distribution and, crucially, re-uses statistics gathered during training at inference time-an an issue when the test session (or user) drifts from the training distribution. RTN replaces this with a causal, per-feature normalizer that is computed independently for every sample, band, electrode channel and frequency bin. Let
Dt,n, E R.
t = T n ....N 1,
denote the log-pow nog-power value time-step t, mini-bateh index band b, electrode and frequency bin f. RTN maintains cumulative statistics
14 Tanbbef
2 ambba
1r + E,
t ++11 8=0

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which are cheap to update via running sums in streaming settings. During warm-up up period of To 125 frames (the first second) the statistics are frozen those computed over the entire warm-up window, The normalized output is
Tr.nooff Zr,n.b.c.f jie
0x
where ju, and o denote the warm-up-frozen statistics for < Tw and the cumulative statistics above for t 2 Tw. RTN adapts continuously to session-specing non-stationarities, avoids batch-level or training-level statistics, and causal.
4.3 Aggressive Channel Masking
We apply ACM the reduced spectral granularity features with B = 6 bands. We mask inputs in 2/3 of mini-batches. For each mini-batch we draw the number frequency masks, ny Unif 1, Each mask is then sampled independently for every electrode and sample in the batch:
1. Width Unif ... fmax 1), with fmax 12. The implementation clamps the width to the number of bands, w 4- min(w, B). Thus the probability that a single mask erases the entire electrode is Pr(w B]
Lmox 0.5.
fmax 2. Start index fo ~ Unif (0, .... B w
3. Set Xt,c,foslotw-1 f- 0 for all timesteps t.
An individual mask already removes a channel with probability 0.5, while two masks remove a channel with probability 0.75. Remaining electrodes lose large spectrally-coherent chunks. By forcing the network to succeed even when so many electrodes are fully masked, the augmentation discourages brittle high-order feature dependencies and promotes low-order motifs that transfer across users.
4.4 EMG Encoder Architectures
Following spectrogram normalization, all architectures apply two-stage encoder composed of Rotation-Invarian MLP and a Time-Depth Separable Convolution (TDSConv) stack, with linear character prediction layer following the last TDSConv block. These architectures were first presented in Sivakumar et al. (14) and are reproduced here for completeness.
Rotation Invariant MLP. Let E X denote the input, where is the number of
time steps, N is the batch size, B 2 denotes the number of bands (hands), C 16 is the number of electrode channels per band, and F is the number of spectral frequency bins. Each band is passed through Rotation-Invarian MLP, which applies multi-layer perceptron rotated versions of the electrode channels to ensure robustness to cyclic spatial shifts:
1 Σ MIP((rol(xx,.)) o)); O 0= (-1,0,1, O
h
OEO
where each rotated input is flattened across C. x F before the MLP is applied. This results in an embedding h, TTNNND per band. Concatenating the left and right hand features yields a total input of shape h E RTxNN2D to the TDSConv blocks in the baseline model, whereas each hand is processed independently in the Split-only and Aelit-and-Share variants.
TDSConv block architecture. The TDSConv stack (4) is composed of alternating temporal convolution and fully connected blocks. Each convolutional block first reshapes the input from RT RTxNXD to xx where D K H denotes the total feature dimension, K is the number of convolution channels, and is the per-channel hidden width. 2-D convolution with kernel size 1 x w is then applied along the time axis:
B0
z/n, k.
O bln, 4.
θ K
5

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Character Prediction Layer
JOINT
JOINT
JOINT
TDSConv Blocks (4x)
JOINT
LEFT
RIGHT
SHARED
SHARED
Rotation Invariant MLP
LEFT
RIGHT
LEFT
RIGHT
SHARED
SHARED
(a) Joint-Hand
(b) Split-Only
(c) Split-and-Share
Figure 3: EMG encoder architectures. Left (blue) and right (red) hand specific modules only process inputs from a single hand, with hand-specific weights. Joint (purple) modules jointly process inputs from both hands. Shared (gray) modules process inputs from either hand using identical weights. a) Joint-Hand baseline architecture of Sivakumar al. (14), b) Split-only architecture, in which hand-specific modules process signals from each hand separately. c) Split-and-She Sharee architecture, where shared-weight modules process signals from each hand separately.
Because the kernel height is 1, the same weights reused at every hidden-widie position h. The convolution still mixes all K input channels to produce each of the K output channels, giving the layer K2w parameters. The output passed through ReLU, summed with the residual, and normalized using LayerNorm. The subsequent fully connected block consists of two linear layers with a ReLU in between and residual connection:
Zi: LayerNorm(FC, (ReLUU (Zconw))) Zeonv)
Architecure variants. In the architecture of Sivakumar al. (4. which we refer to the Joint- Hand architecture, both hands are processed jointly following the Notation-Invarian MLP, yielding a post-MLP embedding of 2D 768. The TDSConv stack operates with this full dimensionality throughout. In contrast, both the Split-only and Split-and-Shar models operate on embeddings of D 384 per hand, and apply the TDSConv blocks separately to each hand's input. This reduces the width of all fully connected layers from 768 768 to 384 384, leading to four-fold reduction parameters per FC layer, while preserving expressiveness by keeping the number of convolution 32 unchanged. Despite duplicating the encoder, the
channels K 24 and kernel width Split-only model has lower total FLOPs FLOPs-apprrs approximately 60% the baseline- due to the narrower per-hand embeddings. The Split-and -itt-and-hharr model uses the same dual-stream structure but shares all encoder weights between hands. This model has roughly half the parameters the Split-only model, since almost all parameters (excluding the final prediction layer) shared between the two encoders. Notably, the Split-only and Split-and and-Sarr models have the same FLOPs.
To evaluate whether representational capacity might further enhance the strong performance of the Split-and-Share architecture, we also test an Upscaled Split-and-Shan variant. This model increases the per-hand embedding size D 528 and expands the number of convolution channels in the final two TDS blocks from 24 to 48. Nonetheless, the total number of parameters remains about half that of the baseline, and the FLOPs are only modestly higher (about 15%). We refer to the Upscaled Split-and-Shae Share model as SplashNer and the smaller Split-and Share model as SplashNet-mint
4.5 Backspacc-aware beam search with 6-gram Character LM
For decoding we adopt the backspace-awand beam search of Sivakumar et al. (14), which keeps the 50 most-probable CTC label prefixes each frame. Blank transitions simply update the score of an unchanged prefix, while the highest-scoring non-blank characters extend each hypothesis and are re-ranked with a 6-gram character LM prior. When a backspace symbol appears, the algorithm retracts the last maguage-modee contribution, letting deletions correct earlier mistakes without extra

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Table 1: Zero-shot CER (%, mean + s.d. across participants). GFLOPs, and parameters. Columns in gray correspond to training domain validation results, which are reported for transparency but not used as indicators of generalization.
Train domain
Other domain val
Test domain val
Test domain test
GFLOPs
Method
Params
(30 s)
val
Sivakumar et al. 2024
12.14
72.07
52.10
= 5.54
51.78 + 4.61
61.61
5.29M
+ RSG
13.52
67.48
47.26
5.26
5.119
54.15
4.96M
+ RIN
13.09
61.95
39.49
7.45
3:21
54.15
4.96M
+ ACM
23.47
63.08
42.62 + 7.18
7.10
54.15
4.96M
RTN + ACM
21.71
58.85
36.41 7.21
36.1
54.15
4.96M
+ Split
23.93
58.64
37.28 4.91
3733
36.84
2.68M
Share (SplashNet-mini)
26.44
58.20
36.46 7.09
36.41
36.84
1.38M
+ Upscale (SplashNet)
20.59
56.95
1..497..55
35.67 + 6.79
71.38
2.58M
passes. After the final frame any open LM context closed, and the best-scoring prefix provides the decoded keystroke sequence. All beam-search parameters are the same as in Sivakumar et al. (14)
4.6 Dataset Splits
The official emg2qwerty protocol introduced by Sivakumar et al. 14 assigns 100 participants to the training pool and eight held-out test pool. For the 96 training participants with > 4 recording sessions, every session except the final two is used to train generic models, while those last two sessions form the authors' validation set; models are selected on this set before being evaluated on the eight held-out participants.
Because the validation data come from the same individuals seen during training, this procedure encourages models that memorize participant-specifie idiosyncrasies rather than ones that generalize across users. To illustrate this effect while limiting computational cost, we evaluate training domain validation performance using the validation sessions from 18 of the 96 training users, which we term the training domain validation set. The same 18 are also used in Appendix A.8 where they fully held out from training to provide a more appropriate validation signal.
To obtain a validation signal that better predicts zero-shot performance in the main experiments, instead exploit the four training moannng-poolllo participants who recorded < 4 sessions and were therefore excluded from model fitting in the original setup. We take the final session of each of these four participants and combine them into what we term the other domain validation set. These participants exhibit noisier EMG data than those in the official test pool, providing harder proxy for generalization. Nonetheless, they provide a way validating zero-shot generalization without requiring a complete restructuring the official dataset splits.
Results
5.1 Zero-shot model performance
Before turning to the main cross-user generalization results, we first highlight a key discrepancy between training domain validation and held-out user evaluation. As shown in Table 1, the CERs the training domain validation set are not only lower, but the relative ranking of models follows a markedly different pattern than on held-out user evaluation sets. This illustrates why training domain validation provides a misleading signal for cross-user generalization and should not be used for model selection. In Appendix(A.3 [A.8) we further show that when these same 18 users are held out entirely, their CERs rise substantially and align closely with the held-out evaluation trends shown here.
Our analysis centers beam-search decoding using the same 6-gram character LM in Sivakumar al. (4); corresponding -eeddddd results appear in Appendix A.3] All reported p-values were obtained from one-tailed paired t-tests across participants.

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Table 2: Finetuned CER (%, mean + s.d. across participants), GFLOPs, and parameters.
Test domain val
Test domain test
GFLOPs
Method
Params
(30 S)
Sivakumar et al. 2024
8.31 + 3.19
6.95 + 3.61
61.61
5.29M
RSG
6.70 = 3.22
6.92 = 3.79
54.15
4.96M
RTN
6.63 3.58
54.15
4.96M
+ ACM
7
7.87
54.15
4.96M
RTN + ACM
6
6.53 3.227
54.15
4.96M
+ Split
6.04:2.0
6.10
36.84
2.68M
Share (SplashNet-mint)
6
5.87
36.84
1.38M
+ FT Unshare
5.85 + 2.83
5.96
36.84
2.68M
Upscale (SplashNet)
5.46 2.60
5.51
71.38
2.58M
+ FT Unshare
5.57 = 2.65
5.67 + 2.97
71.38
5.06M
First, we apply RSG to replace the 33-bin spectrogram with six coarser frequency bands. RSG leads to modest but consistent improvement in CER from 51.78% to 47.18% (p 7e-5). Part this performance improvement may stem from the implicit amplification of frequency masking under SpecAugment (due to the lower spectral resolution).
Second, we replace batch-level normalization with RTN. RTN significantly improves zero-shot generalization (p 6e-4), reducing the CER 39. 15%. This suggests that input scale and shift differences across users are primary obstacle to cross-user generalization in EMG decoding.
Third, apply ACM, which encourages the model to rely lower-order combinations of input features. Without RTN, ACM reduces zero-shot CER to 42.6% (p .02 vs. +RSG only). Combined with RTN, ACM reduces zero-shot CER 36.42% (p 5e-3 vs. +RSG+RTN), providing strong Joint-Hand baseline.
Fourth, explore architectural modifications that better reflect the causal and bilateral structure of EMG typing. We evaluate a Split-only model that encodes each hand separately without parameter sharing, achieving a CER of 37.37%. Despite having only half the parameters and 66% of the FLOPs of our Joint-Hand baseline, this model performs competitively, though its lack of shared parameters may limit data efficiency. We then evaluate SplashNet-minn: where both hands encoded via identical weight-shares encoders. This model achieves a 36.41% CER on par with the Joint-Hand baseline-whil using just a quarter of its parameters and 66% of the FLOOS. Finally, by increasing the embedding width and expanding the final convolutional layers, we create SplashNet, with similar FLOPSs to the baseline of Sivakumar al. (14) but still half the parameters. This yields further improvement, reducing the CER to 35.67% (p .049 vs. SplashNet-mini).
Together, these results demonstrate that combination of architectural priors, per-session normaliza- tion and principled regularization can significantly improve zero-shot EMG decoding.
5.2 Finetuned Model Performance
We next evaluate the performance of models finetuned user-specific data. As in Sivakumar et al. (14), we maintain identical training hyperparameters during both generic pretraining and finetuning. While this simplifies the analysis, we note that the optimal hyperparameters for finetuning likely differ from those used during pretraining. and further gains may achievable through phase-specific hyperparameter tuning. We also note that, because each recording session involved doffing and re- donning the wristbands, the finetuning experiments inherently probe generalization across electrode placements in different sessions.
We report all results using beam search with an externl character-level language model (LM), which remains the standard for achieving state-of-the the-art performance in CTC-based decoding pipelines. While our models consistently outperform the baseline Sivakumar al. (14) under beam search, we observe slightly worse performance under greedy decoding. We attribute this in part to the more aggressive channel masking induced by our reduced spectral representation ($4). which we discuss further in Appendix A.3

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Participant
Finetuned Participant-wiit Test-set CER
16
Character Erro Rate (%)
12
40
Character
30-
Sivakumar et +RSG
+RTN
+ACM
SplashNet mini
Splashfet
Sivakumar al. +RSG
+RNN
+4CM
Splash min!
SplashNet
Figure 4: Zero-shot and finetuned CER distribution across users. Each of the 8 test users are represented by dot, with lines connecting the same user across models. Boxplots depict median and interquartile ranges. Our methods improve performance for all participants relative to the baseline of Sivakumar et al. (14), with some participants showing very large improvements: two users reach CER between 30% in the zero-shot setting, and one user attains CER below 2% when finetuned.
First, we assess whether the same methods that improved zero-shot generalization also enhance performance in the finetuned setting. Using RSG alone yields similar performance to the baseline of Sivakumar et (4), with a CER of 6.91%, %, while adding RTN yields a significantly improved CER of 6.63% (p = .028 vs. +RSG). Interestingly, applying ACM in isolation worse performance than the baseline of Sivakumar et al. (4). whereas the without RTN-leads to baseline model, with both ACM and RTN, matches the model with RTN alone (6.53% CER). This suggests that masking-inducel variability may hinder learning when the model lacks an appropriate normalization strategy to stabilize the input feature space. Moreover, unlike the generic case, finetuned models may rely more on higher-order feature correlations that are relatively stable across sessions for the same user, diminishing the benefits of aggressive masking.
We next examine architectural changes that explicitly encode inductive biases about the bilateral and causal structure of EMG typing. Both the Split-onh and SplashNet-mini models yield substantial improvements over the Joint-Hand baseline, with CERs of 6.10% and 5.87% (p 6e-3 and p = 2c-3. respectively). For the latter, we evaluate two strategies during finetuning: either maintaining shared weights or duplicating them to allow separate adaptation per hand. Interestingly, both strategies yield similar performance, suggesting that hand-specific encoder weights are unnecessary even in the finetuned setting, although is possible that unsharing weights could become advantageous with more user-specfic data for finetuning.
Finally, we evaluate the SplashNer model. Again, do find any benefit from unsharing the weights during finetuning. With the weights kept shared, SplashNer achieves the best performance overall with CER of 5.51% % (p .02 vs. SplashNer-mint), establishing new state-of-the-un thr-rrrtttttttt for uper-specific EMG keystroke decoding in this benchmark.
Discussion
The central contribution of this work instilling simple, well-motivated priors through preprocessing. augmentation and architecture that can close a surprising fraction of the generalization gap in wrist-EMG typing- SplashNet-mini and SplashNet both achieve large absolute and relative CER reductions (-15.4 / -31.1% zero-shot; -1.44 pp -20.7% after fine-tuning) while cutting parameters to 1/41/2 of the baseline and FLOPs to 0.6-1.15x. These gains are on par with (and complementary to) the 25% error reduction CTRL-Labs Reality Labs et al. (2) reported from doubling dataset size in handwriting task, suggesting that principled inductive biases are as potent as raw data scaling for sEMG.
practical ambition articulated in Sivakumar et al. (14) is to run the entire decoder on the wristbands themselves, thereby mitigating concerns around latency, privacy, and robustness to Bluetooth inter- ference. Achieving bn-dand inference will require an architecture that is not only light-weight but also fully split-able to process and output keystrokes from each wrist independently, without any cross-hand coupling. SplashNet moves part-way toward this goal by duplicating (and sharing) the encoder streams, yet it still merges information at the final linear output layer, each wrist must

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communicate its embeddings to the other. Removing this last dependency, or replacing it with low-bandwidth handshake, remains an open engineering challenge and fruitful direction for future model architecture work. Another promising direction is exploring hybrid cross-hand architectures that retain split-and-share components but introduce limited, structured interactions between the hands, potentially offering a middle ground between the Split-and-Share and Hond extremes.
Beyond these concrete results, our study points several avenues. Normalization: RTN is single-pass causal z-score; richer adaptive schemes (momentum updates, learnable affine transforms, or brief self-supervised calibration) may yield further robustness. Structured masking: ACM already improves zero-shot CER, but spatially contiguous electrode "drop-blocks", global time masks, or anatomical adjacency priors could guide the network toward even more transferable features. Model scale and self-supervision: SplashNet shows that capacity can be reinvested profitably once good priors are place; coupling larger split-and-share encoders with masked- -peckectpreiitioon pre-training may unlock still-higher accuracy without additional labels.
Two limitations deserve emphasis. First, our models are developed for the practical beam-search + LM pipeline; under pure greedy decoding, the Joint-Hand baseline scores the best on unseen users, and ACM dents finetuned accuracy (details and discussion in Appendix A..3) This likely reflects the fact that the Joint-Hand baseline can exploit cross-hand co-articulatory patterns, which tend to correlate strongly with character bi- and tri- graass These weak statistical regularities effectively act as an implicit language model under greedy decoding. By contrast, Aplit-and-Share models do not have access to these bilateral dependencies, and ACM further suppresses within-user cues by enforcing lower-order feature reliance. Once external 6-gram LM is applied, however, its much stronger prior readily compensates for both the lost co-articulatory signal of the Split-and-Shan and the reduced within-user discriminability induced by ACM, thereby revealing the underlying generalization advantage of our approach. Second, all experiments use healthy participants; the extent to which our priors transfer to populations with motor or limb differences (where electrode placement and muscle recruitment differ markedly) remains to be tested.
models
In sum, we show that the long-standing vision of "keyboard "reyboard-quality" EMG typing can be advanced not only by more data but also by better assumptions. By pairing causal normalization, aggressive yet structured regularization, and a symmetry-awann encoder. we make significant strides towards truly out-of-the-bor wrist-sEMG typing-paving the way for real-world assistive and AR/VR interfaces.
7 Acknowledgments
This work was supported by the following awards to JCK: NSF CAREER 1943467, NIH DP2NS122037, NIH RO1NS121097. The authors would also like thank Andrea Ortone for carefully checking the FLOPs calculations and providing helpful feedback.
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A Technical Appendices and Supplementary Material
A.1 Training Details
All models were trained for 150 epochs with the hyperparameter configuration of Sivakumar et al. (14). we used the same optimizer (Adam), the same LR schedule (linear warmup from 1e-8 to le-3 for first 10 epochs, followed with cosine annealing to le-6 until epoch 150), and the same augmentations. These included rotation augmentation, which shifts all electrodes each band one electrode to the left or right for each training sample, and temporal alignment jitter augmentation, which jitters the EMG signals from each hand by maximum offset of 60 ms. We only made two modifications to hyperparameters:
1. Input window length. We employ training windows, rather than the 4 s clips used by Sivakumar et al. (14). This was done to speed up training.
2. SpecAugment settings.
With ACM. We raise the maximum frequency mask width to 12 and disable time masking.
Without ACM. We retain the original SpecAugment parameters of Sivakumar et al. (4) (maximum frequency mask width 4; up 3 time masks per electrode per sample, each masking out up to 200 ms).
A.2 Evaluation Details
All validation and test sessions were loaded as single sample (i.e. with batch size of 1). By comparison, Sivakumar et al. (14) loaded validation (but not test) sessions as shortened chunks. Although this unlikely to have affected their greedy decoding results as their architecture (and ours) has receptive field of only 1 second, this might have affected the validation performance they reported with an external LM, which would lose the character history between each chunk and therefore make poorer predictions. This might explain why the validation CER they report with beam-search (8.31% % CER) is conspicuously higher than their test performance (6. 66%5 CER) compared to the gaps between validation and test sessions.
12

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A.3 Greedy vs. Beam-Search Decoding
Our main text focuses on beam-search decoding with an external 6 6-gram language model (LM), because this is the configuration most relevant realistic, cilency-constrained deployment. For completeness, we also report greedy CTC decoding results,i.e. decoding without any LM.
Table 3: Zero-shot CER (%, across participants). Columns in gray correspond training domain validation results, which are reported for transparency but not used as indicators of generalization. Note that the training domain validation results shown here with greedy decoding were computed over all 96 training users; the beam search decoding results are computed on the 18 user subset.
Train Train Other Other Test Test Test Test domain domain domain domain domain domain domain domain val (greedy)
Method
val (beam) (greedy) (beam) (greedy) (beam) (greedy) (beam)
val
val
val
val
test
test
Sivakumar et al. 2024
22.51
12.14
72.44
72.07
55.57
52.10
55.38
51.78
+ RSG
23.59
13.52
68.55
67.48
52.24
47.26
52.27
47.18
+ RTN
23.49
13.09
64.19
61.95
46.31
39.49
46.07
39.15
+ ACM
34.77
23.47
65.60
63.08
48.87
42.62
49.23
42.62
+ RTN ACM
32.85
21.71
61.82
58.85
43.72
36.41
43.80
36.42
Split
35.59
23.93
61.74
58.64
45.73
37.28
45.69
37.37
+ Share (SplashNet-mini)
37.72
26.44
61.07
58.20
45.33
36.46
45.26
36.41
Upscale (SplashNer)
33.57
20.59
60.16
56.95
44.79
35.49
44.78
35.67
Zero-shot generalization. Table 3 shows that, in the zero-shot regime (i.c. on non-training domains), the joint-hand architecture with RTN and ACM often surpasses the Split-and-She architecture under greedy decoding, even though the ranking reverses once beam search is applied. The most plausible explanation is that the roint-hand encoder can observe both wrists simultaneously and therefore learns a stronger implicit LM, capturing bi- and tri gram dependencies spread across hands. While that emergent linguistic prior is helpful when no external LM available, becomes redundant-aan and potentially counter-rroductive once more reliable 6-gram LM is introduced at inference time.
Table 4: Finetuned CER (%) with and without beam search.
Test Test Test domain val (greedy)
Test domain
domain val (beam)
domain test (greedy)
Method
(beam)
Sivakumar et al. 2024
11.39
8.31
11.28
6.95
+ RSG
12.11
6.70
12.50
6.92
RTN
11.83
6.47
11.75
6.63
ACM
14.24
7.18
14.74
7.45
RTN ACM
12.70
6.55
12.90
6.53
+ Split
12.67
6.04
12.89
6.10
Share
13.01
6.13
13.22
5.87
FT Unshare
12.68
5.85
13.07
5,96
Upscale (SplashNet)
12.10
5.47
12.39
5.51
+ FT Unshare
11.80
5.57
12.13
5.67
Fine-tuned models. A different pattern emerges after user-specific fine-tuning (Table None of our variants fully match the baseline of Sivakumar et al. (14) under greedy decoding, despite decisive gains once beam search enabled. Because the only changes between the baseline and the RSG" model are (i) four-fold longer clips during training, (ii) coarser spectral resolution, and (iii) five-fold higher probability of channel masking, the degradation must stem from one (or combination) of these factors.
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Two clues implicate aggressive channel masking (ACM). First, the model with RTN but not ACM achieves the best greedy scores, whereas the model with just ACM but not RTN yields the worst greedy performance. Second, the performance gap between the model with just RTN and the model with both RTN and ACM vanishes under beam search, indicating that the external LM compensates for information lost when ACM forces the network to rely on low-order feature combinations. We therefore hypothesize that ACM, while beneficial for cross-user generalization, removes within-user cues that help distinguish confusable keystrokes, weakness that beam-search decoding can largely recover.
To confirm that the implicitly increased channel masking in the "+ RSG" model (and all other models) largely responsible for the uniformly worse greedy decoding performance we see compared to the baseline of Sivakumar et al. (14), we ran an additional experiment in which we trained model similar to our +RSG model, but apply SpecAugment on the 33-bin spectrograms from each channel before the bin aggregation of RSG (rather than after). This keeps the extent of masking equivalent to that of Sivakumar al. 2024 while also allowing us to use the RSG frontend. This model does not perform significantly worse than the baseline of Sivakumar al. 2024 with greedy decoding, confirming our suspicion that the core reason for the worse greedy decoding results the finetuned case is increased channel masking.
Table 5: Finetuned CER (%) under greedy decoding. Values are mean standard deviation.
Test domain val (greedy)
Test domain test (greedy)
Method
Sivakumar 2024
11.39 + 4.28
11.28 4.45
RSG
12.11 + 4.67
12.50 = 4.97
RSG w/ pre-aggregation masking
11.02 4.32
11.53 4.70
In summary, greedy decoding accentuates two complementary inductive biases: (1) joint-hand encoders learn a useful internal LM, an advantage that vanishes once an external LM is applied, and (2) aggressive masking of input channels trades cross-user generalization for within-user discriminability. a trade-off that RTN and beam-searde decoding can effectively offset.
A.4
Calculation of FLOPs
FLOPs were measured using PoopTensorDissaaccMMdde in PyTorch with an arbitrary 30-second input.
A.5
Additional Ablations on ACM Masking, RTN Sliding Windows, and RSG
Table 6: Zero-shot CER (%, mean = s.d. across participants) for ablations ACM mask width and RTN sliding window. "-RSG" corresponds to applying ACM on the full-resolution spectrogram as described in the text.
Other domain val
Test domain val
Test domain test
Method
SplashNet-Mini
58.20 + 10.50
36.46 + 7.09
36.41 + 7.30
4s SW inference
60.43 + 7.86
37.57 + 7.18
37.71 + 7.47
+ 4s SW train + inference
60.43 + 7.70
36.69 + 7.80
16s SW inference
57.24 t 11.00
7.34
36.73 7.40
+ mask width = =8
58.12 + 11.72
36.57
36.05 105+:0 6.74
mask width 16
57.65 10.47
36.66 6.21
36.92 7.40
- RSG
60.70 + 11.23
6.58 37.90
37.19 - 6.33
Point-Hand baseline (+ RSG RTN ACM)
58.85 + 10.50
7.24
36.42 + 7.11
+ mask width = 8
59.41 10.63
37.55 6.97
37.30 - 6.90
mask width 16
57.17 + 11.32
36.95 7.62
36.47 7.96
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Table 7: Finetuned CER (%, mean + s.d. across participants) for ablations on ACM mask width and
RTN sliding window.
Test domain val
Test domain test
Method
SplashNet-Mini
6.13 + 2.96
5.87 + 3.04
+ 4s SW inference
6.10
6.28 3.09
+ 4s SW train + inference
2.86
+ 16s SW inference
5.76
5.74
+ mask width 8
5.85 +.772
3.11
mask width 16
5.72 2.88
RSG
5.75 5.87
3.06
Joint-Hand baseline (+ RSG RTN + ACM)
6.55 +
6.53 3.27
+ mask width
6.14
6.56 + 3.60
+ mask width 16
6.55 + 3.09
6.45 3.18
We conducted additional ablations to examine the sensitivity of model performance to the strength of ACM, the presence of RSG, and the temporal window used for RTN normalization. Unless otherwise noted, none of the differences reported here reached significance under two-tailed paired t-test across participants.
ACM mask width. In our main experiments, ACM uses a maximum frequency mask width of 12 bins, while models without ACM use maximum width of 4. Here, trained SplashNer-mint and the Joint-Hand baseline with maximum mask widths of 8 and 16. As shown in Tables and 7 these variations produced small, nonsignificant changes in CER in both the zero-shot and finetuned settings, indicating that performance is not particularly sensitive to the precise mask width within this range.
RSG. We also evaluated variant without RSG, in which ACM was applied to the full-resolution spectrogram by masking 6-bin dummy vector and inverting the 33-to-6 bin mapping used in RSG to obtain a 33-bin mask. Although performance differences relative to the standard RSG configuration were small and nonsignificant, this variant considerably increases both compute and memory requirements since it has more than sixfold greater input feature dimensionality.
RTN sliding window. Our original RTN normalization uses all past time points within sample to compute normalization statistics. We additionally evaluated inference time sliding- window variants with 4-second and 16-second windows. Using 4-second window led to small but statistically significant degradation in zero-shot performance (p < 0.05, two-tailed), while a I 16-second window yielded performance comparable the default setting. This likely reflects the fact that models were trained on 16-second samples and benefited from matching normalization context at test time. When models were also trained with 4-second RTN sliding window, this degradation disappeared, and differences were no longer significant in either the zero-shot or finetuned settings.
Summary. Across all tested configurations, performance differences were small and generally nonsignificant. ACM is robust to maximum frequency mask width changes the range 8-16 bins. Removing RSG does not significantly degrade performance but incurs higher compute and memory costs. RTN performance remains stable across temporal windows when training and inference are matched, with only modest increase in CER when using 4 second windows at inference time alone.
15

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A.6 Aggressive channel masking with mean imputation
Table 8: CER (%, mean + s.d. across participants) for different ACM masking configurations.
Test domain val
Test domain test
Method
Sivakumar et al. 2024
52.10 + 5.54
51.78 + 4.61
RSG
47.26 5.26
47.18 5.19
ACM
42.62 + 7.18
42.62 + 7.10
ACM (mean impute)
47.59 7.16
48.43 6.24
RTN ACM
36.41 7.21
36.42 7.11
We performed an ablation to test whether RTN might interact with ACM by stabilizing input features such that the default masking value of 0 corresponds their per-session mean. When using ACM with RTN, the default mask value of O corresponds to the per-sample mean for each feature, whereas under BatchNorm this same value can be far out of distribution for given feature from given sample. To test whether it is important that the masking value is the per-sample feature mean, replaced RTN with standard BatchNorm while setting the ACM masking value the per-sample mean of each feature, training a Joint-Hand model with RSG, BatchNorm, and ACM using per-channel sample mean imputation.
Somewhat surprisingly, this variant performed on par with the model with RSG alone and substantially worse than the RSG + BatchNorm + ACM configuration with standard zero imputation. This suggests that simply matching the mask value to the per-session mean is not sufficient to reproduce the performance benefits obtained with RTN ACM, let alone to obtain the benefits of ACM in the absence of RTN. More generally. these results indicate that the choice of masking value being stable across samples (rather than corresponding to the per-sample mean) appears to be the more important factor for ACM's s effectiveness in this setting.
A.7 UMAP analyses on early intermediate representations
+RSG
+RSG+ACM
+RSG+ACM+RTN
SplashNet-mini
Figure 5: UMAP visualization of model activations after the first TDSConv block for four models (+RSG, +RSG+ACM, +RSG+ACM+RIV and SplashNet-mini), We extracted activations from every 100th timestep from session of each of the 8 held-out users. Colors indicate the user identity of each point. In the models without RTN, some users' representations occupy largely disjoint regions of the activation manifold, whereas models with RTN (+RSG+ACM+RTN and SplashNet-mini) produce markedly more overlapping per-user representations, indicating improved cross-user alignment in the learned feature space.
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A.8 Alternative train-test split analyses
Table 9: CER (%, mean + s.d. across participants) with new 78/18 train/validation split. The first column shows performance on the same 18 users when they were included in the original training set (i.e., the previous training domain validation performance), while the remaining columns show results when these 18 users are held out for validation. The test set remains the same held-out users.
Train domain val (prev. split)
Train domain val (18 held-out)
Test domain val (18 held-out)
Test domain (18 held-out)
Method
+ RSG
13.52 7.59
58.42 + 10.43
47.89 = 6.36
49.01 = 5.35
RTN
13.09 + 6.32
53.99 + 11.14
42.81 6.75
42.62 = 6.89
+ ACM
23.47 9.74
55.95 + 10.46
44.59 = 6.47
45.10 + 7.26
RTN ACM
21.71 9.67
+ 51.25 11.49
38.72 6.63
39.05 = 7.22
+ Split
23.93 - 10.99
51.57 + 11.78
39.81 6.72
40.14 7.16
Share
26.44 10.64
49.84 + 12.57
38.60 7.52
38.45 + 7.86
A.9 Analyses on emg2pose dataset
Table 10: Mean angular error (degrees) and landmark distance (mm) under different generalization regimes, All results in the "tracking" setting.
Angular Error
Landmark Dist
Generalization
Model
User
RTN+ACM
7.5951
10.1567
Stage
RTN+ACM
11.1243
15.2404
User, Stage
RTN+ACM
10.8433
15.3644
User
RTN
7.6007
10.1641
Stage
RTN
11.1627
15.2241
User, Stage
RTN
10,9085
15,3851
User
ACM
7.6388
10.2394
Stage
ACM
11.2601
15.4489
User, Stage
ACM
11.0343
15.6413
User
Baseline
7.6549
10.2585
Stage
Baseline
11.2892
15.4360
User, Stage
Baseline
11.1222
15.7388
Although the focus of this work is EMG keystroke decoding, also performed preliminary exper- iments to assess how RTN and ACM transfer to the emg2pose benchmark (13). A key consideration that the baseline model of Salter et al. (13) uses learned convolutional featurizer that immediately mixes signals across all electrodes, rather than spectrogram braabdeg per-electrode features as in our keystroke decoding setting. This architectural difference makes direct application of RTN and ACM less straightforward.
To adapt these methods, we applied RTN and ACM to the outputs of the first convolutional layer in the fcaturizer. Since these intermediate features are not spectrograms, ACM was implemented by randomly zeroing out 50% of the fcatures in one-third of training samples and 75% in another third. The resulting performance, summarized Table I0] shows minimal differences relative to the baseline in both mean angular enror and landmark distance across user, stage, and combined generalization regimes.
These findings are somewhat unsurprising: applying ACM after early feature mixing likely limits its ability to enforce robust low-order structure, and RTN is less meaningful without clear per-electrode feature boundaries. Future work should explore applying these methods directly to the raw EMG signal before featurization, or using architectures with explicit per-electrode feature streams (e.g.. spectrogram based structured learned featurizers), where RTN and ACM may have stronger effects.
17

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A.10
Compute Resources
All experiments were run on a single RTX 4090 (with 24GB VRAM) GPU. Each generic model took roughly 24 hours to train. Finetuning took 20-30 minutes for each of the 8 test users, and an additional 30 minutes evaluate with beam search for each of the test users.
A.11 Dataset License
The emg2qwerty dataset (14) is available under the CC-BY-NC-4,0 license, which this work is compliant with.
18

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25


Reviews:
[{"review_id": "zqtx0Wdsf5", "reviewer": "Reviewer_gmaj", "summary": "This work improves upon the emg2qwerty benchmark (https://arxiv.org/abs/2410.20081) which is the current public state-of-the-art for keyboard typing using surface electromyography. The authors propose simple and efficient architectural modifications - (1) per-session causal time normalization, (2) aggressive channel masking, (3) per-wrist encoder prior to fusing - that lead to significant improvements in this 108 user dataset on both the zero-shot user benchmark (~52% to ~36%) and finetuned benchmark (7% -> 5.5%). The authors also provide a smaller version of the model with much less params and FLOPs with spectral frequency reduction that's nearly just as performant.", "questions": "1. Have the authors considered entirely changing the architecture from the TDS convolutional model to transformer based?\n2. For the split and share architecture, the authors can clarify the order of electrodes between the left and right bands. Is it possible to see one hand as a the mirror of another and use a \"fully\" shared encoder rather than learning hand specific weights?\n3. Does the gain obtained from Aggressive Channel Masking indicate that a subset of channels may be more useful than others, and is there room to simply remove low SNR channels entirely for the task of typing?", "limitations": "Yes", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 4, "significance": 3, "originality": 3, "strengths_and_weaknesses": "The significance of the paper lies in the simplicity of the methodologies and the extent of the improvement obtained, especially on the zero-shot user generalization benchmark (~52% to ~36%) without any additional data.\n\nStrong justifications are provided for each of the three contributions in the methodology via inductive priors, visualizations (peri-stimulus histograms pre and post within-session normalization), and with experimental results including detailed ablations.\n\nOpen-source code built on the original emg2qwerty repo is provided for reproducibility and continuing to improve upon this baseline.\n\nThe \"mini\" version of the model obtained with spectral resolution reduction achieves almost similar performance with significant reductions to model size (1/4th) and compute (0.6x).\n\nThe paper excels in overall clarity - the motivation, inductive priors for each of the improvement proposed, methodical description of the improvements, experimental results showing clear performance gains, and ablations showing how the methods stack up.\n\nOverall, a strong paper that demonstrates an outsized improvement on a publicly available dataset and benchmark with very simple strategies for the niche but emergent modality of surface electromyography.", "was_revised": true, "final_justification": "The authors responded to the suggested improvements satisfactorily in their rebuttal. The suggestions were largely posed with the intention of further improving this work, and they shouldn't block the acceptance of this work. My rating remains unchanged.", "rebuttal": "We thank Reviewer gmaj for their positive assessment of our work's simplicity, significant improvements, and reproducibility, as well as their insightful questions on architectural alternatives and channel masking. Our rebuttal addresses these points, including clarifications on electrode symmetry and potential for channel ablation, which align with our focus on efficient, generalizable EMG decoding.\n\n**Q1:** *Have the authors considered entirely changing the architecture from the TDS convolutional model to transformer based?*\n\n- Though we considered changing to a transformer-based model early on, we ultimately felt that the initial hyperparameter tuning and subsequent model training would be prohibitive given our fairly limited compute budget (1x RTX 4090), and decided to instead focus on simple, principled methods starting from the already-tuned and compact TDSConv baseline, with an emphasis on efficiency and feasibility for on-device applications. We leave investigations of other block micro-architectures to future work, and note that the Split-and-Share macro-architecture is generalizable to any encoder block micro-architecture, as are the other methods we propose.\n\n**Q2:** *For the split and share architecture, the authors can clarify the order of electrodes between the left and right bands. Is it possible to see one hand as a the mirror of another and use a \"fully\" shared encoder rather than learning hand specific weights?*\n\n- Yes, the order of electrodes is mirror symmetric between the left and right bands, so it is possible to see one hand as a “mirror” of the other. The encoder weights are in fact fully shared between the two hands for our SplashNet models, aside from the final linear decoding layer; the reason why the last layer must differ is that the same movements correspond to different keystrokes between the left and right hands. For example, the same movement that generates an “f” keystroke on the left hand generates a “j” keystroke for the right hand. Hence, in the same sense that the right and left hands use a similar range of motions to type different sets of keys, we use the same encoder for both hands, but allow the final keystroke decoder layer to differ.\n\n**Q3:** *Does the gain obtained from Aggressive Channel Masking indicate that a subset of channels may be more useful than others, and is there room to simply remove low SNR channels entirely for the task of typing?*\n\n- We do not take the gain obtained from ACM to necessarily indicate that a subset of channels may be more useful than others, as the principle behind using ACM (breaking the model’s reliance on user-specific high-order feature correlations) still applies just as well if all channel subsets are equally informative. Nonetheless, it seems very likely true that some channels are more useful than others: different electrodes record from different muscles, which are almost certainly not uniform in their informativeness for keystroke decoding or consistency across the user population. We do not explore full-on ablations of channel subsets in this work, but we believe that this a fruitful direction, especially considering that many consumer-grade EMG devices in the near future will likely feature fewer than 16 electrodes due to cost constraints."}, {"review_id": "uOH92K4J62", "reviewer": "Reviewer_5Ux2", "summary": "This paper revisits the emg2qwerty baseline for decoding typing movements from sEMG. They add several features to the network: (1) a rolling time normalization (2) additional agressive channel-time masking (3) weight sharing across encoders. Overall this reduces parameter counts and improves performance on the baseline by approximately 25-40% and for small networks reduces computation time. They present a set of ablations that expose which features are most important for the improved performance.", "questions": "See weaknesses above", "limitations": "yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 3, "originality": 2, "strengths_and_weaknesses": "Strengths\n* Improves the baseline performance on emg2qwerty\n* Paper is well written and easy to follow and makes clear the relevant points\n* The ablations are clear and some of the details are interesting, e.g. the other domain validation set\n\n\nWeaknesses above\n* Improvements are somewhat incremental; the novelty of these is more based on a combination of parts rather than any individual modeling innovation, e.g. changing the masking fraction or z-scoring input or sharing weights alone would be quite insufficient. But the paper is very clear and well executed. \n * An experiment on an additional EMG dataset showing the generality of some of these advances (e.g. emg2pose or ninapro) would be beneficial. \n* The architectural innovations and experiments could be greater, e.g. exploring lightweight sharing across hands before the character encoding to capture aspects of coarticulation. \n* There isn’t quantitative experiments examining different types of rolling time normalization or the amount of masking. These should be further developed and quantified.", "was_revised": true, "final_justification": "Based on inclusion of additional dataset to show generality of the approach I raised my score to a 5, and looking at the feedback it seems there is general interest in the approach.", "rebuttal": "We appreciate Reviewer 5Ux2's recognition of our improved performance, clear writing, and clear ablation studies, along with their constructive suggestions for expanding experiments on other hyperparameters, architectures and EMG datasets. In our rebuttal, we respond to these by providing new quantitative results on masking and normalization variants, and we discuss hybrid architectures and extensions to other EMG tasks as promising future directions.\n\n**W1:** *Improvements are somewhat incremental; the novelty of these is more based on a combination of parts rather than any individual modeling innovation, e.g. changing the masking fraction or z-scoring input or sharing weights alone would be quite insufficient. But the paper is very clear and well executed.*\n\n- We thank the reviewer for their comments regarding our paper’s clarity and execution. We agree that the key novelty of our work was not to propose model components that are altogether new for machine learning, but rather to derive very simple and inexpensive algorithmic improvements from insights into the data and task structure of EMG keystroke decoding. While we believe research into entirely new methods will be invaluable for further improvements, we also believe that given the early stage of this benchmark, it is appropriate to seek simple and effective solutions that provide insight into the problem. We emphasize that these simple and effective solutions are also more likely to have significant impact on real-time compute-limited devices.\n\n**W2:** *An experiment on an additional EMG dataset showing the generality of some of these advances (e.g. emg2pose or ninapro) would be beneficial.*\n\n- Emg2pose and Ninapro both investigate inherently unimanual tasks, so the Split-and-Share macro-architecture does not apply in these cases. While we believe that the rationale for Rolling Time Normalization and Aggressive Channel Masking should also apply to the emg2pose and Ninapro datasets, the focus of this paper was on the keyboard typing task, and the emg2qwerty dataset is by far the largest and highest quality publicly available dataset for this task. We therefore decide to leave these investigations into other tasks for future work, and will make this clear in the Discussion.\n\n**W3:** *The architectural innovations and experiments could be greater, e.g. exploring lightweight sharing across hands before the character encoding to capture aspects of coarticulation.*\n\n- We appreciate the suggestion and agree that exploring architectures with lightweight joint-hand processing before character decoding could offer a compelling way to combine the strengths of both Split-and-Share (e.g., improved within-hand EMG-to-keystroke modeling) and Joint-Hand (e.g., improved coarticulation modeling) approaches. In this work, we intentionally focused on evaluating the benefits of the Split-and-Share architecture in order to isolate and highlight the advantages of improved within-hand processing. That said, we believe hybrid approaches, such as introducing cross-hand interactions at intermediate layers, are a promising direction for future work. However, we also view this as a substantial and orthogonal design space, deserving of focused investigation in its own right. We will explicitly discuss this in our revised draft as an important direction for future work. \n- We also note that the architectural constraints imposed by the Split-and-Share design bring practical advantages. Aside from parameter and compute efficiency, our architecture is well-suited for future directions such as fully on-wrist typing systems, leveraging unimanual foundation models, or adapting the trained encoders for other unimanual finger-tapping tasks. We will make these points more explicit in our revised draft.\n\n**W4:** *There isn’t quantitative experiments examining different types of rolling time normalization or the amount of masking. These should be further developed and quantified.*\n\n- We fully agree that such results would greatly strengthen our paper. We have swept two additional masking levels for the SplashNet-Mini model, along with two inference-time sliding window variations of rolling time normalization.\n- For our new experiments, we trained SplashNet-mini models with maximum frequency mask width set to 8 and 16 (the default value for ACM in our paper was 12, and the default value without ACM was 4). We observe small, nonsignificant differences in CER, indicating that the model is not particularly dependent on the strength of ACM within this range. We will further sweep frequency mask width on all of our main models (RSG+RTN+ACM, SplashNet-mini, SplashNet) for the final manuscript.\n- We also evaluated inference-time variants of RTN using a sliding window. Though our original RTN used all previous time points in a sample to normalize the data, we have now run experiments with two sliding-window settings: using a 4 second and 16 second sliding window for input feature normalization. We observed a slight degradation of performance when using only a 4 second window but no significant difference when using a 16 second window. However, it is worth noting that the model was trained on samples of length 16 seconds, with RTN using data from up to 16 seconds for normalization during training. In our revised draft, we will further include experiments where we train with RTN using a sliding window of up to 4 seconds during training, to see if this could improve robustness when using a 4 second sliding window at inference. We are happy to include any other types of rolling time normalization that the reviewer would like to see in the revised draft.\n- Results in the zero-shot setting:\n|  | Other domain val | Test domain val | Test domain test |\n| --- | --- | --- | --- |\n| SplashNet-Mini | 58.20 ± 10.50 | 36.46 ± 7.09 | 36.41 ± 7.30 |\n| + 4s SW inference | 60.43 ± 7.86 | 37.57 ± 7.18 | 37.71 ± 7.47 |\n| + 16s SW inference | 57.24 ± 11.00 | 36.43 ± 7.30 | 36.73 ± 7.40 |\n| + mask width = 8 | 58.12 ± 11.72 | 36.57 ±7.02 | 36.05 ± 6.74 |\n| + mask width = 16 | 57.65 ± 10.47 | 36.66 ± 7.21 | 36.92 ± 7.4 |\n\n- Results in the finetuned setting:\n|  | Test domain val | Test domain test |\n| --- | --- | --- |\n| SplashNet-Mini | 6.13 ±2.96 | 5.87 ± 3.04 |\n| + 4s SW inference | 6.10 ± 2.91 | 6.28 ± 3.09 |\n| + 16s SW inference | 5.76 ± 2.64 | 5.74 ± 2.86 |\n| + mask width = 8 | 5.85 ±2.72 | 6.02 ± 3.11 |\n| + mask width = 16 | 6.09 ± 2.88 | 5.72 ± 2.96 |"}, {"review_id": "5nGwhc6kgf", "reviewer": "Reviewer_zDKU", "summary": "This paper introduces SplashNet, a novel architecture for surface electromyography (sEMG)-based keystroke decoding. The authors address key challenges in cross-user generalization by proposing three innovations: (1) ​​Rolling Time Normalization (RTN)​​ for adaptive per-session signal standardization, (2) ​​Aggressive Channel Masking (ACM)​​ to encourage robust low-order feature dependencies, and (3) a ​​Split-and-Share encoder​​ leveraging bilateral symmetry of typing biomechanics. Evaluated on the emg2qwerty benchmark, SplashNet-mini reduces character error rates (CER) to 36.4% zero-shot and 5.9% after fine-tuning while using 1/4 the parameters and 60% FLOPs of prior work.", "questions": "1. Why were the 4 low-session users selected for \"other domain validation\"? Could this choice favor models overfit to noisy data? Providing results on the official validation set alongside Table 1 would clarify generalization.\n\n​​2. Does RTNs online normalization enhance ACMs feature-subset regularization? Please provide an analysis like mutual information between masked features which could disentangle their interaction.\n\n3. The paper assumes that \"low order feature similarity\" is the direct reason for ACM to improve generalization, but lacks cross user similarity experiments on feature subsets", "limitations": "The authors partially address limitations. However, there is a lack of cross user similarity experiments on feature subsets, and sensitivity analysis on masking ratios is not provided", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "1. Well-structured with clear motivation.This article proposes persuasive improvements based on empirical findings of signals.\n2. Rigorous evaluation via ablation studies validates each component’s contribution. The 5.5% fine-tuned CER significantly advances state-of-the-art\n3.The bilateral Split-and-Share design is a biologically inspired novelty, while ACM rethinks augmentation for EMG-specific challenges.\nWeaknesses:\n1. ​​Validation Set Justification​​: This method lacks validation on other datasets.\n2.The paper did not provide a sensitivity analysis of masking ratios, and zero sample generalization experiments were conducted with different masking ratios (30%/50%/70%)", "was_revised": true, "final_justification": "I appreciate the authors' comprehensive response, which has adequately addressed several of my initial concerns. In light of these improvements, I have decided to revise my score upward.", "rebuttal": "We are grateful to Reviewer zDKU for highlighting our well-motivated improvements and rigorous ablations, while pointing out areas for deeper analysis like validation sets and feature interactions. Our rebuttal includes new sensitivity analyses on masking ratios, training domain validation set results, clarifications on RTN-ACM synergies, and reframing of cross-user feature similarity as a hypothesis.\n\n**W1:** *Validation Set Justification: This method lacks validation on other datasets.*\n\n- For this comment, we were unsure if you were referring to concerns with our validation set or were pointing out that we only used one dataset (emg2qwerty). We address the latter point here, and we thoroughly address the former point in our response to Q1.\n- We agree that external validation is important. However, among publicly available datasets, emg2qwerty is the only resource that simultaneously offers naturalistic, continuous touch typing and the user/session scale required to study zero‑shot generalization and model personalization. Other public sEMG typing datasets either comprise isolated, metronome‑paced keypresses with small cohorts (19 users, two sessions) [1] or target password‑specific security settings (37 participants) [2], which do not reflect naturalistic typing or provide sufficient diversity for large‑scale cross‑user generalization experiments.\n\n[1] Eby et al., Electromyographic typing gesture classification dataset for neurotechnological human-machine interfaces.\n\n[2] Gazzari et al., My(o) Armband Leaks Passwords: An EMG and IMU Based Keylogging Side-Channel Attack\n\n**W2:** *The paper did not provide a sensitivity analysis of masking ratios, and zero sample generalization experiments were conducted with different masking ratios (30%/50%/70%)*\n\n- We have now performed sensitivity analyses for the masking ratios and will add further sweeps to the final paper. We would like to clarify that zero-shot generalization experiments were not conducted with different masking ratios unless otherwise noted (e.g. models with ACM have a different masking ratio than those without it, but otherwise all of our models have the same masking ratios).\n- For our new experiments, we trained SplashNet-mini models with maximum frequency mask width set to 8 and 16 (the default value with ACM was 12, and the default value without ACM was 4). We observe nonsignificant differences in CER, indicating that the model is not particularly dependent on the strength of ACM within this range. We will further sweep mask width on all of our main models (RSG+RTN+ACM, SplashNet-mini, SplashNet) for the final manuscript.\n- Results in the zero-shot setting:\n|  | Other domain val | Test domain val | Test domain test |\n| --- | --- | --- | --- |\n|SplashNet-Mini|58.20 ± 10.50| 36.46 ± 7.09| 36.41 ± 7.30|\n|+ mask width = 8|58.12 ± 11.72| 36.57 ± 7.02| 36.05 ± 6.74|\n|+ mask width = 16|57.65 ± 10.47| 36.66 ± 7.21| 36.92 ± 7.4|\n- Results in the finetuned setting:\n|  | Test domain val | Test domain test |\n| --- | --- | --- |\n| SplashNet-Mini|6.13 ± 2.96|5.87 ± 3.04|\n|+ mask width = 8|5.85 ± 2.72|6.02 ± 3.11|\n|+ mask width = 16|6.09 ± 2.88|5.72 ± 2.96|\n\n**Q1:** *Why were the 4 low-session users selected for \"other domain validation\"? Could this choice favor models overfit to noisy data? Providing results on the official validation set alongside Table 1 would clarify generalization.*\n\n- We would like to clarify some potential misunderstandings. Neither us nor Sivakumar et al. 2024 use these 4 users for model training: Sivakumar et al. 2024 doesn’t use them at all, whereas we use them as the “other domain validation set”. Hence, our models cannot “overfit” to these particular users in the sense of learning user-specific strategies during training. It is possible that the other domain validation set might select for models with higher performance for these noisy held-out users, potentially at the cost of performance on the cleaner 8 test users. However, we empirically observe that performance on the other domain validation set mostly tracks with performance on the test set users (Table 1).\n- The same cannot be said for the training domain validation set recommended by Sivakumar et al. 2024. Though running this evaluation with LM beam search is time intensive (requiring ~20 hours per model due to the non-parallelizability of beam search and the size of the training domain validation set), we will provide these results in the final paper. For now, we provide the results with greedy decoding (no LM) below, alongside the results in our current Table 3. We will include results with beam search in both Table 1 and Table 3 in our revised draft.\n| |Train domain val (greedy)|Other domain val (greedy)|Other domain val (beam)|Test domain val (greedy)|Test domain val (beam)|Test domain test (greedy)|Test domain test (beam)\n|-|-|-|-|-|-|-|-|\n|Sivakumar et al. 2024|22.51|72.44|72.07|55.57|52.10|55.38|51.78\n|+ RSG|23.59|68.55|67.48|52.24|47.26|52.27|47.18\n|&nbsp;+ RTN|23.49|64.19|61.95|46.31|39.49|46.07|39.15\n|&nbsp;+ ACM|34.77|65.60|63.08|48.87|42.62|49.23|42.62\n|&nbsp;+ RTN + ACM|32.85|61.82|58.85|43.72|36.41|43.80|36.42\n|&nbsp;&nbsp;+ Split|35.59|61.74|58.64|45.73|37.28|45.69|37.37\n|&nbsp;&nbsp;&nbsp;+ Share (SplashNet-mini)|37.72|61.07|58.20|45.33|36.46|45.26|36.41\n|&nbsp;&nbsp;&nbsp;&nbsp;+ Upscale (SplashNet)|33.57|60.16|56.95|44.79|35.49|44.78|35.67\n- We can clearly see that the official training domain validation set is remarkably misleading if one’s goal is to transfer to new users. Though our methods show the best performance on the other domain validation set and the test domain sets (i.e. the only sets consisting of held-out users), we get poor performance on the training domain validation set. In particular, RTN and ACM, methods which led to unambiguous, statistically significant gains in zero-shot performance on the held-out user sets, produce either no effect or harm performance according to the training domain validation set.\n- We would also like to note that most other papers on cross-user EMG generalization (e.g., emg2pose and CTRL Labs et al. 2024) use held-out users for validation, whereas emg2qwerty makes the unusual choice to use the same users for both training and validation. Training domain validation performance has little practical value—even Sivakumar et al. 2024 did not report it—since the goal of generic models is transfer to new users, not optimization for trained ones. We are happy to clarify this in the paper.\n- We agree that using only 4 users with noisy data for validation is not ideal, regardless of its superiority to a training domain validation set. We made this choice as it was the only option that allowed us to have a held-out user validation set while also maintaining the official train and test sets from Sivakumar et al. 2024, making it easier to compare our results to theirs. However, to address concerns regarding the other domain validation set, we will repeat our main experiments using a new dataset split, where we will use 80% of the official training users for training, 20% of these users for validation, and the same 8 users as Sivakumar et al. 2024 for testing (the “other domain validation set” users will be excluded completely as in Sivakumar et al. 2024). Given the large and consistent improvements our methods show compared to Sivakumar et al. 2024 on all held-out test users (Figure 4), we believe the present results are robust, and we do not anticipate any noteworthy differences when using a new split.\n\n**Q2:** *Does RTNs online normalization enhance ACMs feature-subset regularization? Please provide an analysis like mutual information between masked features which could disentangle their interaction.*\n\n- RTN’s online normalization likely does work synergistically with ACM. In particular, RTN makes the 0 value used for masking more meaningful: for any participant, it is the mean value that any channel occupies within a given session. On the other hand, when using the default batch normalization of Sivakumar et al. 2024, a value of 0 could be far out of distribution for any given user. We will make this point explicit in our revised draft. We will also include a new ablation in which we use the batch normalization of Sivakumar et al. 2024, but with the masking value set to the mean value occupied by a channel within a given session. In principle, this should disentangle whether the within-user mask value stabilization that RTN induces is itself helpful for improved decoding when separated from the more general within-session normalization provided by RTN.\n- We favor this analysis over a mutual information based analysis, since reliable estimation of high-dimensional mutual information is itself an area of research, and can be significantly affected by hyperparameters including distribution discretization. If our analysis does not satisfy your concern, we are happy to further discuss to be sure we fully address this concern. Thank you!\n\n**Q3:** *The paper assumes that \"low order feature similarity\" is the direct reason for ACM to improve generalization, but lacks cross user similarity experiments on feature subsets*\n\n- We agree and will revise the manuscript to state low‑order cross‑user feature similarity as a hypothesis, not a demonstrated mechanism. Our scope is improving EMG keystroke decoding, and our empirical result is that ACM consistently improves generalization to unseen users. ACM randomly masks channels/electrodes during training, which discourages reliance on user‑specific, high‑order conjunctions and encourages solutions built from lower‑order feature combinations. We agree that this inductive bias is compatible with, but does not prove, greater cross‑user similarity within feature subsets. We will label this as indirect evidence, temper the language accordingly, and add a limitation noting that validating the mechanism would require further cross-user similarity experiments, which we leave for future work."}, {"review_id": "Vb9bI8zqdv", "reviewer": "Reviewer_zecG", "summary": "The paper proposes three simple but effective improvements on emg2qwerty that demonstrate a better zero-shot and fine-tuning performance on sEMG to typing decodings. The proposed three improvements are well motivated, including the per-feature normalisation across time, aggressive channel masking for data augmentation, and shared weights when processing signals from left and right hands. With the three improvements, the model substantially reduces the character error rate for both zero-shot and fine-tuning tasks and reduces computational costs as well. This demonstrates its potential for advancing EMG to typing neuromotor interfaces in AR/VR applications.", "questions": "1. As shown in table one and table two, the three major contributions are tested on top of the reduced spectral granularity. Wondering the performance if keeping the high spectral resolution but applying aggressive channel masking. Will the fine-grained aggressive masking produce different results?\n2. Is it possible to keep both low-order features that are more generalisable across subjects as well as high-order features that are more reliable during each subject’s fine tuning, e.g., by using learnable masks/frequency features?\n3. I wondered what the feature space looks like after applying the three improvements. Are the features more overlapped across subjects when visualising using clustering methods/t-SNE/UMAP?\n4. Nit: line 78: misspelling of aggressive", "limitations": "As the authors mentioned in the discussion, the proposed method has only been validated on data from healthy subjects. The other point the authors might want to discuss is the potential applications in cross-session performance especially when the band is put on and off with electrode shifts.", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 4, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths\n1. The three key contributions of this work are reasonably proposed given observations and learnings from the sota model. All of them are solid with clear motivations.\n2. The ideas are well presented.\n3. The authors did comprehensive experiments to validate the effectiveness of the three contributions.\n\nWeaknesses\n\nBarely found any weaknesses from the perspective of soundness, presentation, and contribution. Please see the open questions below.", "was_revised": true, "final_justification": "Thanks for the response. My critiques have been well addressed. Looking forward to the analysis and the future work.", "rebuttal": "We thank Reviewer zecG for their enthusiastic feedback on our solid motivations, clear presentation, and comprehensive experiments, as well as their thoughtful questions on spectral granularity, learnable masking, and feature visualization. In our rebuttal, we address these by proposing learnable masks as a future extension, while committing to add clustering visualizations and masking without reduced spectral granularity in the revised draft.\n\n**Q1:** *As shown in table one and table two, the three major contributions are tested on top of the reduced spectral granularity. Wondering the performance if keeping the high spectral resolution but applying aggressive channel masking. Will the fine-grained aggressive masking produce different results?*\n\n- We switched to RSG early on because the frequency bins used in the original model were considerably more granular than is standard for sEMG. For example, CTRL-Labs et al. 2024 used features in the same six frequency bands that we use with RSG. When we saw that performance didn’t decrease and in fact slightly improved, we decided to keep this change for the rest of our experiments, as it also reduced training time, parameter count, and FLOPs. It is possible that ACM might produce different results without RSG, and we will include results with ACM without RSG in the revised draft. Nonetheless, we generally believe RSG is an improvement over the highly granular spectrogram of Sivakumar et al. 2024.\n\n**Q2:** *Is it possible to keep both low-order features that are more generalisable across subjects as well as high-order features that are more reliable during each subject’s fine tuning, e.g., by using learnable masks/frequency features?*\n\n- It might be possible to learn such feature masks, and this is an interesting direction for future development. We will include this as a future direction in our discussion.\n\n**Q3:** *I wondered what the feature space looks like after applying the three improvements. Are the features more overlapped across subjects when visualising using clustering methods/t-SNE/UMAP?*\n\n- We think this could make for an insightful analysis, similar to what Ctrl-Labs et al. 2024 provided in Fig 4 of their work, and we would be happy to include such visualizations in our revised draft.\n\n**Q4:** *Nit: line 78: misspelling of aggressive*\n\n- Thank you for pointing this out. We have now corrected this spelling error.\n\n**L1:** *As the authors mentioned in the discussion, the proposed method has only been validated on data from healthy subjects. The other point the authors might want to discuss is the potential applications in cross-session performance especially when the band is put on and off with electrode shifts.*\n\n- We have now included a more explicit discussion of what our approach means for cross-session performance. In particular, the emg2qwerty dataset examines cross-session performance, both in the zero-shot and finetuned settings. That is, the data used for validation and testing all comes from different sessions (and in the zero-shot setting, new users), between which the participants doffed and donned the EMG wristbands (as per the description in the emg2qwerty paper). Hence, our results directly reflect how models might perform in new sessions when the band is put on and off."}, {"review_id": "FmrbxqNNWl", "reviewer": "Reviewer_zN88", "summary": "This paper addresses the poor cross-user generalization and high error rates in surface electromyography based typing systems. The authors identify three primary issues in the existing emg2qwerty baseline: mismatched signal statistics across users, a fragile dependency on high-order feature combinations, and the lack of an architectural design that reflects the bilateral nature of typing. To solve these problems, the paper introduces three modifications: Rolling Time Normalization to adaptively align input signal distributions; Aggressive Channel Masking, a data augmentation technique that encourages the model to rely on more robust and generalizable low-order features ; and a Split-and-Share encoder architecture that processes signals from each hand independently with weight-shared streams. The resulting model, SplashNet, establishes a new state-of-the-art, reducing the zero-shot character error rate by a relative 31% to 35.7% and the fine-tuned CER by a relative 21% to 5.5%. Notably, these improvements are achieved with greater efficiency, as the compact SplashNet-mini model uses only one-quarter of the parameters and 60% of the FLOPs of the baseline.", "questions": "The paper attributes its success to specific signal processing choices, including adopted frequency bands and a powerful normalization method. However, since these were evaluated on a validation set described as \"notably noisier,\" it is necessary to clarify whether the performance gains reflect a fundamentally better approach or if they are an artifact of the proposed methods being uniquely effective on this non-standard, noisy data.", "limitations": "**Limitations**\n1. The state-of-the-art results reported in the main text are achieved using beam-search decoding with an external 6-gram language model. The appendix, however, shows that under greedy decoding (without a language model), the performance of the proposed models can be inferior to the baseline, especially after fine-tuning. This suggests that the core model's intrinsic ability to discriminate signals may be reduced, and the system depends heavily on the language model to achieve its top performance.\n2. While the authors' critique of the original validation set is valid, their proposed \"other domain validation set\" is built from only four participants. The paper also states these participants exhibit \"notably noisier EMG data\". A validation set that is both small and potentially unrepresentative in its data quality may not provide a stable foundation for model selection, raising questions about the generalizability of the reported validation performance.\n3. The paper concludes that unsharing the encoder weights during the fine-tuning phase offers no performance benefit, suggesting that hand-specific adapted weights are unnecessary. This conclusion is drawn from experiments with a limited amount of user-specific data. It is plausible that with more extensive fine-tuning data, allowing the model to adapt to subtle neuromuscular asymmetries between a user's left and right hands could yield further gains. Thus, the claim that this strategy is broadly unnecessary may be an overstatement based on the current evidence.", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 3, "originality": 3, "strengths_and_weaknesses": "**Strengths**\n1. The research begins by conducting a precise diagnosis of the specific weaknesses in the prior state-of-the-art model. This analytical approach ensures that the proposed solutions are well-motivated and directly target the root causes of poor performance.\n2. The proposed modifications are computationally inexpensive and grounded in domain knowledge of signal processing and biomechanics. The Split-and-Share architecture, in particular, effectively incorporates an inductive bias for the bilateral symmetry of typing , which not only improves accuracy but also significantly reduces the model's parameter count and computational complexity.", "was_revised": true, "final_justification": "The authors have addressed most of my concerns. Therefore, I decided to raise my score to 5.", "rebuttal": "We appreciate Reviewer zN88's praise for our targeted diagnosis of baseline weaknesses and efficient, biomechanically grounded solutions, alongside their valid concerns about validation stability and decoding dependencies. Our rebuttal clarifies the generalizability of our gains and commits to new dataset splits and expanded discussion of tradeoffs.\n\n**Q1:** *The paper attributes its success to specific signal processing choices, including adopted frequency bands and a powerful normalization method. However, since these were evaluated on a validation set described as \"notably noisier,\" it is necessary to clarify whether the performance gains reflect a fundamentally better approach or if they are an artifact of the proposed methods being uniquely effective on this non-standard, noisy data.*\n\n- Thank you for raising this point. We wish to clarify that the state-of-the-art performance we achieve for all 8 held-out test users (Figure 4), all of whom have standard-quality data, is strong evidence that our performance gains reflect a fundamentally better approach that is not “uniquely effective on this non-standard, noisy data”. In principle, it is possible that using the other domain validation set might select for models that retain higher performance for the 4 noisy validation users, potentially at the cost of performance on the cleaner 8 held-out test users. However, we empirically observe that performance on the other domain validation set largely tracks with performance on the test set users. Nonetheless, we will run experiments with a new train/val/test split to address concerns regarding the other domain validation set, which we address further in L2.\n\n**L1:** *The state-of-the-art results reported in the main text are achieved using beam-search decoding with an external 6-gram language model. The appendix, however, shows that under greedy decoding (without a language model), the performance of the proposed models can be inferior to the baseline, especially after fine-tuning. This suggests that the core model's intrinsic ability to discriminate signals may be reduced, and the system depends heavily on the language model to achieve its top performance.*\n\n- We agree that this is an important limitation of our work, and though we address it as such in our discussion (and further in the appendix), we will expand on the trade-offs involved in the results and discussion sections of our revised draft, as we believe it provides important insights into how our models work. As emphasized in Sections 5.1,  5.2, and Appendix  A.3, our primary evaluation metric is beam-search decoding with a 6-gram character LM, which we view as the most realistic configuration for practical deployment. This mirrors standard practice in CTC-based speech pipelines, where an external LM adds negligible compute yet is essential for disambiguating confusable outputs in noisy, real-time settings such as EMG-based typing. Greedy decoding, while simpler, is not representative of assistive-technology or AR/VR interfaces, which can accommodate a small-beam search without violating latency budgets but still demand high accuracy. In precisely this realistic setup our Split-and-Share models achieve state-of-the-art performance, yielding relative CER reductions of 31% in the zero-shot regime and 21% after fine-tuning versus the baseline of Sivakumar et al. 2024.\n- We note two settings where beam search and greedy decoding results diverge. First, when no LM is used, the Split-and-Share models generally perform worse than the Joint-Hand models. However, when an LM is used, the Split-and-Share models generally outperform the Joint-Hand models. Please note the same LM is used for all models. These results therefore support the view that the Split-and-Share models better discriminate aspects of the EMG signal that are complementary to an LM prior, whereas the Joint-Hand models better discriminate aspects of the EMG signal which are redundant to an LM prior. Importantly, this interpretation is consistent with the inductive biases allotted by the two architectures: Co-articulatory activations between hands processed by the Joint-Hand model are likely to correlate with n-grams in the text, and hence are somewhat redundant with an n-gram LM. The Split-and-Share model cannot learn such co-articulatory patterns, and hence must learn other aspects of the EMG signal, such as the within-hand mapping from EMG signals to keystroke movements, which in turn are less redundant with an n-gram LM. We will incorporate this into the discussion.\n- The second setting where beam search and greedy decoding results diverge is in the fine-tuned case, where we observe that increased channel masking causes a decrease in fine-tuned model performance without an LM. Because all of our models have reduced spectral granularity, and reduced spectral granularity implicitly increases channel masking even before ACM, we observe that all of our models perform worse than the baseline from Sivakumar et al. 2024 in the fine-tuned case when using greedy decoding. Though we speculated in the previous draft that this was due to the increased channel masking (rather than the reduced spectral granularity itself), we now provide an experiment confirming this. Specifically, we trained a model similar to our +RSG model, but apply SpecAugment on the 33-bin spectrograms from each channel before the bin aggregation of RSG (rather than after). This keeps the extent of masking equivalent to that of Sivakumar et al. 2024 while also allowing us to use the RSG frontend. This model does not perform significantly worse than the baseline of Sivakumar et al. 2024 with greedy decoding, confirming our suspicion that the core reason for the worse greedy decoding results in the finetuned case is increased channel masking. This aligns with our statement that “aggressive masking of input channels trades cross-user generalization for within-user discriminability, a trade-off that RTN and beam-search decoding can effectively offset.” We will further highlight these points in the main text of our revised manuscript.\n|  | Test domain val (greedy) | Test domain test (greedy)|\n| --- | --- | --- |\n| Sivakumar 2024 | 11.39 ± 4.28 | 11.28 ± 4.45 |\n| + RSG | 12.11 ± 4.67 | 12.50 ± 4.97 |\n| + RSG w/ pre-aggregation masking | 11.02 ± 4.32 | 11.53 ± 4.70 |\n- In summary, our models' design choices enhance performance under practical conditions, and we appreciate this feedback for deepening our analysis.\n\n**L2:** *While the authors' critique of the original validation set is valid, their proposed \"other domain validation set\" is built from only four participants. The paper also states these participants exhibit \"notably noisier EMG data\". A validation set that is both small and potentially unrepresentative in its data quality may not provide a stable foundation for model selection, raising questions about the generalizability of the reported validation performance.*\n\n- The generalizability of the reported validation performance is evident in the state-of-the-art results we achieve on the 8 held-out test users with standard-quality data. Nonetheless, we agree that using only 4 users with noisy data as a validation set is far from ideal, regardless of its superiority to a training domain validation set. We made this choice as it was the only option that allowed us to have a held-out user validation set while also maintaining the official train and test sets from Sivakumar et al. 2024, making it easier to compare our results to theirs. However, to address concerns regarding the other domain validation set, we will repeat our main experiments using a new dataset split, where we will use 80% of the official training users for training, 20% of these users for validation, and the same 8 users as Sivakumar et al. 2024 for testing (the “other domain validation set” users will be excluded completely as in Sivakumar et al. 2024). Given the large and consistent improvements our methods show compared to Sivakumar et al. 2024 on all held-out test users (Figure 4), we believe the present results are robust, and we do not anticipate any noteworthy differences when using a new split.\n\n**L3:** *The paper concludes that unsharing the encoder weights during the fine-tuning phase offers no performance benefit, suggesting that hand-specific adapted weights are unnecessary. This conclusion is drawn from experiments with a limited amount of user-specific data. It is plausible that with more extensive fine-tuning data, allowing the model to adapt to subtle neuromuscular asymmetries between a user's left and right hands could yield further gains. Thus, the claim that this strategy is broadly unnecessary may be an overstatement based on the current evidence.*\n\n- We agree that per-hand weights might become advantageous with more user-specific data, and will make this explicit in our revised draft."}]

Author rebuttal:

Meta-review: This work offers creative and well executed ML contributions to the problem of surface typing from multivariate EMG signals in the context of neuromotor interfaces. The gains on the SoTA are significant and all reviewers endorse this work for publication at NeurIPS. This work is therefore recommended for acceptance.
Decision: Accept (poster)