NeurDuo-EEG: A Long-Sequence EEG Foundation Model with Persistent State and Explicit Memory

Yifan Wang; Haiping Liu; Yang Cui; Wenhao Cai; Shuhang Li; Xiaoyang Huang; Xianyang Liu; Jingyu Sun

cs.LGSep 29, 2026

NeurDuo-EEG: A Long-Sequence EEG Foundation Model with Persistent State and Explicit Memory

Authors: Yifan Wang, Haiping Liu, Yang Cui, Wenhao Cai, Shuhang Li, Xiaoyang Huang, Xianyang Liu, Jingyu Sun, +8 more

Organizations: University of Manchester · ETH Zürich · Shanghai Jiao Tong University · Anhui University · ELLIS Institute Finland · Aalto University · Institute of Automation, Chinese Academy of Sciences · University of Chinese Academy of Sciences

Abstract

Electroencephalography (EEG) is recorded continuously over hours, with relevant dynamics spanning timescales from milliseconds to hours. Most EEG foundation models nevertheless process fixed windows independently, limiting their ability to capture information encoded in long-timescale dynamics. State-space architectures enable persistent recurrent processing, but long-range information remains implicitly compressed in recurrent states. We present NeurDuo-EEG, a causal EEG foundation model with channel-resolved persistent memory. NeurDuo-EEG introduces multi-timescale memory management with learned consolidation and selective retrieval, enabling persistent modelling of continuous EEG with fixed-size state. It is pre-trained on 3,955 hours of EEG from 17 public datasets using multichannel autoregressive prediction of discrete spectral codes. Across three short-window and two long-sequence downstream tasks, NeurDuo-EEG achieves the best performance on four of five benchmarks, including all three short-window tasks and seizure detection, where AUC-PR improves from $0.285$ to $0.471$ over the strongest non-NeurDuo baseline. NeurDuo-EEG also remains competitive on sleep staging and supports efficient streaming inference, with nearly constant per-chunk latency as the available history grows to one hour. Notably, the Small variant achieves this with only 4.7M backbone parameters. These results demonstrate the value of persistent, multi-timescale modelling for both long-sequence and short-window EEG analysis. Our code is available at https://github.com/YifaNNW/NeurDuo-EEG.

Figures & tables

Figure 1: Overview of NeurDuo and its two-stage pre-training framework. A frozen spectral tokenizer provides discrete per-channel targets. The backbone processes coordinate-aware EEG streams through fast and slow recurrent pathways, consolidates recent states into bounded memory, and retrieves relevant past information for per-channel next-token prediction.

Model	$H$	$d_{f}$	$L_{f}$	$d_{s}$	$L_{s}$	Params
Small	128	256	6	192	3	4.70M
Base	192	480	10	320	5	24.31M
Large	256	672	15	448	7	67.83M

Table 1: NeurDuo model configurations.

Methods	Model Size	FACED		KaggleERN		SEED-VIG
		Bal. Acc. $\uparrow$	Cohen’s $\kappa$ $\uparrow$	ROC-AUC $\uparrow$	AUC-PR $\uparrow$	Pearson’s $r$ $\uparrow$	$R^{2}$ $\uparrow$
BP-GBDT	–	0.173	0.070	$0.557\pm 0.005$	$0.753\pm 0.009$	0.445	0.031
FBCov-TS-Lin	–	0.185	0.084	$0.613\pm 0.023$	$0.778\pm 0.023$	0.344	0.110
ERP-Lin	–	0.229	0.130	$0.623\pm 0.019$	$0.782\pm 0.012$	0.020	$-0.019$
BIOT	3.2M	$0.364\pm 0.016$	$0.283\pm 0.017$	$0.496\pm 0.030$	$0.708\pm 0.023$	$0.535\pm 0.065$	$0.179\pm 0.136$
CBraMod	4.9M	$0.537\pm 0.008$	$0.476\pm 0.010$	$0.571\pm 0.015$	$0.755\pm 0.009$	$0.529\pm 0.038$	$0.196\pm 0.051$

Table 2: Short-window downstream performance. † LaBraM was pre-trained on KaggleERN; see Appendix D.1 .

Methods	Model Size	Sleep-EDF		CHB-MIT (15 ch)		CHB-MIT (2 ch)
		Macro-F1 $\uparrow$	Cohen’s $\kappa$ $\uparrow$	AUC-PR $\uparrow$	FP $\cdot$ h -1 $\downarrow$	AUC-PR $\uparrow$	FP $\cdot$ h -1 $\downarrow$
BP-GBDT	–	$0.710\pm 0.013$	$0.695\pm 0.021$	$0.209\pm 0.109$	$3.34$	$0.208\pm 0.057$	$3.28$
FBCov-TS-Lin	–	$0.674\pm 0.018$	$0.666\pm 0.019$	$0.285\pm 0.136$	$2.14$	$0.224\pm 0.124$	$4.12$
ERP-Lin	–	$0.306\pm 0.044$	$0.255\pm 0.031$	$0.006\pm 0.003$	$15.42$	$0.009\pm 0.007$	$12.65$
BIOT †	3.2M	$0.731\pm 0.017$	$0.733\pm 0.017$	$0.208\pm 0.059$	$4.73$	$0.124\pm 0.094$	$6.12$
CBraMod	4.9M	$\underline{0.759\pm 0.009}$	$0.749\pm 0.014$	$0.163\pm 0.085$	$4.48$	$0.277\pm 0.150$	$3.40$

Table 3: Long-sequence downstream performance. CHB-MIT is evaluated using either all

15

channels or only T7 and P8. † BIOT was pre-trained on SHHS and CHB-MIT; see Appendix D.1 .

Context	$L$	Sleep-EDF		CHB-MIT (15 ch)		CHB-MIT (2 ch)
		Macro-F1 $\uparrow$	Cohen’s $\kappa$ $\uparrow$	AUC-PR $\uparrow$	FP $\cdot$ h -1 $\downarrow$	AUC-PR $\uparrow$	FP $\cdot$ h -1 $\downarrow$
$30$ s †	1	$0.696\pm 0.010$	$0.695\pm 0.011$	$0.324\pm 0.095$	$2.02$	$0.241\pm 0.188$	$7.96$
$1$ min	2	$0.707\pm 0.009$	$0.702\pm 0.010$	$0.322\pm 0.153$	$4.12$	$0.290\pm 0.153$	$4.05$
$2$ min	4	$0.725\pm 0.009$	$0.722\pm 0.010$	$0.404\pm 0.160$	$5.83$	$0.358\pm 0.137$	$1.64$
$5$ min	10	$0.744\pm 0.015$	$0.742\pm 0.005$	$0.331\pm 0.128$	$0.80$	$0.375\pm 0.156$	$2.18$
$10$ min	20	$\underline{0.755\pm 0.008}$	$0.749\pm 0.011$	$\mathbf{0.413\pm 0.174}$	$0.96$	$0.370\pm 0.177$	$1.62$

Table 4: Effect of causal context length on NeurDuo (Small).

L

denotes the number of consecutive

30

s units; all other evaluation settings remain fixed.

{}^{\dagger}L=1

provides no cross-unit context.

Figure 2: Inference latency on CHB-MIT.

Variant	FACED		Sleep-EDF		CHB-MIT (15 ch)
	Bal. Acc. $\uparrow$	$\kappa$ $\uparrow$	Macro-F1 $\uparrow$	$\kappa$ $\uparrow$	AUC-PR $\uparrow$	FP $\cdot$ h -1 $\downarrow$
NeurDuo (Small)	$0.589\pm 0.005$	$0.534\pm 0.006$	$0.755\pm 0.008$	$0.749\pm 0.011$	$0.413\pm 0.174$	$0.96$
w/o per-channel tokens	$0.460\pm 0.011$	$0.391\pm 0.012$	$0.745\pm 0.011$	$0.739\pm 0.008$	$0.412\pm 0.096$	$1.10$
w/o channel attention	$0.523\pm 0.009$	$0.460\pm 0.011$	$0.753\pm 0.016$	$0.750\pm 0.009$	$0.340\pm 0.125$	$4.24$
w/o memory retrieval	$0.506\pm 0.012$	$0.442\pm 0.013$	$0.744\pm 0.012$	$0.736\pm 0.010$	$0.389\pm 0.112$	$1.08$
w/o slow stream & memory	$0.545\pm 0.015$	$0.485\pm 0.017$	$0.742\pm 0.017$	$0.738\pm 0.010$	$0.394\pm 0.179$	$0.47$

Table 5: Component ablations and temporal-order control for NeurDuo (Small). Shuffling is not applicable to FACED, whose samples are independent windows.

State	Sleep-EDF		CHB-MIT (15 ch)
	Macro-F1 $\uparrow$	$\kappa$ $\uparrow$	AUC-PR $\uparrow$	FP $\cdot$ h -1 $\downarrow$
Reset	$0.745\pm 0.014$	$0.738\pm 0.013$	$0.307\pm 0.191$	$5.16$
Carry	$\mathbf{0.755\pm 0.008}$	$\mathbf{0.749\pm 0.011}$	$\mathbf{0.413\pm 0.174}$	$\mathbf{0.96}$

Table 6: Persistent state in NeurDuo (Small). Backbone state is reset every 30 s or carried across a 10 min sequence, keeping the context-head architecture fixed.

Appendix figures & tables20 assets

Supplementary material from the paper’s appendix.

Appendix

Domain	Datasets	#Rec.	Hours	#Ch.	#Windows
Clinical	TUSZ, TUEP, Siena, TUAR	8,007	1,811.9	17–19	395,424
Sleep (PSG)	PhysioNet-2018, ISRUC	11,339	1,882.3	6	406,517
Motor	EEGMMIDB, HGD, GAL, BCI IV-1	1,821	95.5	32–126	18,415
Attention	Cao 2019	516	81.9	30	17,652
Emotion	DEAP, DREAMER	1,694	46.2	14–32	7,367
Speech (auditory)	Broderick	380	20.6	128	4,056

Appendix

Table 7: Pre-training corpus by domain. All counts are after preprocessing. #Rec. is the number of recordings and Hours their total duration; #Ch. is the electrodes retained once channel labels are resolved to standard positions, given as a range where a domain’s corpora use different montages; #Windows is the number of

32

s pre-training windows the recordings are cut into, at

50\%

overlap. Table 8 gives the per-dataset breakdown.

Dataset	Domain	#Rec.	Hours	#Ch.	#Windows
PhysioNet-2018 ( Ghassemi et al., 2018 ; Goldberger et al., 2000 )	Sleep (PSG)	6,830	1,138.3	6	245,880
TUSZ ( Shah et al., 2018 )	Clinical	4,959	911.2	17–19	197,388
ISRUC ( Khalighi et al., 2016 )	Sleep (PSG)	4,509	744.0	6	160,637
TUEP ( Veloso et al., 2017 )	Clinical	2,697	631.7	17–19	138,035
Siena ( Detti et al., 2020 )	Clinical	41	169.0	19	37,963
TUAR ( Hamid et al., 2020 )	Clinical	310	100.0	19	22,038

Appendix

Table 8: Pre-training corpus, per dataset, after preprocessing. The per-dataset expansion of Table 7 , ordered by hours. #Rec. is the number of recordings and Hours their total duration; #Ch. is the electrodes retained once channel labels are resolved to standard positions; #Windows is the number of

32

s pre-training windows the recordings are cut into, at

50\%

overlap. Where a channel count differs from the one quoted in a release’s own publication, the table reports the signal channels the released files contain.

	Hyperparameter	Stage 1 (tokenizer)	Stage 2 (backbone)
Data	Chunk	$128$ samples ( $0.5$ s), non-overlapping
	Sequence length	$64$ chunks ( $32$ s)	$64$ chunks ( $32$ s)
	Sequence stride	$64$ (no overlap)	$32$ ( $50\%$ overlap)
Optimisation	Optimiser	AdamW	AdamW
	Peak learning rate	$5\times 10^{-5}$	$3.0/2.2/1.9\times 10^{-4}$ (S/B/L)
	Weight decay	$10^{-4}$	$0.05$

Appendix

Table 9: Pre-training hyperparameters. Stage 1 trains the spectral tokenizer and stage 2 the backbone, with the tokenizer frozen between them. A value spanning both columns is shared by the two stages; the Tokenizer block describes the stage-1 model itself and so is not stage-dependent. Every entry is identical across the three model sizes except the peak learning rate, given as S/B/L. “–” marks a setting that does not apply to that stage.

	Hyperparameter	Value
Frontend	Chunk encoder kernels	$\{3,7,15\}$ , three parallel branches
	Metadata encoder width	$64$
	Channel attention	every $2$ fast layers, $4$ heads
Streams	SSM state dimension	$16$ (fast and slow)
	SSM expansion factor	$2$ (fast and slow)
	Causal convolution kernel	$4$ (fast), $3$ (slow)

Appendix

Table 10: Architecture constants. Identical in all three model sizes; the widths and depths that scale are given in Table 1 . One step is one

0.5

s chunk, so the durations in parentheses follow from the step counts beside them.

Figure 3: Pre-training curves for the three sizes of Table 1 , on the held-out recordings. Small and Base reach their minima as the schedule ends, at

6.0503

and

6.0272

nats; Large reaches its minimum of

6.0491

after

51{,}000

updates and then rises to

6.0715

Field	Used	Reason
Electrode coordinates	✓	Physical position; the only cue distinguishing channels.
Reference scheme	✓	Eight values across the pre-training corpus, reflecting how each recording was referenced.
Channel type	—	Constant: all $438{,}323$ channels share one value.
Sampling rate	—	Constant: every recording is resampled to $256$ Hz.
Device identifier	—	Near-constant: $22{,}740$ of $22{,}780$ recordings share one value.
Corpus identifier	—	Informative but unavailable at transfer time.

Appendix

Table 11: Metadata fields available in the pipeline and their disposition.

	Absolute CE ( $\downarrow$ )		Improvement over identity ( $\uparrow$ ) for $K=$
Predict ahead	marginal	identity	$\mathbf{4}$ s	$\mathbf{8}$ s	$\mathbf{16}$ s	$\mathbf{32}$ s	$\mathbf{64}$ s	$\mathbf{128}$ s	Best
$1$ s	3.4435	3.2911	0.1048	0.1090	0.1097	0.1070	0.1038	0.0982	$16$ s
$10$ s	3.4418	3.2918	0.0892	0.0952	0.0988	0.1001	0.0984	0.0948	$32$ s
$30$ s	3.4433	3.2922	0.0761	0.0835	0.0891	0.0915	0.0920	0.0895	$64$ s
$60$ s	3.4398	3.2913	0.0695	0.0756	0.0817	0.0854	0.0865	0.0854	$64$ s

Appendix

Table 12: Exploratory history-probe results conditioned on recording-level statistics. Rows are prediction horizons and columns the length of history supplied. Marginal is a predictor given no input and Identity one given only a leave-one-out estimate of the recording’s own code distribution; the first two columns are their absolute held-out cross-entropies in nats, and the remaining six are

\mathrm{CE}(\textsc{Identity})-\mathrm{CE}(\textsc{Identity}{+}K)

, the reduction in cross-entropy from adding

K

seconds of history to the same recording-level estimate. Best denotes the history length

K

with the largest improvement, also marked in bold . Measured on

19{,}200

windows drawn from

4{,}600

pre-training recordings, of which

17{,}026

windows support the leave-one-out estimate, split by recording.

Dataset	Task	Unit	#Ch.	Rate	$\mathbf{L}$	$\mathbf{K}$	Selection	Split
FACED ( Chen et al., 2023 )	Emotion, $9$ -class	$10$ s	$30$	$250$	$1$	$19{,}200$	$\kappa$	subject, fixed
KaggleERN ( Mattout et al., 2014 ; Margaux et al., 2012 )	ERN, binary	$2$ s	$19$	$200$	$1$	$3{,}840$	ROC-AUC	subj., $4$ folds †
SEED-VIG ( Zheng and Lu, 2017 )	Vigilance, regression	$8$ s	$17$	$200$	$1$	$19{,}200$	$R^{2}$	session, fixed
Sleep-EDF ( Kemp et al., 2000 ; Goldberger et al., 2000 )	Sleep stage, $5$ -class	$30$ s	$2$	$100$	$20$	$12{,}000$	$\kappa$	subject, $5$ -fold
CHB-MIT ( Goldberger et al., 2000 ; Shoeb, 2009 )	Seizure, binary	$30$ s	$15$	$256$	$20$	$90{,}000$	AUC-PR	case, $5$ -fold ‡

Appendix

Table 13: The five downstream tasks. The three above the rule are window tasks and the two below are sequence tasks. Unit is the interval one label is defined on, #Ch. the electrodes used and Rate their sampling rate in Hz;

L

is the number of consecutive units per sample, so

L=1

is a single labelled segment;

K

is the readout budget, fixed per dataset and identical across models (Appendix D.2.1 ); Selection is the validation criterion a checkpoint is chosen on, and Split how the data is partitioned. † Not a cross-validation (Appendix C.2 ). ‡ Folds are over the 24 case folders, which come from 23 subjects; see Appendix C.5

Baseline	Architecture / feature family	#Params	Pre-training
BIOT	Linear-attention transformer over per-channel STFT patches	$3.2$ M	MGH, SHHS, TUAB, TUEV, CHB-MIT, IIIC
CBraMod	Criss-cross transformer over the channel $\times$ time grid	$4.9$ M	TUEG, ${>}9{,}000$ h
LaBraM-Base	Vector-quantised tokenizer with masked-token prediction	$5.8$ M	$16$ corpora, ${>}2{,}500$ h
EEGPT-Large	ViT with dual mask-reconstruction and alignment	$25.3$ M	PhysioNet-MI, TSUBenchmark, M3CV, SEED
ST-EEGFormer-S	ViT with masked autoencoding on raw EEG	$25.4$ M	${>}8$ M segments; MI, P300, SSVEP
REVE-Base	ViT with 4D Fourier encoding of electrode geometry	$69.2$ M	$92$ datasets, ${>}60{,}000$ h, ${\sim}25{,}000$ subjects

Appendix

Table 14: The nine baselines. The six above the rule are pre-trained foundation models and the three below are decoders that were never pre-trained. #Params is the pre-trained backbone as loaded, excluding pre-training-only components such as decoders and projection heads; a classical pipeline has no separable backbone and readout, so the column is left blank. Pre-training reproduces each release’s own account of its corpus, which is why the entries are not stated on a common footing.

Model	FACED	KaggleERN	SEED-VIG	Sleep-EDF	CHB-MIT
BIOT				$\circ$	$\bullet$
CBraMod					$\circ$
LaBraM-Base	$\circ$	$\bullet$			$\circ$
EEGPT-Large	$\circ$
ST-EEGFormer-S
REVE-Base				$\circ$	$\circ$

Appendix

Table 15: Pre-training overlap by model and task. Every model’s pre-training corpus, ours included, was audited against every downstream dataset.

\bullet

marks corpus overlap, the downstream corpus itself appearing in that model’s pre-training set; it occurs twice and both cases are discussed below.

\circ

marks domain overlap, a different corpus of the same recording paradigm and population, which is the ordinary condition of pre-training at scale rather than leakage. Blank means neither. The three classical pipelines of Table 14 have no row here, never having been pre-trained.

Dataset	$\mathbf{K}$	Determined by	Head size	Spread
FACED	$19{,}200$	$1{,}920$ dim/s at $10$ s	$4.92$ – $4.98$ M	$1.012\times$
KaggleERN	$3{,}840$	$1{,}920$ dim/s at $2$ s	$1.00$ – $1.05$ M	$1.045\times$
SEED-VIG	$19{,}200$	carried over from FACED	$4.93$ – $5.02$ M	$1.020\times$
Sleep-EDF	$12{,}000$	no-clamp bound $\min_{m}N_{m}D_{m}$	$4.75$ – $4.94$ M	$1.041\times$
CHB-MIT (15 Ch)	$90{,}000$	no-clamp bound $\min_{m}N_{m}D_{m}$	$24.81$ – $24.95$ M	$1.006\times$
CHB-MIT (2 Ch)	$12{,}000$	no-clamp bound $\min_{m}N_{m}D_{m}$	$4.75$ – $4.94$ M	$1.041\times$

Appendix

Table 16: Readout budget per dataset and the head sizes it produces.

K=Nd_{\mathrm{tok}}

is the product of token count and per-token width, held fixed per dataset so that head size does not track whichever tokenizer emits the most tokens. Determined by names the rule that sets it: either a fixed readout capacity per second of EEG, or the no-clamp bound

\min_{m}N_{m}D_{m}

where holding that capacity would push some model’s

d_{\mathrm{tok}}

above its width

D_{m}

. Head size is the range over the nine models that receive a shared head and Spread their ratio; the Sleep-EDF and CHB-MIT figures include the context head, which is identical across models and therefore compresses the ratio.

Activation	$\mathrm{d}/\mathrm{d}x$ at $x=-8$	Seeds escaping	Balanced accuracy
GELU	$0$ (and ${<}0$ for $x<-0.75$ )	$0/5$	$0.111\pm 0.000$
ReLU	$0$	$1/5$	$0.165\pm 0.108$
ELU	$3.4\times 10^{-4}$	$3/5$	$0.219\pm 0.124$
LeakyReLU $(0.01)$	$1.0\times 10^{-2}$	$5/5$	$0.354\pm 0.013$

Appendix

Table 17: Escape from the class-prior solution on FACED with the BIOT backbone. Five seeds per activation, warm-up disabled, everything else held fixed. A run that fails to escape drives all

256

hidden units of the shared head negative and emits the training class prior thereafter, at which point balanced accuracy is exactly

1/9

, the chance level for nine classes. Rows are ordered by how much gradient the activation leaves on the negative side, evaluated at

x=-8

, a value typical of the collapsed units. The recipe of the main tables adds three warm-up epochs, which is why the LeakyReLU row reads

0.354

here against the

0.364

of Table 2 .

Figure 4: Effect of pre-training. Downstream performance of NeurDuo (Small) with and without pre-training. Arrows indicate whether higher or lower values are better.

Model	W	N1	N2	N3	REM
BIOT †	$0.924$	$0.463$	$0.828$	$0.676$	$0.764$
CBraMod	$0.914$	$0.458$	$0.845$	$\mathbf{0.805}$	$0.775$
LaBraM	$0.928$	$0.478$	$\mathbf{0.851}$	$0.799$	$0.773$
EEGPT	$0.922$	$0.455$	$0.829$	$0.766$	$0.725$
ST-EEGFormer-S	$0.923$	$0.413$	$0.826$	$0.672$	$0.740$
REVE-Base	$\mathbf{0.930}$	$0.478$	$0.835$	$0.680$	$0.784$

Appendix

Table 18: Per-class F1 on Sleep-EDF , five-fold means. Classes are the five AASM stages, W for wake and N1 to N3 for non-REM depth. Bold marks the best value in a column among the seven models above the rule, each in the configuration Table 3 reports. Below the rule are that same model with its state reset at every unit boundary and the difference between the two, neither of which is part of that comparison. † Domain overlap on Sleep-EDF, see Appendix D.1 .

Metric	Fold 1	Fold 2	Fold 3	Fold 4	Fold 5	Mean	$\mathbf{t}$	Positive
Macro-F1	$+0.0277$	$+0.0159$	$+0.0039$	$+0.0002$	$+0.0037$	$+0.0103$	$+2.02$	$5/5$
Accuracy	$+0.0089$	$+0.0146$	$+0.0044$	$+0.0083$	$+0.0043$	$+0.0081$	$+4.27$	$5/5$
Cohen’s $\kappa$	$+0.0141$	$+0.0189$	$+0.0051$	$+0.0107$	$+0.0040$	$+0.0106$	$+3.80$	$5/5$

Appendix

Table 19: Carried minus reset on Sleep-EDF, fold by fold. Same folds, same seed, same readout and context head; only the backbone state differs.

t

is the paired statistic over the five differences, for which the two-sided

5\%

critical value at four degrees of freedom is

2.776

Figure 5: The two streams run at different rates. (a) Nominal time constant

\tau=1/(\Delta|A|)

, with

\Delta

taken from the trained bias alone, of every state dimension of the pre-trained Small model, converted to seconds at each stream’s own step; dashed lines are medians. (b) Relative state change per second on one ten-minute CHB-MIT test sequence, averaged over the

15

channels; the shaded band marks its three seizure units.

Figure 6: Stateful streaming inference. Each

0.5

s chunk advances the fast stream by one step; every fourth chunk writes a memory token that advances the slow stream and enters the

16

s bank the fast state queries. Nothing is reset within a recording.

Model	Sleep-EDF (2 ch)						CHB-MIT (15 ch)
Model	30 s	300 s	600 s	1,800 s	3,600 s	GB	30 s	300 s	600 s	1,800 s	3,600 s	GB
EEGPT-Large	$4.0$	$27.3$	$49.8$	$137.6$	$274.0$	$1.22$	$8.5$	$66.0$	$128.2$	$382.9$	$765.5$	$3.46$
ST-EEGFormer-S	$3.9$	$40.5$	$136.6$	$1{,}221$	$5{,}119$	$0.97$	$28.9$	$1{,}934$	$8{,}061$	$74{,}302$	– ∗	$5.64$
BIOT	$6.0$	$23.3$	$40.5$	$106.9$	$209.2$	$0.89$	$6.0$	$23.3$	$42.7$	$107.0$	$209.5$	$0.98$
LaBraM	$9.9$	$98.0$	$196.4$	$587.2$	$1{,}174$	$0.05$	$10.0$	$98.1$	$196.2$	$585.9$	$1{,}177$	$0.20$
CBraMod	$5.8$	$5.9$	$6.1$	$15.1$	$38.1$	$0.16$	$5.8$	$9.2$	$19.0$	$93.6$	$281.4$	$0.99$

Appendix

Table 20: Cost of conditioning on longer history. Latency (ms) to encode one window, with peak memory (GB) measured at

1{,}800

s. NeurDuo latency is measured per

0.5

s chunk after streaming the stated history.

Component	Shape	Values	KB (fp32)
Fast stream	$6\times(512\times 16\ \text{SSM}+512\times 4\ \text{conv})$	61,440	240.0
Slow stream	$3\times(384\times 16\ \text{SSM}+384\times 3\ \text{conv})$	21,888	85.5
Memory bank	$8\times 192$	1,536	6.0
Writer queue	$16\times 256$	4,096	16.0
Last output	$256+192$	448	1.8
Total per channel		89,408	349.2

Appendix

Table 21: The persistent state , per channel, for NeurDuo (Small). Sizes follow from the architecture constants of Table 10 and are the same at every point in a recording. The writer queue is a ring buffer of

16

slots, of which each write pools the most recent

w=8

; an implementation that kept only those eight would save

8

KB per channel.

	Sleep-EDF montage, two channels				CHB-MIT montage, fifteen channels
History	ms	$\bm{\times}$ real time	GB peak	MB state	ms	$\bm{\times}$ real time	GB peak	MB state
$30$ s	$4.89$	$102\times$	$0.047$	$0.68$	$4.90$	$102\times$	$0.061$	$5.12$
$1$ min	$4.89$	$102\times$	$0.047$	$0.68$	$4.89$	$102\times$	$0.061$	$5.12$
$5$ min	$4.90$	$102\times$	$0.047$	$0.68$	$4.87$	$103\times$	$0.061$	$5.12$
$10$ min	$4.84$	$103\times$	$0.047$	$0.68$	$4.85$	$103\times$	$0.061$	$5.12$
$30$ min	$4.85$	$103\times$	$0.047$	$0.68$	$4.87$	$103\times$	$0.061$	$5.12$

Appendix

Table 22: Streaming a recording from thirty seconds to an hour , NeurDuo (Small). Each row advances the state to the stated amount of history and then measures the next chunks, averaged over a full write cycle. Nothing in the table depends on the history, which is the property § 3.2.2 claims and this table exists to check.

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May 27, 2026, cs.AIJ/K move · Enter open · S save

CaMBRAIN: Real-time, Continuous EEG Inference with Causal State Space Models

Abhilash Durgam, Nyle Siddiqui, Jeffrey A. Chan-Santiago, Qiushi Fu+2

CRCV, University of Central Florida · Department of MAE, University of Central Florida · Department of Neurology, Loma Linda University

Electroencephalography State Space Models Streaming Inference Long-Term Memory Real-Time Systems Causal

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Electroencephalography (EEG) is a critical, non-invasive method to monitor electrical brain activity. EEGs can span anywhere from a couple seconds to multiple hours, posing a major hurdle for existing deep learning methods due to two major factors: (1) existing EEG models are predominantly built upon the attention mechanism, incurring quadratic scaling as the sequence length increases, and (2) raw EEG signals must be processed in a sliding-window fashion due to fixed-length input requirements, preventing global understanding of the entire signal. To this extent, we propose CaMBRAIN - the first Causal, Mamba-based state space model (SSM) capable of real-time inference of EEG signals, arguing that bidirectional approaches are needlessly expensive given the causal, unidirectional nature of EEG. However, training such a model is non-trivial, as crucial EEG events can be extremely brief - within fractions of a second - yet separated by long intervals spanning minutes. Current EEG methods use self-supervised objectives that optimize for signal reconstruction, but these are not well suited for streaming SSMs; they fail to explicitly train the hidden state to retain the salient long-range context needed for streaming inference. We therefore introduce a multi-stage self-supervised training pipeline specifically tailored to encourage long-range memory retention and strong performance on EEG signals, while preserving the linear-time complexity of state space models. CaMBRAIN achieves state-of-the-art (SOTA) results across 3 different EEG datasets with >10x higher throughput than existing models, enabling the first model capable of long-range, continuous inference of variable-length EEG signals.

Abstract

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