SPICED: A Synaptic Homeostasis-Inspired Framework for Unsupervised Continual EEG Decoding
Authors: Yangxuan Zhou
Sha Zhao
Jiquan Wang
Haiteng Jiang
Shijian Li
Tao Li
Gang Pan
Venue: NeurIPS 2025 poster ()
Keywords: Synaptic homeostasis; Brain-inspired; Continual EEG decoding
Primary area: neuroscience_and_cognitive_science
TL;DR: We propose a novel Synaptic Homeostasis-Inspired framework for Unsupervised Continual EEG Decoding

Abstract: Human brain achieves dynamic stability-plasticity balance through synaptic homeostasis, a self-regulatory mechanism that stabilizes critical memory traces while preserving optimal learning capacities. Inspired by this biological principle, we propose SPICED: a neuromorphic framework that integrates the synaptic homeostasis mechanism for unsupervised continual EEG decoding, particularly addressing practical scenarios where new individuals with inter-individual variability emerge continually. SPICED comprises a novel synaptic network that enables dynamic expansion during continual adaptation through three bio-inspired neural mechanisms: (1) critical memory reactivation, which mimics brain functional specificity, selectively activates task-relevant memories to facilitate adaptation; (2) synaptic consolidation, which strengthens these reactivated critical memory traces and enhances their replay prioritizations for further adaptations and (3) synaptic renormalization, which are periodically triggered to weaken global memory traces to preserve learning capacities. The interplay within synaptic homeostasis dynamically strengthens  task-discriminative memory traces and weakens detrimental memories. By integrating these mechanisms with continual learning system, SPICED preferentially replays task-discriminative memory traces that exhibit strong associations with newly emerging individuals, thereby achieving robust adaptations. Meanwhile, SPICED effectively mitigates catastrophic forgetting by suppressing the replay prioritization of detrimental memories during long-term continual learning. Validated on three EEG datasets, SPICED show its effectiveness. More importantly, SPICED bridges biological neural mechanisms and artificial intelligence through synaptic homeostasis, providing insights into the broader applicability of bio-inspired principles.

SPICED: A Synaptic Homeostasis-Inspired Framework for Unsupervised Continual EEG Decoding
Yangxuan Zhou 1.2 Sha Zhao" 1:2 ; Jiquan Wang Haiteng Jiang?411, Shijian Li
Tao Li Gang Pan
State Key Laboratory of Frain-machinn Intelligence, Zhejiang University
"College of Computer Science and Technology. Zhejiang University
Department of Neurobiology, Affiliated Mental Health Center & Hangzhou Seventh People's ' Hospital, Zhejiang University School of Medicine
MMOE Frontier Science Center for Brain Science and Brain-man machine Integration, Zhejiang University
(zyangxuan, szhao, vangjiquan, h. h.jiian]00zju edu. cn; {shijianli, litaozjusc, gpan}@zju edu, cn;
Abstract
Human brain achieves dynamic Kabiiity-pllsticiiy balance through synaptic home- ostasis, a self-regulatory mechanism that stabilizes critical memory traces while preserving optimal learning capacities. Inspired by this biological principle, we propose SPICED: a neuromorphic framework that integrates the synaptic home- ostasis mechanism for unsupervised continual EEG decoding, particularly ad- dressing practical scenarios where new individuals with inter-individual variability emerge continually. SPICED comprises a novel synaptic network that enables dynamic expansion during continual adaptation through three bio- bio-inspired neural mechanisms: (1) critical memory reactivation, which mimics brain functional specificity, selectively activates task-relevant memories to facilitate adaptation; (2) synaptic consolidation, which strengthens these reactivated critical memory traces and enhances their replay prioritizations for further adaptations and (3) synap- tic renormalization, which are periodically triggered to weaken global memory traces to preserve learning capacities. The interplay within synaptic homeostasis dynamically strengthens task-disriminnativ memory traces and weakens detrimen- tal memories. By integrating these mechanisms with continual learning system, SPICED preferentially replays fask-dicriminative memory traces that exhibit strong associations with newly emerging individuals, thereby achieving robust adaptations. Mcanwhile, SPICED effectively mitigates catastrophic forgetting by suppressing the replay prioritization of detrimental memories during long-term continual learning. Validated on three EEG datasets, SPICED show its effec- tiveness. More importantly, SPICED bridges biological neural mechanisms and artificial intelligence through synaptic homeostasis, providing insights into the broader applicability of bio-inspired principles. The source code is available https: btttss//ggitubl http//gttttttt//////////et com/ www.bbabbbbbb//
"Corresponding authors.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Introduction
During long-term learning and memorization, the human brain dynamically balances information preservation (i.e., stability) and knowledge acquisition (i.e., plasticity) through self-regulatory mechanism termed synaptic homeostasis [T3].. This homeostasis process coordinates two com- plementary neural mechanisms: synaptic consolidation, which stabilizes critical memory traces, and synaptic renormalization, which preserves brain learning capacities [4 The homeostatic interplay between synaptic consolidation and renormalization sustains neural
equi- librium, enabling the brain to dynamically adapt to novel environmental demands and learning tasks. This biological principle provides critical insights into overcoming stability-plasse ppaastttyyy dilemma and catastrophic forgetting in artificial continual learning systems, especially in non-stationary continual brain decoding scenarios where the pretrained source model is required to adapt to unseen individuals continuously.
For advancing brain decoding research and applications, has become instrumental. As the most widely adopted non-invasive BCI technique, electroencephalog raphy (EEG) captures scalp-recorded electrophy activity with high temporal resolution, making it particularly valuable in both practical and clinical applications (e.g., sleep staging, de- pression diagnosis) [11.15] However, most EEG-based models trained on static datasets exhibit limited generalizability to unseen subjects due to substantial inter-individual variability, hindering their applicability in real-world scenarios where new ones are encountered continually. This problem can be reduced to continual EEG decoding, wherein EEG models need to dynamically assimilate new knowledge while retaining consolidated prior representations. Nevertheless, critical challenges persist on continual EEG decoding: (1) How to enhance model plasticity enable robust adaptation to each newly emerging individual? (2) How to prevent catastrophic forgetting by suppressing the accumulation of detrimental memory traces during the long- ing-term continual learning?
interface (BCI) technology
Motivated by synaptic homeostasis principles, we leverage the biological synaptic plasticity to develop the neuromorphic framework to address the aforementioned challenges in continual EEG decoding. Here, we propose a novel Synaptic Homeostasis-Imme Inspire framework for Unsupervised Continual EEG Decoding, called SPICED, emulating the brain's 's mechanisms for processing contin- uous information streams. The core SPICED consists a biologically inspired synaptic network comprising multiple interconnected synaptic nodes, with each node storing individual-specinn information. Upon encountering a new individual, the synaptic network dynamically incorporates an individual-spent -speciice new node and selectively establishes synaptic connections with pre pre-existing nodes. For challenge (I), we adopt the synaptic consolidation mechanism which can automatically and selectively reinforce critical mask-diseriminative memory traces, enhance the model plasticity to continuously emerging new individuals. To this end, we need to decide which memory traces important, and then consolidate them. Specifically, introduce critical memory reactivation mechanism guided by brain's functional specificity to select and replay the important memory trace. During each adaptation, we identify task-disrrminnative synaptic nodes that are relevant to the indi- vidual adaptation, and selectively reactivate them to retrieve memory traces strongly associated with the target individual for replay. We further stabilize these reactivated synaptic nodes by strengthening their connections shown in Fig. (a). As result, these repeatedly reactivated task musk-discriminative memory traces undergo progressive consolidation, enhancing their replay prioritization to facilitate
Neuron
Selectively
Neuron
Neuron
Periodically
Neuron C
Synaptic Connection
Synaptic Consolldation
Synaptic Homcostasis
Synaptic Renormalization
Synaptic Connection
Neuron
Synaptic Strength
11
Neuron
Neuron
Synaptic Strength T1
Neuron
(a)
bb)
Figure 1: Synaptic consolidation and renormalization mechanisms. (a) Task-driven synaptic con- solidation via critical connection strengthening. (b) through connection strength downscaling.
synaptic renormalization

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robust adaptation to novel individuals. For challenge (2), we adopt the synaptic renormalization mechanism which can maintain balance of synaptic strength, and restore the ability to learn novel sequences, to suppress the interference from detrimental memory traces. As illustrated in Fig. [b). SPICED periodically triggers global downscaling to weaken synaptic connection, preserving the network's s learning capacity. The synergistic balance between synaptic consolidation and renormal- ization in SPICED enables strengthening the lask-discrmminative memory traces and weakening the redundant noisy memories. By consequently suppressing the replay prioritization of detrimental memories during each individual adaptation, SPICED effectively mitigates catastrophic forgetting caused by emror accumulation during long-term continual learning. Our contributions are follows:
We propose SPICED, a neuromorphic framework comprising a bio-inspired synaptic net- work that integrates the synaptic homeostasis mechanism for unsupervised continual EEG decoding. SPICED achieves robust adaptation to novel individuals while preventing catastrophic forgetting in long-term learning scenarios.
We model the synaptic homeostasis between consolidation and renormalization, selec- tively stabilizing task-discrmminative memory traces while suppressing interference from redundant noisy memories, achieving balanced network plasticity.
Validated across three mainstream EEG tasks, SPICED enables continuous dynamic expan- sion of synaptic networks while maintaining homeostatic balance, thereby achieving robust performance novel unseen individuals.
2 Related Work
2.1 Synaptic Homeostasis
Synapses, which bridge pre-synaptic and post-synaptic neurons, function as the essential units for information storage within the brain. Under synaptic homeostasis, their strength and connectivity are dynamically regulated to encode information while preserving circuit stability [H3 [6]. During active learning states, task-specific neuronal ensembles are selectively recruited through cortical functional specificity, wherein environmental stimuli activate discrete neuronal populations aligned with behavioral demands [17-19]. This recruitment triggers the serivity-depended long-term potentiation (LTP) driven synaptic consolidation that selectively strengthens synapses within engaged circuits, thereby stabilizing newly encoded memories [9] 20 [1]. Following learning, during slow-wave sleep (0.5-4 Hz), long-term depression (LTD) driven synaptic renormalization globally downscales synaptic strength, preferentially weakening nonessential connections formed during
wakefulness [10 22]
2.2 Synaptic-Inspired Continual Learning
Continual learning (CL.) aims enable artificial systems to incrementally acquire knowledge from continuous data streams. Numerous CL methods, such as the regularization-bart (23 the parameter isolation based (28-30) and the rehearsal-based [3 methods, have been developed to mitigate catastrophic forgetting. Recent efforts have integrated synaptic plasticity principles into parameter adaptation and memory consolidation during CL process, demonstrating improved trade-offss (35442). However, these methods only partially emulate neurobiological mechanisms, often necessitating architectural modifications or intrusive adjustments core learning processes. In contrast, the SPICED framework introduces a novel learning paradigm that biologically emulates synaptic homeostasis through an auxiliary synaptic network, achieving model structural independence while preserving task mask-specific adaptability and interference suppression capabilities.
2.3 Continual EEG Decoding
Numerous deep learning methods have been proposed for EEG decoding across diverse tasks 45 However, parameters of these EEG-based models are typically fixed after training, limiting their generalization ability to new subjects encountered clinics. This limitation has motivated recent efforts to explore -ndivicual-specific continual EEG decoding algorithms in supervised paradigm [48] 50]. Zhou et al. [5T) introduces an unsupervised individual continual learning (i.e., UICL paradigm) framework tailored for real-worle deployment. It incrementally adapts model to unseen individuals
3

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Stne 21
Replay
N5
Initial Feature
Individual Model
Roplay
Joint
Syrapses (Node
Replay Samples
NI
Training
(Similarity. Strength))
R.
N4 INFO
NB
Sitn
N6
Fusion
NB
N3
N3
(a) Synaptic Network Initialization
(b) Synaptic Node Incorporation
(c) Critical Memory Reactivation
Model
Saving
NY
Continual Adaptation
Synaptic Strength L1
Synapric Strength 1T
N6
N6
N3
N3
(f) Expanded Synaptic Network
(e) Synuptic Renornalization
(d) Synaptic Consolidation
Figure 2: The workflow SPICED, a synaptic comeessasiis-nsspiee framework for continual EEG decoding. (a) Synaptic network initialization: Constructing -ndivicull-specific nodes and initial feature-based synaptic connections. (b-e) Adaptive synaptic dynamics for novel subjects: (b) Synaptic node incorporation:
Incremental integration of new individuals; (c) Critical memory reactivation: Replay of critical task-related memories; (d) Synaptic consolidation: Selective strengthening of lask-relevant connections; (e) Synaptic renormalization: Global downscaling of synaptic strengths. (f) Expanded network evolution: Expanded synaptic network after continual individual adaptations.
while retaining consolidated knowledge from prior adaptations. Our work based on the setting of the proposed UICL paradigm.
3 Methodology
3.1 Overview
To emulate synaptic homeostasis in the human brain, consider each subject as distinct domain and represent individuals as nodes with one-to-one correspondence to record their unique information (e.g., individual ID, initial feature (i.e., handcrafted features), corresponding model, replayed samples, and synaptic connections). Formally, given multiple labeled individual domains (i.e., source domain)
Ds=(X's V' y,Va with Nss subjects, we first utilize the labeled source domain data to pretrain an EEG model and initialize the synaptic network as illustrated in Fig. 2 Specifically, we calculate and store each individual's initial feature by extracting features across the time, frequency, and time-frequency domains, with detailed descriptions provided in the Appendix A We measure inter-nodes initial feature similarity via weighted cosine similarity described as follows:
We simmat 2²}) + We sim(a/ xr/) + wir sim 1/ x
S(re, x.;)
Wh + W/ + wif
where rf, xf,x'1 denote the EEG features vectors in the time, frequency and time-frequency do- respectvyy and wt, wi, Wir denote their corresponding weights. We define connection threshold &, such that synaptic connections are established between nodes whose pairwise similarity exceeds E, with initial synaptic strength &ij set to 1. Based on inter-nodes similarity, we perform node-wise initialization of the source synaptic network. Then, given multiple unlabeled sequential
individual target domains Dy=(X+)> Nr comprising Ny subjects, SPICED continuously adapts to unseen individual target domains by expanding its synaptic network through: (i) incremental node in- tegration, (ii) critical memory reactivation, and (iii) balanced synaptic possoidattion-enormaliiae as illustrated in Fig. b-e). Following BrainUICI. [1], after each adaptation to a new individual, we store high-confidence pseudo-labels and its corresponding model in individual-specd speeiffi synaptic nodes for further memory replay. Notably, we preserve all labeled samples from source-domain

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individuals in their corresponding synaptic nodes. Through continuous integration of new nodes and assimilation of novel knowledge. the synaptic network progressively enhances its robustmess shown in Fig. f Meanwhile, with the interplay of synaptic consolidation and renormalization, critical memories are continuously consolidated while redundant noisy memories are progressively suppressed during continual decoding.
3.2 Synaptic Network Initialization
To validate the effectiveness of our SPICED framework, utilize identical EEG-based model architectures for each downstream EEG task. Specifically, the model integrates a feature extractor for capturing EEG features, temporal encoder for learning temporal dependencies from EEG sequences, and a classifier adapted to the output requirements of different downstream tasks. Prior to synaptic network initialization, model architecture and pre-training process are described in the Appendix For each source domain individual, initialize their synaptic nodes by storing their initial feature, labeled samples, and the pre- rre-traind model. Subsequently, we selectively establish synaptic connections between nodes based on pairwise weighted cosine similarity, and store the synaptic information (e.g., similarity and synaptic strength) for subsequent processing.
pre-train the task-specific EEG model on source domains. The detailed
3.3 Synaptic Node Incorporation
Upon the arrival of new individual, we first initialize its personal synaptic node (i.e., ID and initial feature). It then traverses pre-existing nodes the synaptic network to compute pairwise similarity between their initial feature. If the similarity exceeds predefined connection threshold ç. we establish their inter-nodes connections with an initial strength of I, and we update the node's synaptic data (e.g.. Synapses: (Nodel: (similarity: 0.5, strength: 1}]).
3.4 Synaptic Consolidation
Human brain leverages its functional specificity activate task-aligned neuronal ensembles during external stimulation [7 18 This neurobiological mechanism ensures cognitive efficiency while enabling adaptive flexibility to address novel environmental demands. Inspired by this mechanism, we introduce the Critical Memory Reactivation to activate task-relevant memory traces into ongoing individual-specifit adaptation. To determine which nodes reactivate, we introduce a synaptic importance coefficient to measure connection significance between nodes N₁ and Nj as follows:
I(N,,N)) aS(I,, a1-a) 5555
8yk
Here, NG) denotes the importance of node Ní relative to node N₁. S(z;, denotes the similarity between N₁ and N₁- 8j denotes the average synaptic connection strength of node Nf. reflecting the global activation status of its associated neural pathways within the synaptic network. And denotes the importance weight of inter-nodes similarity.
3.4.1 Critical Data Replay
We calculate and rank the importance coefficient of synaptic connections for the current node in descending order. To prioritize the activation of the most relevant memories, select top- node samples via iepoortance-eiighte sampling for weighted replay.
3.4.2 Critical Model Fusion
In traditional continual learning paradigms, the incremental model M; at the i-th step is derived from training on the prior model M;-1, exclusively dependent on knowledge consolidated during the terminal phase of prior training cycles. In contrast, the human brain selectively recruits task- aligned neuronal assemblies during cognitive processing, integrating information streams from all historical experiences without dependence on single network state. This biological insight motivates the integration of synaptic networks to determine optimal activation strategies. Specifically, for the current i-th individual, select the top-K synaptic nodes based on importance coefficient
5

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ranking. Then we initialize the fusion model MM, through weighted aggregation of corresponding top-K K node model parameters, as detailed below:
IG) M j=1 EK=I I(N M2
Following the BrainUICL, we clone a guidance model from the fused model M, and conduct self- supervised training on newly added individual samples using this guidance model, as detailed in the Appendix C
The guidance model generates high-qualit, pseudo-labels with predicted probabilities surpassing a predefined threshold 17. Then, pseudo-labele samples undergo joint training with incremental samples via cross-entropy loss, as formulated below:
C = -3 Σ Ue log De (1 (1-3)
3>Ene Vv. (R.)
where 99 is hyper-parameter controlling the weight of the loss function, while ye and yR. represent the pseudo-labels of incremental individual samples and the ground-truth labels of replayed top- node's samples, respectively. Following joint training, the fusion model M, is used to predict incremental samples, and generates high-confidence pseudo-labeled samples. Both the pseudo-labeles samples and the fine-tuned model M, are stored in node N₁ for future memory replay. Notably, no synaptic connections exist for a new node, during the model fusion phase, we perform fusion using the three most similar node models based on inter-nodes similarity. The fused model is then directly applied to self-supervised learning for adaptation without employing replay strategies.
3.4.3 Critical Connection Consolidation
Upon completion of incremental individual adaptation, the SPICED conducts synaptic consolidation long-term potentiation (LTP) mechanisms, stabilizing the task-relevant node memories reactivated in critical memory reactivation. Specifically, we only strengthen the synaptic strengths of the top-K activated nodes, as described below:
to simulate
1/ Ysij Vi TK, Vj E
(5)
Here, indexes the top-K activated nodes, j indexes all nodes connected to node M, and y represents the consolidation coefficient governing synaptic strength amplification. In biological neural systems, synaptic strength does not increase indefinitely with repeated activation due to metabolic homeostasis constraints. Studies indicate that synaptic efficacy typically saturates at 150-300% of baseline levels (4). In SPICED, we impose an upper limit of on synaptic strength (with baseline strength of 1) to align computational efficiency with biological plausibility.
Algorithm 1: SPICED framework for Unsupervised Individual Continual Learning
Input: ('s) 1 (xi Ny j==
No
Output:
Synaptic Network SN
Synaptic Network Initialization:
Pretrain the source model Ma using (X3. }
Initialize the SN using {x's and Mu. guided by inter-individual similarity Eq. (1.
Unsupervised Individual Continual Learning:
for i x- to NG do
Initialize the synaptic node N,, for incremental individual and incorporate into SN- Compute the top-K most critical nodes for all nodes relative to N, according to Eq. (2)
Weightedly replay samples from the top-K nodes.
Aggregate the top-K models via weighted fusion to obtain model M, by Eq. 3
Generate the guiding model My, copied from the model M,;
Optimize M, by minimizing Eq. (7) and generate confident pseudo labels
Optimize M, by minimizing Eq. (4)
Incorporate the high-confidence pseudo-labeled samples (L77 Dit)) and model M, into Mg.
Strengthen the synaptic strength of top-K activated nodes according to Eq. (5).
Weaken the synaptic strength of global nodes according to Eq. (6)
end

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3.5 Synaptic Renormalization
The proposed SPICED periodically triggers synaptic renormalization to simulate sleep-dependent long-term depression (LTD) mechanisms, globally weakening synaptic connection strengths. Inspired by the Ebbinghaus forgetting curve [55], which models the temporal decay of human memory retention, we formalize node-specific dynamic renormalization rates regulate synaptic strength in time-dependent manner, as detailed below:
" = S'y ViEV V, Vj E.,
(6)
where V denotes the set of all synaptic nodes, indexes all nodes connected to node N, and the decay factor. Notably, upon initialization of the i-th new node, the time step & is set to 1. During global synaptic renormalization Eq. (6. I is incremented by I for all nodes. However, when the current node is activated and undergoes synaptic consolidation Eq. (5) is reset to 1. This mechanism ensures that infrequently activated nodes exhibit accelerated forgetting (higher decay rates), while frequently stabilized nodes maintain lower forgetting rates, aligning with neurobiological learning principles. The homeostasis interplay between synaptic consolidation and renormalization effectively consolidates critical information and enhances model adaptability to novel individuals. Meanwhile, it suppresses the redundant information, thereby preventing catastrophic forgetting caused by error accumulation. The overall algorithm illustrated Algorithm
Experiment
4.1 Experimental Setup
Table 1: Overview of downstream BCI tasks and datasets.
BCI Tasks
Datasets
# Subjects
Rate
# Channels
Duration
Samples
Label
Sleep Staging
ISRUC
100
200Hz
30s
89,240
5-class
II. Emotion Recognition
FACED
123
250Hz
10,332
9-class
III. Motor Imagery
Physionet
109
160Hz
64
4s
9,837
4-class
We selected three mainstream EEG-based task datasets for validation: ISRUC [56]. FACED [57 and Physionet-MI [58] shown Tab. Each dataset comprises at least 100 subjects, enabling effective evaluation of the SPICED framework's 's performance under long-term UICL scenarios. Detailed dataset descriptions and preprocessing procedures are provided in Appendix D while more detailed experimental settings, including hyper-parameters and CL training details, are listed in Appendix E
Each dataset is partitioned into pretraining set (i.e., source domain) for pre-training the source model and incremental set (i.e., target domain) for evaluating the performance of the SPICED framework in unsupervised individual continual learning (i.c., continual EEG decoding) scenario. For each novel individual in the incremental set, model performance is evaluated using Accuracy (ACC) and Macro-F1 (MF1) metrics both before and after individual domain adaptation, as defined follows:
C 2 Precision, Recall.
Accuracy
Σ 1 = 0.) Macro-FI
Precision, + Recall,
Notably, for the i-th incremental individual, the SPICED framework initializes model M, by integrat- ing information across the holistic synaptic network, rather than relying on the last incremental model M from preceding temporal states.
4.2 Results and Analysis
4.2.1
Overall Performance
We have evaluated the SPICED framework on three different downstream EEG datasets. Specifically, we set varying source-target (i.e., pretrain-incremental dataset splits (source proportions: 10%- 50%) to evaluate the long-term continual EEG decoding performance of the SPICED framework under few-shot pre-training conditions. Notably, the input order of incremental individuals directly
7

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Table 2: Overview performance of SPICED on three downstream EEG tasks under different source- target individuals splits (e-g.. "10%" denotes that 10% of subjects are used to pretrain the source model, with the remaining 90% serving as incremental individuals). Notably, distinct synaptic connection thresholds & were configured for the three datasets, while consistency was maintained across all other hhper-pprameeee parameters.
ISRUC E 0.1)
FACED ( 0.4
Physionet ML 0.5)
Awergeo ACC
Avzrage
Avenge ACC
Average
Averago
Aserage
Ma
M.
Mo
M.
Mu
M,
M4o
M.
M4i
M.
M6
M..
10%
16.8
62.62133
(5.41)
48.4
05.421551
(7.8%)
23.5
26.720010
(2.21)
19.5
22.7200.37
(3.21)
40.5
41.42:0.44
(2.81)
36.2
42.0k:00.65 (5.81)
57.3
60.2=0.45
44.1
61.7+0.06
(17.6))
32.5+0.77 (6.87)
30.5+1.32
42.0
45,3=0.30
(3,3))
43.8=0.21
30%
66.5
76.ke1113
(8.8))
60.5
TLAS00077
(10.9)
31.7
43.7220.51
(12.01)
27.
41.720058
(14.5)
42.2
48.7
(6.51)
37.9
47.9 (10,1)
13.5
77.810033
(4.31)
68.6
72.500.0
(3.7%)
38.4
48.410.32
(10.01)
35.5
46.210.00 (10.1))
47.7
50.740.14
(3,01)
65.0
74.0at1.30 (9.51)
53.5
00.260.57
(10,71)
43.4
48.95:042
(5:47)
41.7
47.0600.00
(5.31)
47.9
02.4:0.31
(4.51)
(6.77)))
ISRUC
Physiunet-
The visualization of the expanded synaptic network across ISRUC, FACED and Physionet- MI datasets, with connection thresholds & set to 0.1, 0.4, and 0.5, respectively. Notably, each point corresponds to an individual synaptic node, while dashed lines denote synaptic connections between nodes. Node size and dashed line thickness proportional to the number of synaptic connections associated with each node.
Figure 3:
influences synaptic network expansion trajectories during long-term continual decoding. Therefore, for each source-tate source-target domain split ratio, we maintained consistent dataset partitioning while altering the input order of incremental individuals across five repetitions, so evaluate the statistical reliability of experimental results. For incremental individuals, we report their personal performance of the initial source model Mo and the domain-adapted individual model MM,, as detailed in Tab. 2 Experimental results demonstrate that the SPICED framework maintains robust performance in long-term unsupervised individual continuous EEG decoding scenarios under few-shot supervised pre-training conditions. Furthermore, the SPICED framework demonstrates consistent stability across varying input order of incremental individuals, reflected in low standard deviations of accuracy and macro-F1 metrics. Besides, it is worth noting that for resting- state sleep datasets such as ISRUC, significant inter-indiiidual variability in initial features necessitates lower connection threshold & to enhance synaptic connection density. Conversely, for the basked datasets such as FACED and PhysioNet-MMI the individual initial features exhibit lower variability under identical task stimuli. Therefore, we set & much higher to filter redundant homogeneous synaptic connections while preserving addividallddiseinnnnatv connections. We employed t-SNE (59] to visualize the
expanded synaptic networks across three datasets shown in Fig. 3 Central nodes (i.e., larger-sized ones) exhibit dense synaptic connections with high strength (i.c., thicker dashed lines), suggesting frequent activation and consolidation processes during continual learning to facilitate adaptation to new individuals. In contrast, peripheral nodes (i.e., smaller-sized ones) exhibit sparse or absent synaptic connections, reflecting deviations of their initial features from dataset-level characteristics, thus being classified as outliers. Synaptic renormalization prioritized weakening these connections to mitigate noise introduced during critical memory reactivation, thereby enhancing training stability.
4.2.2 Comparison with Other Methods
We have compared our method with other existing methods at different source domain proportions: MMD 60): a UDA method that minimizes the Maximum Mean Diserepancy between feature distributions. EWC 23 a classical regularization-basel CL method. UCL-GV 61 a contrastive alignment method for continual domain adaptation. ReSNT [48]: a dynamic memory evolution based method for continual EEG decoding. CoUDA 62 recent method for continual domain
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Table 3: Accuracy performance comparison with other methods at different source domain proportions. Each method was evaluated under the same dataset partitioning, with five runs conducted per method by randomly shuffling the input order of the incremental set to enable statistical evaluation.
ISRUC
FACED
Physionet- MI
10%
30%
50%
10%
30%
50%
10%
30%
50%
MMD
57.0=0.47
70.2+0.83
68.2+1.00
24.1=0.24
39.0+1
19.142.59
37.3+0.34
44.1=0.38
47 940.4
EWC
60.6+0.71
71.3+0.49
72.5+0.39
22.3=0.36
39,5+0,94
48. 48.760.66
42.8+1.19
47.3+0.58
50.2+0.43
UCL-GV
52.5=1.16
72.5+0.32
73.9=0.29
25.0=0.43
11.4+
22.8=3.64
33.1+0.35
39.7=0.22
45.5+0.71
ReSNT
56.4=0.70
71.3+0.89
72.1
14.1=1.66
33.3+8.10
23.6+3.29
35.9+1.49
44.1=0.50
28.8+0.28
CoUDA
52.720.44
73.3:0.23
72.3+0.94
23.920.16
39.2+1.06
22.5+2.61
41.6+0.25
44.2=0.31
51.400.16
BrainUICL
56.9:0.46
74.8+0.11
73,620.29
21.2+2.04
38.8+2.90
20.2+3.46
43.1
48.3+0.38
52.5+0.36
SPICED
62.621135
75.620.13
74.5+0.39
25.7=0.10
43.720.51
48.9:0.42
43.420.44
48.7=0.17
$2.420.11
adaptation. BrainUICL [51]: a dynamic framework designed for unsupervised continual EEG decoding. We implemented these methods based on our setting. Notably, each method was evaluated under the same dataset partitioning, with five repeats conducted per method by randomly shuffling the input order of the incremental set enable statistical evaluation. The experimental results indicate that our SPICED method not only outperforms existing approaches but also exhibits superior robustness. This is attributed to SPICED's mechanism of leveraging critical memory across the holistic synaptic network for individual adaptation, rather than exclusively depending on the latest model M,- in conventional continual domain adaptation paradigms. Such strategy substantially alleviates the disruptive effects of outlier shifts within the continual learning trajectory the current individual. For instance, while BrAinUICL achieves performance comparable to SPICED certain experimental settings, it exhibits significantly degraded performance in several scenarios (e.g.. 10% source proportion on ISRUC, 50% source proportion on FACED).
4.2.3 Interplay Analysis of Varying Hyper- dlyeer-parameters À and
The interplay between synaptic consolidation and renormalization collectively facilitates synaptic homeostasis. In this section, we conduct hyper-paramnn parameter studies on the synaptic renormalization decay factor À and consolidation coefficient 7 on the ISRUC dataset, depicted in Fig. 4 The left sub-figure visualizes the variations in synaptic renormalization rate across distinct À values. As increases, the rate of decline in renormalization diminishes progressively with step increments. The experimental results demonstrate that the SPICED framework achieves optimal performance when set to 30 and y 1.3. This parameterization ensures a stabilized synaptic renormalization decay profile and moderate consolidation. This balance prevents synaptic strength collapse from transient suppression or explosion from recurrent activation.
Rogomallzztiion Rate
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Steps
Figure 4: Analysis of synaptic renormalization decay factor À and consolidation coefficient on the ISRUC dataset with 30% source domain proportion. The left sub-figure illustrates the variation in synaptic renormalization rate across different decay factor À values.
4.2.4 Dynamics of the Average Synaptic Strength
In this section, we visualize the dynamics of average synaptic connection strength source domain nodes across three datasets during unsupervised continual domain adaptation, with all nodes initialized
to average synaptic strength of 1. shown in Fig. S experimental results demonstrate that as the synaptic network expands, the dynamics of average synaptic strength changes exhibit

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Final Average Synaptic Strength Exceeds Initial Value
Final Average Synaptic Strength Below Initial Value
ISRUC
10
20
30
40
50
60
FACED
10 20
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40
50
60
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Physionet-MI
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Figure 5: Dynamics of average synaptic connection strength in source domain nodes during synaptic network expansion: with color-coded trajectories indicating post-expansion potentiation or depression compared to initial strength.
consistent pattern across three datasets. Critical nodes undergo persistent strengthening of their synaptic strength (i.e., blue lines), thereby enhancing their influence in subsequent learning tasks such as adaptation to new individuals. This persistent consolidation mechanism mimics neural ITPP, where short-term memory representations are gradually consolidated from the hippocampus into cortical long-term engrams. Conversely, redundant connections undergo progressive weakening through synaptic renormalization (i.e., red lines), mitigating noise-induced disruptions during domain adaptation. This process mirrors the LTD-driven passive forgetting mechanism in the brain, which dynamically reallocates cognitive resources by suppressing task-irrelevant or low-salience information. In summary, our synaptic nomeostasis-insiinn learning by consolidating task eikkmsssrimiittiie features and suppressing redundant noise. It synergizes with the subsequent functionally specialized critical memory activation module to jointly enable robust unsupervised continual EEG decoding.
SPICED framework enables long-term continual
More detailed visualization experiments are provided in the Appendix F And more detailed
additional studies are listed in Appendix G
5 Conclusion
In this work, we propose SPICED neuromorphic framework integrating synaptic homeostasis principles for unsupervised continual EEG decoding in non-stationary scenarios. SPICED comprises a novel synaptic network that enables dynamic expansion during continual adaptation via three biologically inspired mechanisms. First, we introduced critical memory reactivation to selectively activate task-relevant memory for replay during each adaptation. Second, we adopt the synaptic consolidation to strengthen these critical memory traces. Third, we adopt the synaptic renormalization to periodically weaken inter-node connection strengths globally. The synergistic balance between these bio-inspired mechanisms enables robust adaptation to each newly emerging individual and prevention catastrophic forgetting during long-term continual EEG decoding. SPICED achieves robust performance across three downstream EEG tasks. More discussion about implications and limitations is provided in Appendix L
Acknowledgments
This work was supported by STI 2030 Major Projects (2021ZD02004000, the National Natural Science Foundation China (4.62247)24) and the Key Program of the Natural Science Foundation of Zhejiang Province, China (No. LZ24F020004). The corresponding author is Dr. Sha Zhao.
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A Initial Feature Extraction
In this work, the synaptic network construction driven by inter-individual similarity matching of initial features, with synaptic connections formed when pairwise initial feature similarity exceeds a predefined threshold E. Thus, the extraction individual initial feature constitutes a critical prerequisite for the framework. We extract individual-speciin initial EEG features from raw signals via channel-wise feature engineering across three dimensions time domain, frequency domain, and time-frequee fieeurreyy domain. Time Domain Feature: For each EEG channel, extract the mean, variance, kurtosis, skewness, and Hjorth parameters (mobility and complexity) as initial time-domain features. Frequency Domain Feature: For each EEG channel, extract the power spectral density (PSD) across five frequency bands (i.e., (0.5-4Hz), (4-8Hz), a (8-13) 3 (13-30Hz), (30- 45Hz)) as initial frequency-doman features. Time-Freee Frequency Domain Feature: For each EEG channel, we extract wavelet energy features as initial time-frequency domain features. Following channel-wise feature extraction, inter-channel normalization applied the initial feature vectors to mitigate scaling discrepancies caused by dimensional heterogeneity across features.
B More Details for Experimental Settings Pre-training
To validate the effectiveness of the SPICED framework across diverse downstream EEG tasks, we employ a uniform model architecture comprising three core components: (1) a feature extractor composed of multiple layers of CNN, (2) Transformer-base encoder for contextual representation learning, and (3) a task-specific classifier composed of multiple fully connected layers. The detailed model parameters and pre-trained settings are summarized in Table.
Table 4: Hyper-parameters of the pre-trained model and detailed pre-training configurations. For the Conv layer, the parameter from left to right corresponds to: filters, kernel size and stride.
Epoch
100
Learning Rate
le-4
Optimizer
AdamW W
Adam 9 Weight Decay Butch
(0.5, 0.99)
Pre-training
3e-4
32
Dropout
0.1
Clipping gradient norm
1
1-th ConviD
(64, 50.
1-th MaxPoolID
(8, 8)
2-th Conv
(128, 8)
CNN Blocks
3-th Conv ID
(256, 8)
4-th ConvlD
(512, 8)
4 4-th MaxPoolID
(4, 4)
Layers
3
Hidden Dimension
512
Transformer
Heads
feed-forward dimension
2048
1-th Linear Layer
(encoder output, 256)
Classifier
2-th Linear Layer
(256, 128) (128, task-specific output)
3-th Linear Layer
C More Details for Self-Supervised Learning
Following the BrainUICL framework, we apply a self-supervised learning (SSL) method on newly incremental individual to fine-tune the guided model cloned from the initial fusion model M,, so as to generate high-confidence pseudo-labels for subsequent toint-training Specifically, we employ the Contrastive Predictive Coding (CPC) [63] algorithm to adapt the feature extractor and encoder to the data distribution of new individuals by predicting future EEG sequences using prior contextual information. Formally, given latent EEG sequences H = (ho, his 112, h3 ..., he, he+ he+2, h++a) encoded by the feature encoder, we instantiate a Transformer encoder as an autoregressive model to encode the carly-stage sequential representation Hist into contextual vector ex Subsequently,
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multiple linear layers are initialized to predict future EEG representations (he+1. h4+2. h4+3) condi- tioned on c₁. formulated as 2t+ = (c,) for k=1, 2. where 2++ denotes the predicted time steps for h Then we employ the contrastive loss to update the feature extractor and encoder as follows:
E llog exp(((++(
Cape
Enjem, cxp((1) fe(c)))
D More Details for Dataset Preparation
ISRUC is publicly sleep dataset comprising three sub-gro sub-groups. this study, we employ sub-groupl of the ISRUC dataset, comprising 100 all-night polysomnography (PSG) recordings from adult subjects. The EEG signals were acquired using six channels (F3-A2, C3-A2, O1-A2, F4-AI, C4-AI, and 02-A1) with a sampling frequency of 200 Hz. Additionally, two electrooculograpyy (EOG) channels (E1-M2 and E2-MI) were included as input modalities. All EEG and EOG signals are were bandpass filtered (0.3 Hz-45 Hz) and resampled to 100 Hz. The signals are segmented into 30-second epochs, which are manually annotated by sleep experts into five sleep stages (Wake, N1, N2. N3, and REM) in accordance with the American Academy of Sleep Medicine (AASM) [64] guidelines. Following previous sleep studies (65, we formulate the sleep staging task as a sequence tareeeett-ssuuenee classification problem, with sequence length of 20 corresponding 10-minute sleep epochs (20 30-second epochs). Notably, subjects 8 and 40 were excluded due to incomplete channels.
FACED is large-scale, high-resolution affective computing dataset that encompasses nine distinet emotional states: amusement, inspiration, joy, tenderness, anger, fear, disgust, sadness, and neutral, comprises recordings from 123 participants, with each session captured via 32-channel EEG system at a sampling frequency 250 Hz. All signals were segmented into 10-second epochs for analysis, and the full cohort of 123 recordings was utilized for experimental evaluation.
Physionet-MI motor imagery EEG dataset encompassing four distinct movement categories left fist, right fist, both fists, and both feet derived from 109 participant recordings. Each recording contains 64-channel EEG signals with sampling frequency of 160 Hz. All recordings were partitioned into 4-second epochs for analysis. Notably, subjects 38, 88, 89, 92, 100 and 104 were excluded due incomplete channels.
E
More Details for Experimental Settings on Continual Learning
The detailed continual learning configurations and synaptic network paper-parameters are summarized in T
Table 5: Detailed continual learning configurations and hyper-parameters of the synaptic network.
SSL Epoch
10
CL. Epocc
10
SSL Learning Rate
Ic-7
CL. Learning Rate
1c-7
Continual Learning
Optimizer
AdamW
Adam 3
(0.5, 0.99)
Weight Decay
3e-4
Batch
32
Dropout
ISRUC Connection Threshold ç FACED Connection Threshold
0.1
0.4
Physionet-Ne MI Connection Threshold
0.5
Time Domain Weight we Frequency Domain Weight wy
0.9
1.5
Synaptic Network
Time- freee-Freueecc Domain Weight any
1.2
Importance Weight Top-K
0.2
Pscudo Label confidence threshold
0.9
Loss Function Weight B
0.7
Renormalization Decay Factor
Consolidation Factor
1.3
22

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Synaptic Network Visualization under Varying Connection Thresholds
Connection Threshold 0.1
Connection Threshold= 0.2
Connection Threshold 0.3
Connection Threshold 0.4
Connection Threshold 0.5
Connection Threshold 0.6
Figure 6: Visualization of the expanded synaptic network in the ISRUC dataset.
Connection Threshold - 0.1
Connection Threshold- - 0.2
Connection Threshold - 0.3
Connection Threshold
Connection Threshold
Connection Threshold
Figure 7: Visualization of the expanded synaptic network the FACED dataset.
Connection Threshold 0.1
Connection Threshold= = 0.2
Connection Threshold = 0.3
Connection Threshold 0.4
Connection Threshold - 0.5
Connection Threshold 0.6
Figure 8: Visualization of the expanded synaptic network in the Physionet-Mr dataset.
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Table 6: Overview of varying connection threshold ç experiment performance with 30% source domain proportion.
ISRUC
FACED
Physionet-Mi Average
Average ACC
Average
Average
Average
Average
0.1
75.5
71.1
43.7
41.8
48.7
47.86
0.2
74.9
70.7
43.9
42.0
48.8
47.92
0.3
71.0
43.6
41.5
48.6
47.75
0.4
74.1
70.0
44.2
42.3
48.6
47.71
0.5
73.4
69.4
43.7
41.5
48.9
48.11
73.9
69.7
43.5
41.4
47.77
We have visualized the synaptic networks derived from three datasets under varying connection thresholds Ç. as depicted the Fig. 6 Fig. 7 and Fig. g In the ISRUC resting-state sleep dataset, significant inter-individual variability in initial features causes sharp reduction in synaptic connections as the connection threshold increases. To preserve synaptic connection diversity, we configured lower threshold (i.e.. { = 0.1 ). In contrast, for task-based datasets such FACED and Physionet-MI, identical stimuli elicit initial features that result in overabundance of redundant synaptic connections at low thresholds (i.e.. < 0.3). Consequenlly, dataset-specific thresholds were established to filter low-quality synaptic connections. Detailed experimental results for the connection thresholds are summarized in Tab. O The results demonstrate that the SPICED framework achieves optimal performance across all datasets under these dataset-specific thresholds. Notably, visualization analyses reveal that synaptic network connection densities converge to a consistent level (i.e., the subplots highlighted by red boxes) when the optimal thresholds are applied for each dataset.
G Hyper-parameter Analysis of Importance Weight
In this section, we investigate the importance weight a to quantify the influence of inter-node similarity and average synaptic connection strength node importance, formulas in Eq. (2). As shown in Fig. 9 the experimental results demonstrate that SPICED achieves superior performance across datasets with smaller a values (where inter-node similarity receives lower weighting) compared scenarios with larger a values (where average synaptic connection strength is assigned reduced weights). To sum op, the average synaptic connection strength of target nodes exerts a greater influence on node importance compared to inter-node similarity, as evidenced by the following reasons. First, inter-node similarity is derived from individual initial features and remains unaffected by subsequent learning processes, thereby lacking the flexibility to dynamically assess node importance. Second. the average synaptic connection strength dynamically evolves through synaptic consolidation and renormalization. enabling more accurate reflection of node's significance within the synaptic network. A higher average synaptic connection strength signifies greater generalizability of node samples, and activating such nodes enhances the model's 's adaptability to new individuals. Based on the aforementioned analysis, we uniformly set a to 0.2 for all datasets.
ACC
MFI
Mome Vitee
75.0
43.5
49.0
73.5
22.0
48.0
40.5
47.5
0.3
0.2
0.5
0.8
0.5
0.5
0.7
0.8
0.9
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.1
0.
0.3
0.4
0.5
0.6
0.7
08
05
different datasets with 30% source domain proportion. The horizontal axis represents the values of a, ranging from 0.1 to 0.9 with a step size of 0.
Figure The analysis of hyper-parameter
H Visualization of the Synaptic Network Expansion Process
In this section, we visualize the dynamic synaptic network expansion processes across the ISRUC, FACED, and Physionet-MI datasets under varying source domain proportions, illustrated in Fig. 10 IT and Fig. [12] Observations from the visualizations reveal that certain critical nodes during the early stages of synaptic network expansion (i.e., larger node sizes) may gradually lose significance

---

Synaptic Network Expansion Stage 0%
Synaptic Network Expansion Stage 20%
Synuptic Network Expansion Stage 40%
Synaptic Network Expansion Stae- 60%
Synagric Network Expansion Stage 80%
Synaptic Network Expansion Stage 100%
Figure 10: Overview of the synaptic network dynamic expansion process the ISRUC dataset.
Synaptic Expansion Stage
Synaptic Expansion Stage
Symuptic Expension Staee
Synaptic Network Expansion Stage 60%
Synsptic Netwoek Expansion Stage 80%
Sycaptic Network Expansion Stage 100%
Figure 11: Overview of the synaptic network dynamic expansion process the FACED dataset.
Synaptic Network Expansion
Synaptic Network Expansion Stage 20%
Synaptic Network 40%
Synaptic Network Expansion Saage - 60%
Synaptic Network Expansion Siage 80%
Sysaptic Network Expension Sage 100%
Figure 12: Overview of the synaptic network dynamic expansion process on the Physionet-MI dataset.
the network expands, triggering synaptic consolidation and renormalization. Conversely, nodes
25

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initially less prominent (i.e., smaller node sizes) may gain increasing importance through progressive integration and connections with newly added nodes. This finding demonstrates that the proposed synaptic network is less susceptible to source domain node initialization, while exhibiting both stability and robustness.
I Ablation Study
To investigate the effectiveness of synaptic consolidation and synaptic renormalization in SPICED, we conducted ablation study. The ablated methods are as follows: w/o SR: only synaptic consolidation is adopted; w/o SC: only synaptic renormalization is adopted; SPICED: the framework with all components. Experimental results demonstrate that SPICED's performance degrades the absence of either synaptic consolidation synaptic renormalization. This further underscores the critical importance of synaptic homeostasis, arising from the interplay between synaptic consolidation and renormalization, to the SPICED framework highlighting the indispensability of both mechanisms.
ISRUC
FACED
Physionet-MI
76.5
44.8
49.0
ACC(%)
75.6
44.2
48.6
73.8
43.1
47.8
72.9
42.6
47.4
72.0
42.0
47.0
73.0
43.0
48.5
MF MF16% ((%)
71.4
41.4
47.5
70.6
40.6
47.0
69.8
39.8
46.5
69.0
39.0
46.0
w/o SR
w/o SC
SPICED
Figure 13: Overview performance comparison with ablated methods at 30% source domain pro- portions. Each ablated method was evaluated under the same dataset partitioning, with five runs conducted per method by randomly shuffling the input order of the incremental set enable statistical evaluation.
Computational Cost and Resource Requirement
As shown in the Tab. 7 the reported average time per individual includes the full adaptation process: synaptic node incorporation, critical memory reactivation, model training (including synaptic consolidation and weight renormalization) and evaluation. The reported storage refers to the disk storage usage per individual. These results demonstrate that SPICED achieves efficient adaptation with manageable computational overhead. And our model is trained on single machine equipped with an Intel Core i9 10900K CPU and eight NVIDIA RTX 3080 GPUs.
Table The computational and storage cost per individual.
ISRUC
FACED
Physionet-MI
Average Cost (minutes) Storage (M)
4.4240.55
4.1640.64 53.7
4.23+0.63
43.7
K Robustness Study
In this section, we evaluate the robustness of SPICED by adding Gaussian noise scaled 1%, 5%, and 10% of the original signal's standard deviation during training phase (i.e., progressively noisier conditions). shown in the Tab. g SPICED demonstrates its robustness such perturbations. Specifically, ISRUC, SPICED maintains an ACC of 73.8% and MF1 of 70.1% under 10% noise
26

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condition, exhibiting only marginal degradation compared to the clean setting. On FACED, the performance SPICED remains nearly invariant across all noise levels. And on Physionet-MI. SPICED shows virtually no performance drop across all noise levels, with fluctuations well within the standard error. These results indicate that SPICED is inherently robust to noisy EEG inputs and can effectively tolerate moderate input perturbations without significant performance loss- desirable property for real-world deployment in low-resource or ambulatory monitoring scenarios.
Table 8: The robustness study of SPICED three downstream EEG tasks under 30% source domain proportion.
ISRUC
FACED
Physionet
ACC
MFI
ACC
MFI
ACC
MFI
Moo
M,
Ma
M,
MMg
M,
Ma
M,
MM
M.
Mu
M,
1% Noise 66.8
74.0#0.24
60.5
70.2+0.21
31.7
43.1=0.41
27.2
40.820.40
42.2
48.720.14
37.9
47. 17.820.12
5%
Noise
66.8
74.2a0.08
60.5
70.4a0.21
31.7
43.2a0.29
27.2
40.920.36
42.2
48.620.14
37.9
47.8s0.14
10% Noise
66.8
73.840.26
60.5
70.140.24
31.7
43.040.21
27.2
40.8a0.24
42.2
48.7a0.05
37.9
47.8s0.08
Clean EEG
66.8
75.620.13
60.5
71.420.07
31.7
43.720.51
27.2
41.720.58
42.2
48.720.17
37.9
47.920.24
L Discussion
L.1
Implications
Our work bridges neurobiological principles with artificial continual learning through three key advances: (1) Mechanistic translation-formmliiin synaptic homeostasis into a neuromor- phic framework that balance stability dilemma biologically grounded consolidation- renormalization dynamics; (2) BCI enaaaiion-eenallngg individual-speciff synaptic network ex- pansion to address inder-lndividual variability in EEG decoding, supporting continual individual adaptation; (3) Generalizability
validaadddte across three different EEG tasks, we demonstrate that bio-inspired synaptic homeostasis mechanism enhances both plasticity for novel individuals and robustness against error accumulation.
L.2 Limitations and Future Work
In this work, we explores synaptic homeostasis-inspiee framework for unsupervised continual EEG decoding and validates its efficacy across diverse downstream EEG tasks, demonstrating promising performances. However, the following limitations remain: First, modern neuroscience still an early stage of understanding the brain, with many neurobiological mechanisms poorly characterized. Consequently, the proposed SPICED framework only approximates the fundamental principles of synaptic homeostasis and does not model deeper neurobiological mechanisms. Second, we adopted unified initial feature extraction paradigm across different EEG tasks without accounting for inter-task variability in EEG features. Task-specific feature extraction strategies could potentially enable more accurate quantification inter-individull similarity. future work, we aim to further investigate neurobiological mechanisms and explore brain-inspired algorithms in broader domains to bridge the gap between biological neural mechanisms and artificial intelligence systems.
27


Reviews:
[{"review_id": "dY0waLL3rt", "reviewer": "Reviewer_EKab", "summary": "This paper presents SPICED, a neuromorphic framework designed for unsupervised continual EEG decoding in dynamic, real-world scenarios where new individuals with distinct EEG patterns are continuously introduced. Inspired by the synaptic homeostasis mechanism in the human brain, SPICED addresses the long-standing stability-plasticity dilemma in continual learning.\n\nBy bridging biological principles with neuromorphic continual learning, SPICED opens new directions for bio-inspired unsupervised learning systems, particularly in brain–computer interface (BCI) and non-stationary signal decoding applications.", "questions": "1) To what extent are the bio-inspired mechanisms in SPICED necessary for performance, versus engineering design choices that could be replaced with other techniques (e.g., replay buffers, attention-based routing)?\n\n2) How does SPICED handle extremely noisy EEG signals, domain shifts across populations (e.g., clinical vs. healthy), or longer task sequences?", "limitations": "yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Quality: The paper presents a well-motivated and carefully implemented framework for continual learning in EEG decoding, grounded in biological principles.\n\nClarity: The paper is clearly written and well-organized. Key concepts such as critical memory reactivation, consolidation, and renormalization are clearly introduced and motivated through biological analogies.\n\nWeaknesses: The paper does not provide details on the computational overhead or resource requirements of SPICED, particularly in terms of memory expansion and replay mechanisms. Understanding the scalability of the model is crucial for real-time BCI applications.\n\nThe paper does not analyze where SPICED may fail or behave suboptimally—e.g., in the presence of low-quality EEG signals, highly non-overlapping user domains, or very long task sequences.", "was_revised": true, "final_justification": "The topic is engaging and of clear interest to the community. While the work presents promising ideas, the current version has notable gaps in completeness and development. Following the rebuttal and discussions, some clarifications were provided. Balancing the novelty of the topic with the present limitations, I recommend a Borderline accept.", "rebuttal": "**We'd like to express our sincere gratitude for your careful readings and valuable comments.** We provide our feedbacks as follows.\n\n## Main Comments:\n\n> #### **Q1: The paper does not provide details on the computational overhead or resource requirements of SPICED.**\n\n**A1: Thanks for this constructive comment.** We have added new experiments to evaluate the computational cost of SPICED. Specifically, we report the average time and storage required for SPICED to adapt to each new individual in the continual learning pipeline. As shown in the table below, **the reported average time per individual includes the full adaptation process**: synaptic node incorporation, critical memory reactivation, model training (including synaptic consolidation and weight renormalization) and evaluation. The reported storage refers to the disk storage usage per individual. These results demonstrate that SPICED achieves efficient adaptation with manageable computational overhead. And our model is trained on a single machine equipped with an Intel Core i9 10900K CPU and eight NVIDIA RTX 3080 GPUs.\n\n|     | ISRUC | FACED | Physionet-MI |\n| --- | --- | --- | --- |\n| Average Cost (minutes) | 4.42±0.55 | 4.16±0.64 | 4.23±0.63 |\n| Storage (M) | 47.1 | 53.7 | 43.7 |\n\n---\n\n> #### **Q2: How does SPICED handle extremely noisy EEG signals, domain shifts across populations (e.g., clinical vs. healthy), or longer task sequences?**\n\n**A2: We thank the reviewer for raising this important point about the robustness of SPICED under extreme conditions.** We address the three extreme scenarios raised regarding the robustness of SPICED as follows:\n\n1. **Condition of Noisy EEG Signals:** We have added Gaussian noise scaled to 1%, 5%, and 10% of the original signal’s standard deviation during training phase (i.e., progressively noisier conditions). **Due to time constraints, we conducted experiments under the source domain split ratio of 30%**, with each experiment repeated for 3 runs to ensure reliable statistical evaluation. And the results are summarized in the table below. Results are reported for $M_0$​ (initial source model) and $M_i$ (adapted individual model) across all the three datasets.\n  \n  | ISRUC | ACC $M_0$ | ACC $M_i$ | MF1 $M_0$ | MF1 $M_i$ |\n  | --- | --- | --- | --- | --- |\n  | 1% Noise | 66.8 | 74.0±0.24 | 60.5 | 70.2±0.21 |\n  | 5% Noise | 66.8 | 74.2±0.08 | 60.5 | 70.4±0.21 |\n  | 10% Noise | 66.8 | 73.8±0.26 | 60.5 | 70.1±0.24 |\n  | Clean EEG | 66.8 | 75.6±0.13 | 60.5 | 71.4±0.07 |\n  \n  | FACED | ACC $M_0$ | ACC $M_i$ | MF1 $M_0$ | MF1 $M_i$ |\n  | --- | --- | --- | --- | --- |\n  | 1% Noise | 31.7 | 43.1±0.41 | 27.2 | 40.8±0.40 |\n  | 5% Noise | 31.7 | 43.2±0.29 | 27.2 | 40.9±0.36 |\n  | 10% Noise | 31.7 | 43.0±0.21 | 27.2 | 40.8±0.24 |\n  | Clean EEG | 31.7 | 43.7±0.51 | 27.2 | 41.7±0.58 |\n  \n  | Physionet-MI | ACC $M_0$ | ACC $M_i$ | MF1 $M_0$ | MF1 $M_i$ |\n  | --- | --- | --- | --- | --- |\n  | 1% Noise | 42.2 | 48.7±0.14 | 37.9 | 47.8±0.12 |\n  | 5% Noise | 42.2 | 48.6±0.14 | 37.9 | 47.8±0.14 |\n  | 10% Noise | 42.2 | 48.7±0.05 | 37.9 | 47.8±0.08 |\n  | Clean EEG | 42.2 | 48.7±0.17 | 37.9 | 47.9±0.24 |\n  \n  As shown in the tables, SPICED exhibits **remarkable robustness** to noise:\n  \n  - **On ISRUC**, even under 10% added noise, SPICED​ maintains an ACC of 73.8 and MF1 of 70.1, with only marginal degradation compared to the clean condition (75.6 ACC, 71.4 MF1).\n  - **On FACED**, SPICED shows nearly identical performance across all noise levels, with $M_i$ ACC fluctuating within ±0.7 of the clean setting (43.7 ± 0.51), and MF1 within ±0.9.\n  - **On Physionet-MI**, performance is virtually unaffected across all noise levels, with deviations well within standard error.\n  \n  **This suggests that SPICED is inherently robust to noisy EEG signals**, and can effectively handle moderate levels of input perturbations without significant performance degradation.\n  \n2. **Condition of Domain Shifts:** In fact, due to significant inter-subject variability, substantial domain shift can exist even within the same population, **which is particularly evident in outlier subjects**. This is discussed in Section 4.2.1, where Figure 3 shows that each dataset contains outliers whose distributions deviate noticeably from the overall data distribution and exhibit significant domain shift. When encountering a new individual with significant domain shift, **SPICED integrates information across the holistic synaptic network for adaptation**, rather than relying on incremental updates from the previous time step. By leveraging global historical information, SPICED effectively mitigates the limitations of prior methods, which often fail to adapt to outliers exhibiting large domain shifts.\n  \n3. **Condition of Longer Task Sequences:** Regarding dataset selection, we have evaluated SPICED on three **large-scale datasets** composed of at least **100** subjects to validate its performance under long task sequences. While many publicly available EEG datasets consist of only dozens of subjects, our choice of large-scale benchmarks enables a more realistic and rigorous evaluation of long-term individual continual learning. Besides, as discussed in Section 4.2.1, we further analyze SPICED’s behavior under various source/target subject splits. Notably, in the 10% split setting, **the number of individuals in the task sequence is nine times the number of pretraining individuals.** For instance, in FACED, only **12** subjects are assigned to the pretraining set, while the remaining **111**subjects form the long sequential stream for continual learning. We believe this constitutes a sufficiently long and challenging continual learning trajectory, and thus provides strong evidence for SPICED’s effectiveness in long-term continual learning scenarios.\n  \n\n**We hope that these points will address your concerns.** Thank you once again for your insightful feedback.\n\n---\n\n> #### **Q3: To what extent are the bio-inspired mechanisms in SPICED necessary for performance, versus engineering design choices that could be replaced with other techniques (e.g., replay buffers, attention-based routing)?**\n\n**A3: Thank you for this insightful and constructive question.** We clarify SPICED’s necessity through its core innovation and its implications for prior approaches:\n\n1. **Innovations of SPICED:** Our framework introduces an auxiliary synaptic network that **aggregates global historical information** (across all prior individuals) for decision-making when adapting to new tasks and activates **the most relevant memories**(e.g., critical memory replay, critical model fusion) to assist adaptation. The selection of critical memory is enabled by the following two operations:\n  \n  - Task-discriminative memory strengthening via **synaptic consolidation**, and\n  - Redundant noisy memory weakening via **synaptic renormalization**.\n2. **Limitations of Traditional Techniques:** The proposed replay buffers or attention-based routing methods necessarily **depend on the model’s immediate prior state**. When significant domain shift occurs between tasks (e.g., due to inter-subject EEG variability), these methods lose access to globally relevant historical knowledge—making them sensitive to the learning trajectory and prone to performance degradation. Crucially, without global memory aggregation, existing methods (e.g., replay buffer-based approaches) **cannot selectively retrieve and replay the most task-discriminative memories** from arbitrary points in the past.\n  \n3. **Resolution via SPICED:** We translate the synaptically inspired mechanism into a continual learning framework, overcoming the aforementioned limitations. Our synaptic network:\n  \n  - Maintains a consolidated memory pool spanning all historical subjects\n  - Dynamically reactivates only task-critical memories during adaptation\n  - Ensures robustness to continual domain shifts\n\nTo sum up, SPICED is not replaceable by conventional techniques—it solves a fundamental limitation in continual learning under cross-subject domain shifts.\n\n**We appreciate your insightful question and hope this clarifies the necessity of SPICED’s bio-inspired design compared to purely engineering-driven alternatives.**"}, {"review_id": "xtu0RFcHq1", "reviewer": "Reviewer_yKhn", "summary": "This paper presents a biologically inspired continual learning framework for EEG decoding, motivated by synaptic homeostasis principles. The model incrementally adapts by: (1) initializing a base network, (2) incorporating new neurons based on pairwise similarity with existing representations, (3) consolidating new and old knowledge through model fusion, and (4) applying renormalization for adaptability. These steps are clearly explained and visually illustrated. The method is evaluated on three EEG datasets and compared against several recent baselines (e.g., MMD, EWC, UCL-GV, ReSNT, BrainUICL) using metrics like accuracy and macro-F1. The results demonstrate competitive performance, and the authors include ablation studies to support design choices.", "questions": "1. Could you briefly describe the baseline methods (e.g., BrainUICL, UCL-GV, ReSNT) in the related work section to better frame your contribution? A comparison of what their identified gaps are and how your method takes it forward.\n2. Are all hyperparameters (including learning rates, weight terms, and consolidation thresholds) tuned individually for each dataset? If so, how feasible is this setup for real-world deployment?\n3. Could you provide a main table with the exact numerical values for accuracy and macro-F1, including standard deviations or confidence intervals, instead of only bar plots?", "limitations": "Yes.", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n\n1. The paper is very well-written and organized, with each methodological step presented clearly in logical subsections.\n2. The main algorithm is biologically intuitively explained and supported by a clear diagram that improves understanding.\n3. The evaluation is comprehensive: the proposed approach is compared against several relevant baselines and tested across three EEG datasets.\n4. The proposed method is more real-world oriented and this makes the work practically meaningful and applicable beyond standard benchmarks.\n\nWeaknesses:\n\n1. The related work section does not contextualize the compared baselines (e.g., BrainUICL, ReSNT, UCL-GV); instead, these are only introduced in the experiments. Briefly describing them earlier would better highlight the novelty and motivation of the proposed method. As in what is the gap in the other methods that this work is filling.\n2. The performance metrics (accuracy and macro-F1) are not defined anywhere. A brief explanation would be helpful for readers unfamiliar with these metrics.\n3. The main comparison table against the recent benchmarks is shown only as bar plots, without numerical values, standard deviations, or statistical significance reporting, which limits interpretability and reproducibility.\n4. It seems like a lof of tunable hyperparameters according to the appendix. It's unclear whether all the 10-15 hyperparameters mentioned are tuned exhaustively or selectively.", "was_revised": true, "final_justification": "My initial borderline accept rating stemmed from concerns about missing baseline descriptions and gap analysis, the tuning of hyperparameters per dataset and its real-world feasibility, and the lack of a main table with exact numerical results and variability measures. In the rebuttal, the authors provided detailed clarifications, including a direct comparison against the most relevant recent baseline (CoUDA, 2025), where their method outperforms on 2 of the 3 datasets. They also explained that their approach does not rely on previous inputs like other methods, instead leveraging global statistical patterns, and showed that only one hyperparameter was tuned per dataset while others remained fixed - strengthening claims of robustness. These responses address my main doubts and increase my confidence in the empirical validity of the work. Given the novelty, practical applicability, and clarified experimental rigor, I am raising my score to an accept.", "rebuttal": "**Many thanks for the your detailed and insightful comments. We are glad to see you approved the contributions of our work.** We provide our feedbacks as follows:\n\n## Main Comments\n\n> #### **Q1: Could you briefly describe the baseline methods (e.g., BrainUICL, UCL-GV, ReSNT) in the related work section to better frame your contribution? A comparison of what their identified gaps are and how your method takes it forward.**\n\n**A1: Thanks for your valuable comments.** We have revised a portion of the Related Work to emphasize our contributions, highlighting the following points:\n\n1. **Limitations of Traditional Continual EEG Decoding Methods:** \"(Sec.2.3) ... in clinics. This limitation has motivated recent efforts to explore individual-specific continual EEG decoding algorithms. UCL-GV [1] employs a FIFO-based buffer and contrastive alignment strategies to mitigate domain shift. ReSNT [2] introduces a dynamically evolving replay strategy for continuous EEG decoding. BrainUICL [3] proposes an unsupervised individual continual learning framework to address the stability-plasticity dilemma in CL. **However, these methods—grounded in incremental model evolution—exhibit strong dependence on the model state from the previous time step**, limiting their ability to cope with large, sustained domain shifts.\"\n2. **Innovations of SPICED:** \"(Sec.2.3)... domain shifts. By contrast, **SPICED aggregates global historical information for decision-making** when adapting to new tasks and activates the most relevant memories to assist adaptation. It overcomes the limitations of incremental model evolution, ensuring robustness against domain shifts while maintaining performance.\"\n\n[1] Abu Md Niamul Taufique, Chowdhury Sadman Jahan, and Andreas Savakis. Unsupervised continual learning for gradually varying domains. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 3740–3750, 2022.\n\n[2] Tiehang Duan, Zhenyi Wang, Gianfranco Doretto, Fang Li, Cui Tao, and Donald Adjeroh. Replay with stochastic neural transformation for online continual eeg classification. In 2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pages 1874–1879.IEEE, 2023.\n\n[3] Yangxuan Zhou, Sha Zhao, Jiquan Wang, Haiteng Jiang, Shijian Li, Tao Li, and Gang Pan. Brainuicl: An unsupervised individual continual learning framework for eeg applications. In The Thirteenth International Conference on Learning Representations.\n\n---\n\n> #### **Q2: The performance metrics (accuracy and macro-F1) are not defined anywhere.**\n\n**A2: We thank the reviewer for the valuable suggestion.** We have added the detailed definitions of accuracy and macro-F1 metrics in Section 4.1 as follows:\n\n$$\n\\text{Accuracy} = \\frac{1}{N} \\sum_{i=1}^{N} \\mathbb{1}(y_i = \\hat{y}_i)\n$$\n\nwhere $N$ is the total number of samples, $y_i$ is the true label, $\\hat{y}_i$ is the predicted label, and $\\mathbb{1}(\\cdot)$ is the indicator function.\n\n$$\n\\text{Macro-F1} = \\frac{1}{C} \\sum_{c=1}^{C} \\frac{2 \\cdot \\text{Precision}_c \\cdot \\text{Recall}_c}{\\text{Precision}_c + \\text{Recall}_c}\n$$\n\nwhere $C$ is the number of classes, and $\\text{Precision}_c$ and $\\text{Recall}_c$ are the precision and recall for class $c$, respectively.\n\n---\n\n> #### **Q3: Could you provide a main table with the exact numerical values for accuracy and macro-F1, including standard deviations or confidence intervals, instead of only bar plots?**\n\n**A3: Many thanks for your constructive suggestions.** In response, we have replaced Figure 4 with a tabular format to better present the results, as shown below. Due to space constraints in the rebuttal, we have split the table into three parts for clarity.\n\n|     | **ISRUC** |     | **FACED** |     | **Physionet-MI** |     |\n| --- | --- | --- | --- | --- | --- | --- |\n| **10%** | ACC | MF1 | ACC | MF1 | ACC | MF1 |\n| MMD | 57.0±0.47 | 50.0±0.65 | 24.1±0.24 | 20.9±0.34 | 37.3±0.34 | 34.3±0.49 |\n| EWC | 60.6±0.71 | 54.1±0.62 | 22.3±0.36 | 19.0±0.49 | 42.8±1.19 | 40.6±2.19 |\n| UCL_GV | 52.5±1.16 | 43.4±1.07 | 25.0±0.43 | 21.9±0.51 | 33.1±0.35 | 24.8±0.51 |\n| ReSNT | 56.4±0.70 | 49.3±1.12 | 14.1±1.66 | 7.1±2.56 | 35.9±1.49 | 30.4±1.79 |\n| BrainUICL | 56.9±0.46 | 48.4±0.68 | 21.2±2.04 | 16.9±2.55 | 43.1±0.33 | 41.5±0.36 |\n| **CoUDA** | 52.7±0.44 | 44.5±0.48 | 23.9±0.16 | 20.5±0.17 | 41.6±0.25 | 38.7±0.39 |\n| **SPICED** | **62.6±1.35** | **55.4±1.51** | **25.7±0.10** | **22.7±0.17** | **43.4±0.44** | **42.0±0.46** |\n\n|     | **ISRUC** |     | **FACED** |     | **Physionet-MI** |     |\n| --- | --- | --- | --- | --- | --- | --- |\n| **30%** | ACC | MF1 | ACC | MF1 | ACC | MF1 |\n| MMD | 70.2±0.83 | 64.9±0.83 | 39.0±1.48 | 35.8±1.66 | 44.1±0.38 | 41.7±0.57 |\n| EWC | 71.3±0.49 | 67.3±0.40 | 39.5±0.94 | 37.0±1.39 | 47.3±0.58 | 45.2±0.75 |\n| UCL_GV | 72.5±0.32 | 68.1±0.45 | 41.4±1.50 | 38.9±1.59 | 39.7±0.22 | 35.8±0.40 |\n| ReSNT | 71.3±0.89 | 67.7±0.78 | 33.3±8.10 | 29.1±9.74 | 44.1±0.50 | 43.1±0.49 |\n| BrainUICL | 74.8±0.11 | 70.8±0.25 | 33.8±2.90 | 29.7±3.55 | 48.3±0.38 | 46.8±0.38 |\n| **CoUDA** | 73.3±0.23 | 69.2±0.23 | 39.2±1.06 | 36.2±1.05 | 44.2±0.31 | 41.1±0.43 |\n| **SPICED** | **75.6±0.13** | **71.4±0.07** | **43.7±0.51** | **41.7±0.58** | **48.7±0.17** | **47.9±0.24** |\n\n|     | **ISRUC** |     | **FACED** |     | **Physionet-MI** |     |\n| --- | --- | --- | --- | --- | --- | --- |\n| **50%** | ACC | MF1 | ACC | MF1 | ACC | MF1 |\n| MMD | 68.2±1.00 | 62.2±1.37 | 19.1±2.59 | 10.0±3.09 | 47.9±0.54 | 45.9±0.63 |\n| EWC | 72.5±0.39 | 67.2±0.80 | 48.7±0.66 | 46.7±1.63 | 50.2±0.43 | 48.0±0.64 |\n| UCL_GV | 73.9±0.29 | 68.4±0.26 | 22.8±3.64 | 14.4±4.33 | 45.5±0.71 | 42.6±0.58 |\n| ReSNT | 72.1±0.69 | 66.5±0.77 | 23.6±3.29 | 15.5±3.96 | 48.8±0.28 | 45.7±0.48 |\n| BrainUICL | 73.6±0.29 | 67.7±0.29 | 20.2±3.46 | 12.0±3.92 | **52.5±0.36** | **51.3±0.48** |\n| **CoUDA** | 72.3±0.94 | 66.4±0.73 | 22.5±2.61 | 13.7±3.31 | 51.4±0.16 | 49.6±0.16 |\n| **SPICED** | **74.5±0.39** | **69.2±0.37** | **48.9±0.42** | **47.0±0.50** | 52.4±0.11 | 51.2±0.14 |\n\n     Furthermore, we have incorporated **CoUDA[1]**, a recent method for continual domain adaptation, as an additional baseline. As shown in the results, SPICED consistently outperforms existing approaches across various data splits, demonstrating strong stability and robustness. For instance, under low-resource settings, most methods suffer performance degradation compared to their initial performance, whereas SPICED is able to further enhance model adaptability even under poor initialization. On the 50% split of FACED, where significant performance drops are observed for most methods—likely due to continuous and substantial domain shifts—SPICED maintains robust performance. This is attributed to its critical memory reactivation mechanism that **aggregates global historical information**, effectively mitigating the impact of domain shifts and improving model adaptability across diverse individuals.\n\n**We thank the reviewer once again for this valuable suggestion.** We hope that this revision provides a clearer overview and addresses your concern.\n\n[1]Chen B, Zhang X, Shen C, et al. CoUDA: Continual Unsupervised Domain Adaptation for Industrial Fault Diagnosis Under Dynamic Working Conditions[J]. IEEE Transactions on Industrial Informatics, 2025.\n\n---\n\n> #### **Q4: Are all hyperparameters (including learning rates, weight terms, and consolidation thresholds) tuned individually for each dataset? If so, how feasible is this setup for real-world deployment?**\n\n**A4: We thank the reviewer for the valuable and insightful question.** To clarify, **all the hyperparameters**—including learning rates, weight terms, and other optimization settings—are **kept consistent across all datasets**. The **only exception is the synaptic connection threshold $\\xi$** , which is adjusted according to the intrinsic characteristics of each dataset, as detailed in Section 4.2.1 and Appendix F.\n\nThe rationale for this adjustment is grounded in the nature of the data:\n\n- For **resting-state datasets** such as ISRUC, significant inter-individual variability in initial individual features necessitates a **lower threshold** $\\xi$ to ensure sufficient synaptic connection density, thereby preserving meaningful individualized patterns.\n- In contrast, for **task-based datasets** like FACED and PhysioNet-MI, **participants are exposed to identical stimuli**, leading to more homogeneous initial representations across individuals. In this case, a **higher threshold** $\\xi$ is adopted to filter out redundant, overly similar connections while retaining those that are discriminative at the individual level.\n\nThe adaptive setting of the connection threshold involves only a lightweight, one-time calibration based on data modality (resting-state vs. task-based), which is justified since high-level experimental conditions—such as task design—are typically known prior to deployment. Importantly, all the other hyperparameters are kept fixed across datasets and experiments. Given this minimal adaptation and consistent configuration, our approach **remains practical and highly feasible for real-world deployment scenarios.**\n\n**We hope this clarification addresses the reviewer’s concern. We thank the reviewer once again for your thoughtful and valuable feedback.**"}, {"review_id": "o9IstvyaWi", "reviewer": "Reviewer_zYsa", "summary": "The authors propose a biologically inspired method for continually learning framework called SPICED from EEG signals that balances learning new information, preserving important past memories. This framework has three key processes: critical memory reactivation that replays relevant past knowledge, synaptic consolidation that strengthens important connections, and finally synaptic renormalization that weakens and reduces the plasticity of less useful connections, enabling adaptability and memory retention. It represents each individual as a node in a dynamic synaptic network, connecting new individuals to similar past ones based on EEG feature similarity by fusing information from multiple relevant models and trains on pseudo-labelled data, The framework is validated on three diverse EEG tasks: sleep staging, emotion recognition, and motor imagery.", "questions": "•Can you report forward transfer (FWT) and backward transfer (BWT) which are standard metrics in continual learning for quantifying positive transfer and forgetting? Including these would strengthen your empirical claims and enable fairer comparison with existing literature. Additionally, can you evaluate SPICED against more recent continual learning methods? \n\n•The paper uses mechanisms like exponential decay and weighted averaging to simulate synaptic forgetting and fusion. How closely do these mechanisms align with actual neural processes? Can you clarify which aspects of synaptic homeostasis are modelled vs abstracted, and cite supporting neuroscience literature. \n\n•Can the authors provide explanations or visualizations of the synaptic trace evolution, model fusion weights, or importance scores? Additionally how does SPICED model perform when the source individual pool is small, noisy, or low quality?\n\n•Can SPICED generalize to non-EEG domains such as sensor data, speech, or time-series forecasting? Please discuss how the core mechanisms such as synaptic network, consolidation, renormalization might transfer to other modalities, and what adaptations might be required. Addressing this would broaden the significance and applicability of your approach.", "limitations": "Yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths: \n\n•SPICED framework is based on synaptic homeostasis demonstrating both synaptic consolidation (strengthening) and renormalization (forgetting), supporting selective replay, model fusion, and adaptive forgetting, without modifying the core model architecture.\n\n•The framework introduces a synaptic network, where each node represents an individual, edges reflecting similarity and memory strength, allowing to selectively reactivate and replay task relevant past individuals based on importance scores combining similarity and synaptic strength.\n\n•It initializes new models by fusing multiple past models using importance-weighted aggregation and simulates forgetting using time dependent synaptic decay, stimulating biologically plausible memory dynamics.\n\n•The framework simulates biologically plausible forgetting through time-dependent synaptic decay and provides interpretable outputs like synaptic strength trajectories, network visualizations that  help understand what the model remembers and forgets during continual learning.\n\n•The framework has been validated on three EEG datasets: ISRUC (sleep), FACED (emotion), and PhysioNet-MI (motor imagery) with ablation studies, hyperparameter analysis, and visualizations of memory dynamics.\n\nWeaknesses:\n\n•Although SPICED is presented as an unsupervised CL method, it relies on labelled source-domain individuals for initialization and pretraining. This makes it semi-supervised in practice, and the unsupervised claim is misleading without further clarification.\n\n•The paper does not report forward transfer (FWT) or backward transfer (BWT) metrics, which are widely used in continual learning to quantify learning and forgetting. Their inclusion would offer deeper insight into SPICED’s stability plasticity behavior.\n\n•Although biologically inspired, the implementation simplifies complex neural phenomena as an example, by using exponential decay for forgetting, model fusion as weighted averaging. The biological claims are not validated against neuroscience literature.\n\n•The effectiveness of SPICED depends on the quality and diversity of the labelled source individuals and the performance under low-resource or poor initialization scenarios is not deeply analyzed.\n\n•Most baselines are either EEG-specific or older CL methods like EWC and MMD. The paper does not compare against recent strong replay-based or dynamic CL methods, including more recent continual learning techniques (e.g., replay-based or dynamic architectures) would strengthen claims.", "was_revised": true, "final_justification": "After closely examining the rebuttal and other reviewers’ comments, I conclude that most of my concerns have been addressed; accordingly, I will revise my score to Borderline Accept.", "rebuttal": "**We'd like to express our sincere gratitude for your careful readings and helpful comments.** We provide our feedbacks as follows.\n\n## Main Comments\n\n> #### **Q1: SPICED relies on labelled source-domain individuals for initialization and pretraining. The unsupervised claim is misleading without further clarification.**\n\n**A1: Many thanks for pointing out this.** We agree and we have refined the part of Introduction to better contextualize the unsupervised claim and avoid potential misinterpretation. Specifically, as stated in Section 1 (first paragraph):\n\n> \"...especially in non-stationary continual EEG decoding scenarios **where the pretrained source model is required to adapt to unseen individuals continuously.**\"\n\n---\n\n> #### **Q2: Can you report forward transfer (FWT) and backward transfer (BWT) which are standard metrics in continual learning for quantifying positive transfer and forgetting?**\n\n**A2: We thank the reviewer for the valuable suggestion.** However, FWT and BWT are ill-suited for the individual continual learning setting, which fundamentally differs from standard CL process(e.g., class-incremental, task-incremental). The reasons are as follows:\n\n1. **Different Core Objectives:** SPICED addresses individual continual learning, not standard CL scenarios (e.g., class- or task-incremental learning) where models must retain performance on discrete, fixed tasks. In real-world deployment, **the goal is not to memorize every past individual, but to robustly adapt to each new subject** while mitigating error accumulation—precisely the stability-plasticity balance achieved through synaptic homeostasis. Our focus is on continual personalization, not task-level transfer. The core challenge is preventing catastrophic forgetting of learning capacity, not preserving fixed task outputs. SPICED meets this via dynamic synaptic consolidation and renormalization, enabling sustained adaptation across individuals—without requiring perfect recall of prior ones.\n  \n2. **Different Model Architecture:** In traditional CL, a single backbone model undergoes incremental adaptation across tasks, enabling BWT/FWT to measure performance changes on a fixed model. In contrast, SPICED employs a dynamically expanding synaptic network, where each individual has a dedicated node (storing model, connections). The current model is derived by fusing **global historical information** via importance-weighted aggregation—not by sequential fine-tuning of a single backbone. Consequently, evaluating this fused model on past or future individuals is not meaningful. Our evaluation—measuring adaptation gain and long-term stability—directly reflects the framework’s goal: continual personalization under distribution shift, not task-level transfer or fixed-model retention.\n\n**Thanks once again for your constructive suggestions. We hope our clarification adequately addresses your concerns.**\n\n---\n\n> #### **Q3: Can you clarify which aspects of synaptic homeostasis are modelled vs abstracted, and cite supporting neuroscience literature.**\n\n**A3: Thank you for raising this profound and crucial question.** We fully agree that biological plausibility is a central concern for brain-inspired computing frameworks. In SPICED, the employed exponential decay and weighted averaging mechanisms are not arbitrary designs, but rather principled computational abstractions of key neural mechanisms underlying synaptic homeostasis. These approaches are grounded in well-established biological foundations, as detailed below:\n\n1. **Exponential Decay based Synaptic Renormalization:** We emphasize that the exponential decay mechanism (Eq. 6) is directly inspired by the **Ebbinghaus forgetting curve [1]**. Neuroscientific studies have established that during slow-wave sleep, the brain undergoes a global, proportional weakening of synaptic strength—a process known as **synaptic downscaling [2]**, which serves the critical function of preventing synaptic saturation and restoring learning capacity. Our exponential decay formulation models this **global downscaling effect**, with its time-dependent decay rate aligned with the Ebbinghaus curve, a well-validated model of natural memory decay [1]. Importantly, this is not merely a heuristic choice but rather a **principled mathematical characterization** of the passive, time-dependent weakening of synaptic strength observed in biological systems.\n  \n2. **Critical Memory Reactivation based Weighted Fusion:** The weighted averaging in model fusion (Eq. 3) is not ad hoc, but a principled abstraction of the **brain’s functional specificity [3]**, where task-relevant neural ensembles are selectively recruited rather than uniformly activating all memories. Our importance coefficient (Eq. 2) combines individual similarity (initial feature similarity) and historical activation strength (average connection strength). The former models representation-based memory retrieval, while the latter reflects the **cumulative synaptic plasticity state (e.g., LTP/LTD effects) [4]**. This “relevance + stability”-based weighting constitutes a biologically grounded mechanism for selective information integration, consistent with neurocognitive principles.\n  \nWe do not simulate molecular-level ion channel dynamics or precise spike timing, as our goal is to build a scalable, functionally equivalent computational framework—not a biological simulator. The LTP and LTD mechanisms constitute the core computational primitives of synaptic plasticity. **We abstract these mechanisms into general principles for continual learning, preserving biological plausibility while effectively addressing practical challenges in continual EEG decoding.**\n\n[1] Hermann Ebbinghaus. Über das gedächtnis: untersuchungen zur experimentellen psychologie. Duncker & Humblot, 1885.\n\n[2] Giulio Tononi and Chiara Cirelli. Sleep and synaptic homeostasis: a hypothesis. Brain research bulletin, 62(2):143–150, 2003.\n\n[3] Karl Friston. A theory of cortical responses. Philosophical transactions of the Royal Society B:Biological sciences, 360(1456):815–836, 2005.\n\n[4] Robert C Malenka, Nicoll, and Roger A. Long-term potentiation–a decade of progress? Science, 285(5435):1870–1874, 1999.\n\n---\n\n> #### **Q4: Can the authors provide explanations or visualizations of the synaptic trace evolution, model fusion weights, or importance scores?**\n\n**A4: We thank the reviewer for this valuable question.** Visualizations and analyses of synaptic trace evolution and average synaptic connection strength are already provided in **Appendix H and I (page18-19)**, respectively. Due to submission constraints in the rebuttal phase, we are unable to include figures here. Regarding model fusion weights and importance scores, we will include corresponding visualizations and analysis in the appendix of the revised manuscript to further clarify their evolution.\n\n---\n\n> #### **Q5: The effectiveness of SPICED depends on the quality and diversity of the labelled source individuals and the performance under low-resource or poor initialization scenarios is not deeply analyzed.**\n\n**A5: Thank for your meaningful comment.** We have indeed evaluated SPICED under low-resource and poor initialization settings, as detailed in **Table 2 (page 8)**. Specifically, we test source domain proportions of 10%, where labeled source individuals are extremely limited (with incremental individuals outnumbering source ones by 9×). This setting leads to **severely degraded initial model performance** (e.g., $M_0$ ACC = 56.8% on ISRUC at 10%), reflecting a challenging poor-initialization regime. However, under such conditions, SPICED achieves **consistent performance gains** after adaptation (e.g., +5.4 ACC and +7.0 MF1 on ISRUC), while most baseline methods exhibit significant performance degradation when pretrained with only 10% source data, as shown in Figure 4 (page8).\n\n---\n\n> #### **Q6: Can SPICED generalize to non-EEG domains such as sensor data, speech, or time-series forecasting?**\n\n**A6: We thank the reviewer for this insightful question regarding the generalizability of SPICED.** While our work focuses on EEG decoding, the core mechanisms of SPICED—synaptic network construction, critical memory reactivation, consolidation, and renormalization—are **modality-agnostic** principles inspired by universal neural computation mechanisms, and thus hold strong potential for transfer to other sequential, individualized, or domain-shift-prone settings. For instance: In **wearable sensor data** (e.g., activity recognition), individuals exhibit high variability in motion patterns. SPICED could maintain a synaptic network of past users, selectively reactivating similar ones to bootstrap adaptation for a new user. Similarly, SPICED can be generalized to other specific scenarios where a sequential, individualized feature space exists. We will incorporate this discussion into the revised manuscript.\n\n---\n\n> #### **Q7: The paper does not compare against recent replay-based or dynamic CL methods. Can you evaluate SPICED against more recent continual learning methods?**\n\n**A7: Thanks for your helpful suggestion.** We have incorporated **CoUDA**, a recent method for continual domain adaptation, as an additional baseline.; due to space constraints in the rebuttal, we refer the reviewer to **Response A3 for Reviewer# yKhn**, which includes a detailed comparison table and analysis. Furthermore, we clarify that the compared methods ReSNT and BrainUICL are not generic CL or EEG-specific methods. **They are dynamic replay-based approaches designed for continual EEG decoding**, and published in 2023 and 2025, respectively. Our comparison is thus grounded in the most relevant and up-to-date literature for this specific problem setting.\n\n**We sincerely thank you again for your constructive comments.** We hope that this rebuttal has adequately addressed your concerns, and we would be happy to provide further clarification if you have any additional questions."}, {"review_id": "TO6P8KmIyx", "reviewer": "Reviewer_HgnD", "summary": "This paper presents a Synaptic Homeostasis-Inspired framework for Unsupervised Continual EEG Decoding (SPICED), which is a biologically inspired continual learning method.   This methodology involves three aspects: i) synaptic consolidation to reinforce critical memory traces, ii) critical memory reactivation to select and replay important traces, and iii) synaptic renormalization to allow learning from new sequences and reduce the effects of detrimental memory traces.", "questions": "Questions relate to the strengths and weaknesses above.  In particular:\n\n1. How does this work relate to existing methodologies, especially growing neural gas and self organizing maps?\n\n2. Can you clarify how this relates to standard continual learning scenarios (class incremental, task incremental, etc)?\n\n3. How was the individual data partitioned? Was data for individuals entirely left out for incremental learning?", "limitations": "Yes.", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 1, "significance": 3, "originality": 2, "strengths_and_weaknesses": "+ The results for this method show better performance to the other methods compared to.\n\n+ Datasets used in the work seem well selected.\n\n- Tests of statistical significance for this method versus the other methods would add strength to the results.\n\n- The methodology seems extremely similar to growing neural gas (a variant of self organizing maps), especially in terms of how nodes are connected and and the proposed “critical connection consolidation” is performed. The paper should be related to work in self organizing maps as much of this appears quite similar.\n\n- The paper claims that this is unsupervised continual learning, however they state that models (for new neurons) are pre-trained on data from new individuals - this is not unsupervised (as it does not appear that new individuals are not detected automatically, rather this information is provided to the methodology), nor does it match standard continual learning scenarios as new individuals are pre-trained on existing data.  It would be good to relate this work to existing continual learning scenarios (task incremental, class incremental, etc)\n\n- It seems that the dataset partitioning is in a sense cherry-picking the problem.  From the paper, each individual’s data is partitioned into a pre-training set and then an incremental set (to use after pretraining), this means the framework has already seen data from all individuals (which also doesn’t line up with continual learning).  Entire individuals should be held out for incremental learning so the performance of the framework could be shown in an actual continual learning scenario.  If individuals were being held out for the incremental version this should be more clearly stated.", "was_revised": true, "final_justification": "I would like to thank the authors for their detailed response. Given the clarification on their data preprocessing (training/validation data split) I've updated my rating of the paper.  The issue of similarity with growing neural gas still seems somewhat unresolved as while their method has a biological motivation it does seem the actual algorithm/methodology is quite similar (e.g., cosine similarity can be used as a distance metric for growing neural gas).", "rebuttal": "**We would like to express our sincere gratitude to you for taking the time to review our submission.** In this rebuttal, we will address each of the key issues and points you have raised.\n\n## Main Comments\n\n> #### **Q1: Tests of statistical significance for this method versus the other methods would add strength to the results.**\n\n**A1: We thank the reviewer for the valuable suggestion.** We have added statistical significance tests comparing SPICED with baseline methods, as summarized in the table below. We will include these results in Section 4.2.2 of the revised manuscript to ensure full transparency and reproducibility.\n\n| 10% | ISRUC (ACC/MF1) | FACED (ACC/MF1) | Physionet-MI (ACC/MF1) |\n| --- | --- | --- | --- |\n| **SPICED vs MMD** | p=0.003** / p=0.002** | p<0.001*** / p<0.001*** | p<0.001*** / p<0.001*** |\n| **SPICED vs EWC** | p=0.048* / p=0.042* | p<0.001*** / p<0.001*** | p=0.38 / p=0.25 |\n| **SPICED vs UCL_GV** | p=0.001** / p<0.001*** | p=0.032* / p=0.026* | p<0.001*** / p<0.001*** |\n| **SPICED vs ReSNT** | p=0.002** / p=0.003** | p<0.001*** / p<0.001*** | p=0.001** / p<0.001*** |\n| **SPICED vs BrainUICL** | p<0.001*** / p<0.001*** | p=0.006** / p=0.003** | p=0.31 / p=0.12 |\n\n| 30% | ISRUC (ACC/MF1) | FACED (ACC/MF1) | Physionet-MI (ACC/MF1) |\n| --- | --- | --- | --- |\n| **SPICED vs MMD** | p<0.001*** / p<0.001*** | p<0.001*** / p<0.001*** | p<0.001*** / p<0.001*** |\n| **SPICED vs EWC** | p<0.001*** / p<0.001*** | p<0.001*** / p<0.001*** | p=0.012* / p=0.008** |\n| **SPICED vs UCL_GV** | p<0.001*** / p<0.001*** | p=0.028* / p=0.019* | p<0.001*** / p<0.001*** |\n| **SPICED vs ReSNT** | p<0.001*** / p<0.001*** | p=0.003** / p=0.002** | p<0.001*** / p<0.001*** |\n| **SPICED vs BrainUICL** | p=0.002** / p=0.003** | p<0.001*** / p<0.001*** | p=0.043* / p=0.038* |\n\n| 50% | ISRUC (ACC/MF1) | FACED (ACC/MF1) | Physionet-MI (ACC/MF1) |\n| --- | --- | --- | --- |\n| **SPICED vs MMD** | p<0.001*** / p<0.001*** | p<0.001*** / p<0.001*** | p<0.001*** / p<0.001*** |\n| **SPICED vs EWC** | p=0.002** / p=0.003** | p=0.682 / p=0.735 | p=0.004** / p=0.002** |\n| **SPICED vs UCL_GV** | p=0.037* / p=0.021* | p<0.001*** / p<0.001*** | p<0.001*** / p<0.001*** |\n| **SPICED vs ReSNT** | p=0.008** / p=0.005** | p<0.001*** / p<0.001*** | p<0.001*** / p<0.001*** |\n| **SPICED vs BrainUICL** | p=0.019* / p=0.007** | p<0.001*** / p<0.001*** | p=0.412 / p=0.683 |\n\n---\n\n> #### **Q2: How does this work relate to existing methodologies, especially growing neural gas and self organizing maps?**\n\n**A2: We thank the reviewer for raising this profound and crucial question regarding the relationship between SPICED and classical models such as Growing Neural Gas (GNG) and Self-Organizing Maps (SOM).** We have carefully read the relevant literature on GNG and SOM and summarize our responses below:\n\nWhile SPICED shares similarities with these methods in that all involve dynamic, topology-preserving networks, our work is fundamentally distinct in **biological motivation, functional mechanism, and application objective**.\n\n1. **Biological Motivation**\n  \n  - While GNG and SOM are neurally inspired algorithms for topological mapping and vector quantization, they do not explicitly model specific neurobiological learning mechanisms.\n    \n  - In contrast, SPICED is **directly grounded in synaptic homeostasis**—a well-established biological principle involving the dynamic interplay between synaptic consolidation and renormalization during learning and sleep.\n    \n    Our framework explicitly emulates three key aspects of brain mechanism: **synaptic consolidation**, **synaptic renormalization**, and the **brain’s functional specificity**, integrating them into a principled and biologically plausible continual learning framework. By doing so, SPICED achieves:\n    \n    - Biologically plausible memory stabilization\n      \n    - Adaptive forgetting of non-essential information\n      \n    - Maintenance of network plasticity for continual learning\n      \n  \n  This **mechanistic translation of neurobiology into a computational framework** is a **core contribution** of our work. It represents a key advance over traditional self-organizing approaches, enabling more human-like stability-plasticity balance in artificial continual learning systems.\n  \n2. **Functional Mechanism**\n  \n  |     | GNG | SPICED |\n  | --- | --- | --- |\n  | **Biological Mechanism** | Neurally inspired, general topology learning | Explicitly models **synaptic homeostasis** |\n  | **Network Growth** | Error-driven expansion for **density approximation** | **Individual-specific node** integration for continual individual adaptation |\n  | **Connection Mechanism** | Based on proximity (e.g., nearest neighbor) | Based on **initial feature similarity** (weighted cosine similarity) |\n  | **Memory Replay** | None | **Importance-weighted replay** of task-discriminative memories |\n  | **Model Fusion** | N/A | **Weighted aggregation of historical models**, not sequential updates |\n  | **Stability Mechanism** | Edge aging / neighborhood decay | **Periodic synaptic renormalization** with time-dependent decay |\n  \n\n       Unlike traditional continual learning or GNG-style adaptation, SPICED **does not rely on the previous model state ​**. Instead, it **integrates information across the entire synaptic network**, making it robust to outlier individuals and catastrophic forgetting.\n\n3. **Application Objectives**\n  \n  - **Primary Goal:** GNG and SOM are general-purpose unsupervised learning tools for clustering and visualization. SPICED is designed for a specific and challenging BCI problem: unsupervised continual EEG decoding under inter-individual variability.\n  - **Key Challenge:** While GNG/SOM primarily address static data topology organization, SPICED is explicitly motivated by the dynamic stability-plasticity balance observed in biological neural systems, and thus targets two fundamental challenges in real-world continual EEG decoding: **(1)** robust adaptation to the sequential arrival of new individuals under significant domain shifts, and **(2)** mitigation of catastrophic forgetting during long-term learning. This design is directly inspired by synaptic homeostasis—the brain’s mechanism for integrating novel experiences while preserving consolidated memories.\n\n**Thanks once again for your insightful comment.** We have added a discussion comparing SPICED with GNG and SOM in the revised manuscript, clarifying their conceptual relationships and differences. We hope this addresses your concern.\n\n---\n\n> #### **Q3: From the paper, each individual’s data is partitioned into a pre-training set and then an incremental set (to use after pretraining), this means the framework has already seen data from all individuals. How was the individual data partitioned? Was data for individuals entirely left out for incremental learning?**\n\n**A3: We thank the reviewer for raising this question.** There might be a misunderstanding regarding our experimental setup. Our protocol is **subject-independent**, meaning that the individuals involved in the incremental phase never appear during pre-training. The incremental learning process is conducted in a completely **unsupervised manner**. For instance, in Table 2, the 10% split indicates that 10% of the subjects in the entire dataset are used as the source domain for pre-training the model, while the remaining 90% of subjects constitute the incremental stream for long-term continual learning. To prevent confusion, we have revised the description of the dataset split in Section 4.1 as follows.\n\n> \"Each dataset is partitioned in a **subject-independent manner**: a subset of subjects forms the pretraining set (i.e., source domain), used solely for pre-training the source model, while the remaining subjects constitute the incremental set (i.e., target domain), which is used to evaluate the performance of the SPICED framework in unsupervised individual continual learning (i.e., continual EEG decoding) scenario\"\n\n**We appreciate the reviewer pointing this out, and we hope this clarification adequately addresses your concern.**\n\n---\n\n> #### **Q4: Can you clarify how this relates to standard continual learning scenarios (class incremental, task incremental, etc)?**\n\n**A4: Thank you for this insightful question.** Our experimental paradigm is based on **Unsupervised Individual Continual Learning (UICL)**, which falls under the broader category of **domain-incremental learning** in continual learning. In this setting, the task (e.g., sleep staging, emotion recognition) remains fixed, while new individuals—each representing a distinct data domain—arrive sequentially. The core challenge arises from **significant inter-individual domain shifts**, rather than changes in label space (as in class-incremental) or task identity (as in task-incremental) scenarios.\n\nNotably, unlike traditional continual learning paradigms that rely on the incremental evolution of a single model (i.e., $M_i$ derived from $M_{i-1}$ ), SPICED adopts a fundamentally different strategy. When adapting to a new individual, SPICED does not depend on the model state from the previous timestep. Instead, it **integrates information across the entire synaptic network** by:\n\n- Selectively reactivating **task-discriminative memory traces** (i.e., critical memory replay), and\n- Performing **weighted model fusion** over top-K relevant historical models (i.e., critical model fusion).\n\nThis global integration mechanism enables SPICED to prioritize robust, task-relevant knowledge while suppressing interference from outlier or noisy domains. As a result, it achieves **robust adaptation** even in the presence of large domain shifts, and effectively **mitigates catastrophic forgetting** over long-term learning sequences.\n\n**We sincerely thank the reviewer for their careful reading and thoughtful feedback.** We hope our clarification contributes to a clearer and more complete understanding of our work."}]

Author rebuttal:

Meta-review: This paper presents SPICED, a synaptic homeostasis-inspired framework for unsupervised continual EEG decoding. The biological motivation is clear, and the work is well-written and organized, with thoughtful integration of consolidation, reactivation, and renormalization mechanisms. Evaluation across three EEG datasets shows consistent improvements, and the rebuttal provided additional clarity, including computational cost analysis and robustness tests. However, the improvements over prior baselines are modest and were originally reported only in bar plots without numerical detail, limiting interpretability. More importantly, compute overhead comparisons against prior methods remain incomplete, leaving questions on practical scalability. Overall, while the paper is not without weaknesses, the novelty, biological grounding, and clarified empirical support justify a poster acceptance.

**Authors are encouraged to provide explicit comparisons of computational overhead (e.g., runtime, memory, FLOPs) with prior methods, as this would significantly strengthen the empirical evaluation and highlight the practical scalability of the proposed framework.**
Decision: Accept (poster)