EMG-FM-Bench: A Comprehensive Benchmark for Foundation Model Transfer and Adaptation on Electromyography

Tianhao Wu; Xu Wu; Amirmohammad Radmehr; Jiawei Yu; Yi Wu; Phuc Nguyen; Jian Liu

cs.LGOct 5, 2026

EMG-FM-Bench: A Comprehensive Benchmark for Foundation Model Transfer and Adaptation on Electromyography

Authors: Tianhao Wu, Xu Wu, Amirmohammad Radmehr, Jiawei Yu, Yi Wu, Phuc Nguyen, Jian Liu

Organizations: University of Georgia · University of Oklahoma · University of Massachusetts Amherst

Abstract

Foundation models (FMs) are increasingly being developed for general time series and physiological signals, yet their transferability to downstream physiological tasks remains poorly understood. This question is particularly challenging for electromyography (EMG), where signal distributions vary substantially across users, sensing configurations, acquisition hardware, and downstream tasks. We introduce EMG-FM-Bench, a systematic benchmark for studying foundation-model transfer and adaptation on EMG. EMG-FM-Bench unifies 20 public datasets with over 1 million EMG segments and evaluates nine pretrained foundation models across four questions: how pretrained models perform when frozen or fully fine-tuned, how much pretraining helps compared with training the same model from scratch, how well models generalize to new users with limited labeled data, and how performance changes across different EMG tasks. Across the benchmark, linear probing provides useful information about pretrained representations, but full fine-tuning can substantially change downstream EMG performance. Comparing each pretrained model with the same model trained from scratch shows that the benefit of pretraining varies substantially across models and is not universal. Performance decreases when models are evaluated on new users, while five-shot adaptation improves macro-F1 in 70.2% of evaluated model-dataset combinations but recovers only part of the lost performance. Model performance is highly consistent between upper- and lower-limb classification and remains strongly correlated with continuous EMG-to-text decoding. Together, these results provide a systematic view of when pretrained time-series models transfer effectively to EMG and how their performance depends on fine-tuning, user variation, and downstream task.

Figures & tables

Figure 1: EMG-FM-Bench evaluates nine pretrained models on 20 heterogeneous EMG datasets under four research questions (RQ1–RQ4).

Category	# Data	# Subj.	# Windows	Benchmark Task
Upper limb	15	489	574,182	Gesture/pose classification
Lower limb	4	156	54,953	Locomotion classification
Typing	1	108	402,119	Character sequence decoding
Total	20	753	1,031,254	Classification and Seq2Seq

Table 1: Scale and task coverage of EMG-FM-Bench . Dataset-level statistics are provided in Appendix B .

Model	Architecture	Pretraining Domain	Params.
MOMENT-S/B [ 13 ]	Encoder-only Transformer	Heterogeneous time series	40M / 125M
Chronos-2-S/B [ 14 ]	Encoder-only Transformer	General forecasting data	28M / 120M
TimesFM [ 20 ]	Decoder-only Transformer	General time series	200M
Lag-Llama [ 22 ]	Decoder-only Transformer	General time series	2.45M
PatchTST [ 56 ]	Encoder-only Transformer	General time series	$\sim$ 1M
UniTS [ 57 ]	Modified Transformer	Heterogeneous time series	3.4M

Table 2: Pretrained model families evaluated in EMG-FM-Bench . Models span different Transformer architectures, pretraining domains, and parameter scales. Parameter counts correspond to the evaluated checkpoints.

Model	LP	FFT	Scratch	Held-out	LOSO	5-shot (F1)
MOMENT-S	41.9 (2.5)	60.1 (2.1)	57.4 (2.1)	39.0 (3.3)	34.9 (3.8)	42.7 (2.6)
MOMENT-B	52.2 (1.2)	46.8 (4.4)	57.3 (2.1)	28.1 (5.7)	35.6 (3.9)	40.0 (3.5)
BRANT	18.2 (7.5)	22.8 (8.0)	20.8 (8.5)	19.8 (7.6)	23.0 (6.9)	12.5 (8.3)
PatchTST	20.2 (7.1)	30.1 (7.4)	30.7 (6.9)	27.2 (5.6)	19.6 (8.6)	16.3 (7.1)
TimesFM	29.4 (5.2)	37.9 (5.9)	31.6 (6.8)	31.1 (5.0)	34.2 (4.6)	27.6 (5.3)
Chronos-2-S	28.1 (4.6)	54.3 (3.1)	38.2 (4.8)	41.6 (3.2)	36.7 (3.6)	44.0 (2.9)

Table 3: Aggregate results across 19 classification datasets. Each cell reports the mean score with mean rank in parentheses; lower rank is better. The 5-shot column reports macro-F1 averaged over five seeds; all other columns report accuracy.

Model	Mean $\Delta$ Acc	Improved (F1)
MOMENT-S	+18.2	18/19
MOMENT-B	-5.4	6/19
BRANT	+4.6	14/19
PatchTST	+9.9	18/19
TimesFM	+8.5	15/19
Chronos-2-S	+26.2	19/19

Table 4: Effect of full fine-tuning relative to linear probing across the 19 classification datasets.

\Delta

Acc denotes

\mathrm{Acc}_{\mathrm{FFT}}-\mathrm{Acc}_{\mathrm{LP}}

Figure 2: Accuracy difference between pretrained full fine-tuning and training the same model from scratch across 19 classification datasets. Positive values indicate a benefit from pretraining.

Model	$\Delta_{\mathrm{New}}$	Drop	$\Delta_{\mathrm{5shot}}$	Gain
MOMENT-S	-27.1	18/19	+11.9	16/19
MOMENT-B	-22.4	18/19	+18.3	19/19
BRANT	-2.5	13/19	+1.2	13/19
PatchTST	-6.3	15/19	-3.5	6/19
TimesFM	-10.4	15/19	+4.2	13/19
Chronos-2-S	-19.1	18/19	+10.9	15/19

Table 5: Model-level new-user degradation and five-shot recovery across 19 classification datasets (macro-F1).

\Delta_{\mathrm{New}}=\mathrm{F1}_{\mathrm{Held\text{-}out}}-\mathrm{F1}_{\mathrm{FFT}}

and

\Delta_{\mathrm{5shot}}=\mathrm{F1}_{\mathrm{5shot}}-\mathrm{F1}_{\mathrm{Held\text{-}out}}

, where

\mathrm{F1}_{\mathrm{5shot}}

is averaged over five seeds. Drop and Gain denote the number of datasets with decreased held-out macro-F1 and improved five-shot macro-F1, respectively.

Dataset	MOMENT-S	MOMENT-B	BRANT	PatchTST	TimesFM	Chronos-2-S	Chronos-2-B	Lag-Llama	UniTS
emg2qwerty	26.8	24.4	54.9	46.8	51.8	24.0	19.7	38.0	29.8

Table 6: Continuous typing decoding performance on the emg2qwerty dataset. All values represent Character Error Rate (CER, %). Lower is better.

Appendix figures & tables8 assets

Supplementary material from the paper’s appendix.

Appendix

Dataset	MOMENT-S	MOMENT-B	BRANT	PatchTST	TimesFM	Chronos-2-S	Chronos-2-B	Lag-Llama	UniTS
NinaPro DB1	43.6/48.1	32.5/35.8	22.0/24.1	22.2/25.5	38.9/39.6	42.5/46.6	44.8/49.0	17.5/12.5	36.1/39.0
NinaPro DB2	55.4/55.2	26.1/25.6	3.5/1.0	6.8/6.2	17.4/16.4	56.6/56.7	49.2/49.3	2.4/0.2	12.8/11.6
NinaPro DB3	28.1/28.1	13.0/12.0	3.7/0.6	2.1/2.1	3.7/2.3	22.9/22.0	21.7/20.9	2.7/0.4	5.6/3.2
NinaPro DB4	44.3/45.5	23.7/22.5	4.0/0.5	9.5/8.2	9.2/8.0	34.1/35.5	40.5/43.1	4.5/0.4	12.6/11.5
NinaPro DB5	28.6/29.0	11.7/10.8	6.7/1.0	6.1/5.3	10.5/8.7	23.0/23.3	22.2/22.5	6.4/1.2	10.8/7.8
NinaPro DB6	51.0/53.1	38.2/38.3	15.7/9.9	21.4/21.4	43.0/32.4	58.9 /59.1	58.3/ 59.9	56.4/58.2	23.0/19.6

Appendix

Table 7: FFT performance across all 19 benchmark datasets. All values reported as Acc./F1 (%).

Dataset	MOMENT-S	MOMENT-B	BRANT	PatchTST	TimesFM	Chronos-2-S	Chronos-2-B	Lag-Llama	UniTS
NinaPro DB1	26.5/26.8	32.0/35.5	11.9/8.9	9.7/7.8	6.3/4.3	16.6/18.1	16.8/17.7	15.7/11.3	10.8/5.5
NinaPro DB2	20.5/20.6	33.0/31.9	0.9/0.0	2.3/1.0	5.7/5.1	8.2/6.2	8.2/6.4	1.7/0.1	2.5/0.8
NinaPro DB3	8.7/8.9	19.6/19.0	0.2/0.0	2.3/0.8	2.3/1.1	3.5/2.0	3.9/2.0	2.9/0.5	2.5/1.3
NinaPro DB4	23.0/19.5	33.4/34.6	5.2/0.8	5.5/4.0	5.7/2.9	7.1/3.2	10.4/5.8	4.3/0.7	3.8/1.2
NinaPro DB5	14.9/10.2	22.2/21.5	7.0/1.1	5.5/1.1	7.0/3.1	8.5/3.7	8.2/3.7	5.3/1.1	6.4/1.1
NinaPro DB6	36.3/36.6	44.4/46.3	5.0/1.4	6.8/3.9	35.9/31.0	19.0/17.7	21.3/19.6	12.9/6.6	35.3/30.7

Appendix

Table 8: Complete Linear Probe Performance across all 19 datasets. All values reported as Acc./F1 (%).

Dataset	MOMENT-S	MOMENT-B	BRANT	PatchTST	TimesFM	Chronos-2-S	Chronos-2-B	Lag-Llama	UniTS	BiLSTM
NinaPro DB1	41.1/45.1	41.6/45.4	20.0/19.3	23.3/25.6	47.4/50.7	25.1/29.0	29.0/32.3	24.3/27.0	28.6/32.1	38.9/41.7
NinaPro DB2	44.6/45.4	52.4/52.9	1.5/0.4	8.9/8.4	8.4/8.0	7.5/5.4	17.3/16.5	25.1/25.4	8.9/8.2	9.4/9.7
NinaPro DB3	21.5/20.7	19.5/18.8	1.7/0.6	2.6/2.5	2.9/2.4	4.3/1.2	4.7/3.1	6.0/6.2	3.5/1.9	2.6/2.4
NinaPro DB4	38.7/39.4	36.3/35.0	5.7/1.2	8.2/7.8	4.5/3.6	10.0/8.5	20.9/19.7	13.7/14.3	9.0/5.7	7.7/7.1
NinaPro DB5	30.7/31.0	22.7/25.4	7.3/1.4	9.9/8.4	6.7/4.1	9.3/6.1	14.3/10.6	12.0/12.1	6.4/0.6	5.3/4.4
NinaPro DB6	46.7/48.0	46.8/48.8	7.1/4.4	25.9/25.4	23.9/24.0	33.2/34.5	38.8/40.4	35.7/36.1	43.2/44.7	17.8/17.5

Appendix

Table 9: Complete train-from-scratch performance across all benchmark datasets. Models are randomly initialized and trained using the same downstream full-training protocol as the pretrained full fine-tuning experiments. All values are reported as Acc./F1 (%).

Dataset	MOMENT-S	MOMENT-B	BRANT	PatchTST	TimesFM	Chronos-2-S	Chronos-2-B	Lag-Llama	UniTS	BiLSTM
NinaPro DB1	23.8/25.0	25.4/22.4	12.4/13.7	21.2/19.9	43.0/41.8	21.2/25.2	28.0/27.1	16.1/9.6	22.8/22.7	17.6/15.2
NinaPro DB2	26.9/23.9	18.9/17.2	1.5/0.4	8.0/4.4	13.4/9.7	27.9/25.8	25.4/24.0	3.5/0.3	12.1/8.5	11.4/10.7
NinaPro DB3	7.8/5.4	1.9/1.4	1.0/0.0	1.9/1.1	7.8/2.8	7.8/4.1	13.6/11.6	1.9/0.1	5.8/1.9	1.0/1.0
NinaPro DB4	37.2 / 30.2	9.3/6.6	2.3/0.2	7.0/3.7	4.7/4.6	27.9/20.3	37.2 /28.1	2.3/0.2	14.0/11.9	11.6/10.5
NinaPro DB5	25.0/ 26.0	9.4/4.3	6.3/0.7	6.3/3.2	12.5/7.4	31.3 /23.9	15.6/8.6	3.1/1.2	6.2/2.6	6.3/3.9
NinaPro DB6	38.0/29.1	29.5/27.4	5.5/1.5	19.0/15.8	23.9/22.4	42.3/37.5	48.5/41.7	35.6/25.8	13.4/11.1	26.4/23.7

Appendix

Table 10: Complete zero-calibration cross-subject generalization performance across all 19 benchmark classification datasets. Models trained on Pool A, evaluated on Pool B without adaptation. All values reported as Acc./F1 (%).

Dataset	MOMENT-S	MOMENT-B	BRANT	PatchTST	TimesFM	Chronos-2-S	Chronos-2-B	Lag-Llama	UniTS	BiLSTM
NinaPro DB1	$16.7\pm 3.8$	$19.0\pm 4.9$	$7.9\pm 1.5$	$6.8\pm 0.9$	$\mathbf{40.6\pm 5.3}$	$19.9\pm 3.5$	$21.3\pm 3.3$	$10.7\pm 2.0$	$13.2\pm 3.2$	$8.5\pm 3.1$
NinaPro DB2	$17.5\pm 3.5$	$\mathbf{18.1\pm 3.3}$	$7.6\pm 1.5$	$5.7\pm 0.5$	$9.3\pm 2.6$	$14.7\pm 4.2$	$15.0\pm 3.6$	$16.8\pm 4.7$	$12.7\pm 3.1$	$10.7\pm 4.9$
NinaPro DB3	$\mathbf{5.9\pm 2.5}$	$5.4\pm 3.0$	$5.8\pm 1.3$	$5.3\pm 1.2$	$3.1\pm 1.4$	$2.8\pm 1.3$	$3.5\pm 1.5$	$5.3\pm 2.5$	$5.7\pm 2.1$	$5.6\pm 1.1$
NinaPro DB4	$19.2\pm 3.3$	$18.9\pm 3.0$	$5.7\pm 0.8$	$6.2\pm 1.3$	$13.6\pm 3.5$	$23.0\pm 4.2$	$\mathbf{24.7\pm 5.5}$	$14.6\pm 2.4$	$15.5\pm 2.2$	$5.9\pm 1.3$
NinaPro DB5	$14.5\pm 3.4$	$13.8\pm 3.7$	$6.6\pm 0.8$	$6.5\pm 0.9$	$10.8\pm 1.9$	$16.2\pm 2.9$	$\mathbf{18.0\pm 3.2}$	$9.6\pm 3.1$	$12.1\pm 2.5$	$5.6\pm 1.5$
NinaPro DB6	$34.3\pm 5.6$	$33.7\pm 4.1$	$17.0\pm 1.8$	$15.3\pm 2.0$	$37.1\pm 3.0$	$42.9\pm 7.1$	$\mathbf{46.5\pm 6.7}$	$28.6\pm 3.0$	$29.1\pm 2.4$	$18.3\pm 1.7$

Appendix

Table 11: Complete cohort-restricted leave-one-subject-out (LOSO) results across all 19 classification datasets. For each dataset, one subject from the evaluation cohort is held out for testing while the remaining cohort members are used for downstream training and validation. Datasets with at most 10 subjects use all available subjects, whereas larger datasets use a fixed 10-subject cohort. Results are reported as mean accuracy

\pm

standard deviation (%) across held-out subjects.

Dataset	MOMENT-S	MOMENT-B	BRANT	PatchTST	TimesFM	Chronos-2-S	Chronos-2-B	Lag-Llama	UniTS	BiLSTM
NinaPro DB1	$\mathbf{44.0{\pm}9.9}$	$29.0{\pm}3.7$	$6.9{\pm}2.6$	$6.1{\pm}0.9$	$32.5{\pm}3.1$	$36.0{\pm}2.9$	$41.0{\pm}2.8$	$4.6{\pm}0.8$	$36.9{\pm}7.9$	$17.4{\pm}1.6$
NinaPro DB2	$35.6{\pm}4.2$	$32.3{\pm}2.9$	$1.6{\pm}0.6$	$3.7{\pm}0.8$	$10.7{\pm}1.4$	$46.3{\pm}2.1$	$\mathbf{46.6{\pm}2.1}$	$16.9{\pm}1.6$	$21.5{\pm}6.3$	$9.2{\pm}1.1$
NinaPro DB3	$22.4{\pm}2.7$	$9.4{\pm}1.0$	$0.8{\pm}0.1$	$1.8{\pm}0.4$	$6.4{\pm}0.9$	$\mathbf{24.0{\pm}2.6}$	$21.1{\pm}2.8$	$0.2{\pm}0.1$	$7.5{\pm}2.9$	$2.1{\pm}0.5$
NinaPro DB4	$26.8{\pm}3.1$	$24.6{\pm}2.6$	$0.7{\pm}0.5$	$3.3{\pm}1.3$	$10.0{\pm}3.6$	$32.0{\pm}8.5$	$\mathbf{38.8{\pm}3.6}$	$0.3{\pm}0.1$	$8.0{\pm}1.1$	$6.1{\pm}1.0$
NinaPro DB5	$13.8{\pm}2.1$	$10.8{\pm}2.3$	$0.9{\pm}0.9$	$2.8{\pm}0.7$	$4.8{\pm}1.3$	$\mathbf{17.6{\pm}5.4}$	$16.3{\pm}3.0$	$0.9{\pm}0.2$	$6.2{\pm}2.5$	$3.7{\pm}0.3$
NinaPro DB6	$40.0{\pm}1.5$	$31.1{\pm}2.1$	$11.0{\pm}2.0$	$14.2{\pm}1.2$	$37.9{\pm}3.6$	$41.0{\pm}5.1$	$\mathbf{42.1{\pm}4.8}$	$20.3{\pm}1.3$	$18.5{\pm}4.2$	$23.4{\pm}1.1$

Appendix

Table 12: Complete five-shot target-cohort adaptation results across all 19 classification datasets (macro-F1). For each of five random seeds, models are adapted using five labeled examples per class sampled from the pooled Pool B training set and evaluated on the fixed Pool B test set. Values are reported as mean

\pm

standard deviation of macro-F1 (%) across 5 random seeds (42–46).

Dataset	Sensor Location	Fs (Hz)	Channels	Task
Hand/Arm Gesture Datasets
NinaPro DB1 [ 39 ]	Forearm	100	10	Hand Gesture Classification
NinaPro DB2 [ 39 ]	Forearm	2000	12	Hand Gesture Classification
NinaPro DB3 [ 39 ]	Forearm	2000	12	Hand Gesture Classification
NinaPro DB4 [ 40 ]	Forearm	2000	12	Hand Gesture Classification
NinaPro DB5 [ 40 ]	Forearm	200	16	Hand Gesture Classification

Appendix

Table 13: Summary of EMG datasets used in this benchmark.

Model	Architecture	Pretraining Objective	Native Scope / Input	Params.
MOMENT-S/B	Encoder; patch tokens	Masked-patch reconstruction	Heterogeneous real-world numerical time series	40M / 125M
Chronos-2-S/B	Encoder; temporal and group attention	Direct quantile forecasting	Univariate and multivariate targets with optional covariates	28M / 120M
TimesFM	Decoder; patched input	Multi-horizon forecasting	Primarily univariate numerical time series	200M
Lag-Llama	Decoder; lagged covariates	Autoregressive probabilistic forecasting	Univariate series with lag and temporal covariates	2.45M
PatchTST	Encoder; channel-independent patching	Forecasting	General time series	$\sim$ 1M
BRANT	Temporal followed by spatial encoders	Masked-signal reconstruction	Multichannel iEEG/SEEG with time–frequency features	$>$ 500M

Appendix

Table 14: Summary of the seven model families and nine pretrained configurations evaluated in EMG-FM-Bench . Parameter counts correspond to the checkpoints used in our experiments.

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Electroencephalography Foundation Models Parameter-Efficient Adaptation Text-Only Adaptation Frozen Encoder Channel Modeling

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Scaling EEG foundation models requires pooling data across heterogeneous electrode montages, a prerequisite both for larger pretraining corpora and for downstream deployment. We present the first systematic comparison of four channel adaptation methods (Conv1d projection, spherical spline interpolation (SSI), source-space decomposition, and Riemannian re-centering) across five pretrained EEG foundation models (5M--157M parameters), five downstream tasks, and two training regimes with 10--15 random seeds each. We find that rigid-montage models (BENDR, Neuro-GPT) require external adaptation, while flexible models (EEGPT, CBraMod) match or exceed it natively when fine-tuned but benefit from external methods under frozen-encoder deployment. A probe-SFT asymmetry exists: external adaptation can cause severe negative transfer during fine-tuning of flexible models. The optimal method is architecture-dependent (Conv1d for BENDR, SSI/Riemannian for Neuro-GPT, source-space decomposition for depression detection), and 5M-parameter CBraMod outperforms models up to 31

\times

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Abstract

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