Cortical-SSM: A Deep State Space Model for EEG and ECoG Motor Imagery Decoding

• URL: http://arxiv.org/abs/2510.15371v1
• Date: Fri, 17 Oct 2025 07:13:55 GMT
• Title: Cortical-SSM: A Deep State Space Model for EEG and ECoG Motor Imagery Decoding
• Authors: Shuntaro Suzuki, Shunya Nagashima, Masayuki Hirata, Komei Sugiura,
• Abstract summary: We propose Cortical-SSM, a novel architecture that extends deep state space models to capture integrated dependencies of EEG and ECoG signals.<n>We validated our method across three benchmarks: 1) two large-scale public MI EEG datasets containing more than 50 subjects, and 2) a clinical MI ECoG dataset recorded from a patient with amyotrophic lateral sclerosis.
• Score: 2.8915595869638424
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Classification of electroencephalogram (EEG) and electrocorticogram (ECoG) signals obtained during motor imagery (MI) has substantial application potential, including for communication assistance and rehabilitation support for patients with motor impairments. These signals remain inherently susceptible to physiological artifacts (e.g., eye blinking, swallowing), which pose persistent challenges. Although Transformer-based approaches for classifying EEG and ECoG signals have been widely adopted, they often struggle to capture fine-grained dependencies within them. To overcome these limitations, we propose Cortical-SSM, a novel architecture that extends deep state space models to capture integrated dependencies of EEG and ECoG signals across temporal, spatial, and frequency domains. We validated our method across three benchmarks: 1) two large-scale public MI EEG datasets containing more than 50 subjects, and 2) a clinical MI ECoG dataset recorded from a patient with amyotrophic lateral sclerosis. Our method outperformed baseline methods on the three benchmarks. Furthermore, visual explanations derived from our model indicate that it effectively captures neurophysiologically relevant regions of both EEG and ECoG signals.

Related papers

• CG-DMER: Hybrid Contrastive-Generative Framework for Disentangled Multimodal ECG Representation Learning [29.83120239597889]
  We propose CG-DMER, a contrastive-generative framework for disentangled multimodal ECG representation learning.<n>A representation disentanglement and alignment strategy is designed to mitigate unnecessary noise and modality-specific biases.<n> Experiments on three public datasets demonstrate that CG-DMER achieves state-of-the-art performance across diverse downstream tasks.
  arXiv  Detail & Related papers  (2026-02-24T17:59:21Z)
• NeurIPT: Foundation Model for Neural Interfaces [13.659630322026095]
  NeurIPT is a foundation model developed for diverse EEG-based Neural Interfaces with a Pre-trained Transformer.<n>We introduce Amplitude-Aware Masked Pretraining (AAMP) to learn robust representations across varying signal intensities beyond local.<n>We also develop Intra-Inter Lobe Pooling (IILP) during fine-tuning to efficiently exploit regional brain features.
  arXiv  Detail & Related papers  (2025-10-18T15:45:00Z)
• EEG-MedRAG: Enhancing EEG-based Clinical Decision-Making via Hierarchical Hypergraph Retrieval-Augmented Generation [45.031633614714]
  EEG-MedRAG is a three-layer hypergraph-based retrieval-augmented generation framework.<n>It unifies EEG domain knowledge, individual patient cases, and a large-scale repository into a traversable n-ary relational hypergraph.<n>We introduce the first cross-disease, cross-role EEG clinical QA benchmark, spanning seven disorders and five authentic clinical perspectives.
  arXiv  Detail & Related papers  (2025-08-19T11:12:58Z)
• CodeBrain: Towards Decoupled Interpretability and Multi-Scale Architecture for EEG Foundation Model [52.466542039411515]
  EEG foundation models (EFMs) have emerged to address the scalability issues of task-specific models.<n>We present CodeBrain, a two-stage EFM designed to fill this gap.<n>In the first stage, we introduce the TFDual-Tokenizer, which decouples heterogeneous temporal and frequency EEG signals into discrete tokens.<n>In the second stage, we propose the multi-scale EEGSSM architecture, which combines structured global convolution with sliding window attention.
  arXiv  Detail & Related papers  (2025-06-10T17:20:39Z)
• BrainOmni: A Brain Foundation Model for Unified EEG and MEG Signals [46.121056431476156]
  This paper proposes Brain Omni, the first brain foundation model that generalises across heterogeneous EEG and MEG recordings.<n>Existing approaches typically rely on separate, modality- and dataset-specific models, which limits performance and cross-domain scalability.<n>A total of 1,997 hours of EEG and 656 hours of MEG data are curated and standardised from publicly available sources for pretraining.
  arXiv  Detail & Related papers  (2025-05-18T14:07:14Z)
• EEG2GAIT: A Hierarchical Graph Convolutional Network for EEG-based Gait Decoding [8.529597745689195]
  Decoding gait dynamics from EEG signals presents significant challenges due to the complex spatial dependencies of motor processes.<n>We propose EEG2GAIT, a novel hierarchical graph-based model that captures multi-level spatial embeddings of EEG channels.<n>We also contribute a new Gait-EEG dataset, consisting of synchronized EEG and lower-limb joint angle data collected from 50 participants over two lab visits.
  arXiv  Detail & Related papers  (2025-04-02T07:48:21Z)
• CognitionCapturer: Decoding Visual Stimuli From Human EEG Signal With Multimodal Information [61.1904164368732]
  We propose CognitionCapturer, a unified framework that fully leverages multimodal data to represent EEG signals.<n>Specifically, CognitionCapturer trains Modality Experts for each modality to extract cross-modal information from the EEG modality.<n>The framework does not require any fine-tuning of the generative models and can be extended to incorporate more modalities.
  arXiv  Detail & Related papers  (2024-12-13T16:27:54Z)
• Spatiotemporal Pooling on Appropriate Topological Maps Represented as Two-Dimensional Images for EEG Classification [0.0]
  Motor classification based on electroencephalography (EEG) signals is one of the most important brain-computer interface applications. This study proposes a novel EEG-based motor imagery classification method with three key features. Experimental results using the PhysioNet EEG Movement Motor/Imagery dataset showed that the proposed method achieved the best classification accuracy of 88.57%.
  arXiv  Detail & Related papers  (2024-03-07T09:35:49Z)
• A Knowledge-Driven Cross-view Contrastive Learning for EEG Representation [48.85731427874065]
  This paper proposes a knowledge-driven cross-view contrastive learning framework (KDC2) to extract effective representations from EEG with limited labels. The KDC2 method creates scalp and neural views of EEG signals, simulating the internal and external representation of brain activity. By modeling prior neural knowledge based on neural information consistency theory, the proposed method extracts invariant and complementary neural knowledge to generate combined representations.
  arXiv  Detail & Related papers  (2023-09-21T08:53:51Z)
• DGSD: Dynamical Graph Self-Distillation for EEG-Based Auditory Spatial Attention Detection [49.196182908826565]
  Auditory Attention Detection (AAD) aims to detect target speaker from brain signals in a multi-speaker environment. Current approaches primarily rely on traditional convolutional neural network designed for processing Euclidean data like images. This paper proposes a dynamical graph self-distillation (DGSD) approach for AAD, which does not require speech stimuli as input.
  arXiv  Detail & Related papers  (2023-09-07T13:43:46Z)
• EOG Artifact Removal from Single and Multi-channel EEG Recordings through the combination of Long Short-Term Memory Networks and Independent Component Analysis [0.0]
  We present a novel methodology that combines a long short-term memory (LSTM)-based neural network with ICA to address the challenge of EOG artifact removal from EEG signals. Our approach aims to accomplish two primary objectives: 1) estimate the horizontal and vertical EOG signals from the contaminated EEG data, and 2) employ ICA to eliminate the estimated EOG signals from the EEG.
  arXiv  Detail & Related papers  (2023-08-25T13:32:28Z)
• fMRI from EEG is only Deep Learning away: the use of interpretable DL to unravel EEG-fMRI relationships [68.8204255655161]
  We present an interpretable domain grounded solution to recover the activity of several subcortical regions from multichannel EEG data. We recover individual spatial and time-frequency patterns of scalp EEG predictive of the hemodynamic signal in the subcortical nuclei.
  arXiv  Detail & Related papers  (2022-10-23T15:11:37Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.