Rethinking Generalized BCIs: Benchmarking 340,000+ Unique Algorithmic Configurations for EEG Mental Command Decoding

• URL: http://arxiv.org/abs/2512.02978v1
• Date: Tue, 02 Dec 2025 17:56:46 GMT
• Title: Rethinking Generalized BCIs: Benchmarking 340,000+ Unique Algorithmic Configurations for EEG Mental Command Decoding
• Authors: Paul Barbaste, Olivier Oullier, Xavier Vasques,
• Abstract summary: We present a benchmark evaluating over 340,000+ unique combinations of spatial and nonlinear EEG classification.<n>Our findings highlight that no universal 'one-size-fits-all' method can optimally decode EEG motor imagery patterns across all users or datasets.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Robust decoding and classification of brain patterns measured with electroencephalography (EEG) remains a major challenge for real-world (i.e. outside scientific lab and medical facilities) brain-computer interface (BCI) applications due to well documented inter- and intra-participant variability. Here, we present a large-scale benchmark evaluating over 340,000+ unique combinations of spatial and nonlinear EEG classification. Our methodological pipeline consists in combinations of Common Spatial Patterns (CSP), Riemannian geometry, functional connectivity, and fractal- or entropy-based features across three open-access EEG datasets. Unlike prior studies, our analysis operates at the per-participant level and across multiple frequency bands (8-15 Hz and 8-30 Hz), enabling direct assessment of both group-level performance and individual variability. Covariance tangent space projection (cov-tgsp) and CSP consistently achieved the highest average classification accuracies. However, their effectiveness was strongly dataset-dependent, and marked participant-level differences persisted, particularly in the most heterogeneous of the datasets. Importantly, nonlinear methods outperformed spatial approaches for specific individuals, underscoring the need for personalized pipeline selection. Our findings highlight that no universal 'one-size-fits-all' method can optimally decode EEG motor imagery patterns across all users or datasets. Future work will require adaptive, multimodal, and possibly novel approaches to fully address neurophysiological variability in practical BCI applications where the system can automatically adapt to what makes each user unique.

Related papers

• PRISM: Exploring Heterogeneous Pretrained EEG Foundation Model Transfer to Clinical Differential Diagnosis [5.616707402426108]
  We introduce PRISM, a masked autoencoder ablated along two axes -- pretraining population and downstream adaptation.<n>We compare a narrow-source EU/US corpus against a geographically diverse pool augmented with multi-center South Asian clinical recordings.<n> PRISM matches or outperforms REVE (92 datasets, 60,000+ hours) on the majority of tasks.
  arXiv  Detail & Related papers  (2026-02-28T19:50:28Z)
• MultiDiffNet: A Multi-Objective Diffusion Framework for Generalizable Brain Decoding [1.6528632644902828]
  We introduce textitMultiDiffNet, a diffusion-based framework that bypasses generative augmentation entirely by learning a compact latent space optimized for multiple objectives.<n>We decode directly from this space and achieve state-of-the-art generalization across various neural decoding tasks using subject and session disjoint evaluation.
  arXiv  Detail & Related papers  (2025-11-23T05:22:27Z)
• scMRDR: A scalable and flexible framework for unpaired single-cell multi-omics data integration [53.683726781791385]
  We introduce a scalable and flexible generative framework called single-cell Multi-omics Regularized Disentangled Representations (scMRDR) for unpaired multi-omics integration.<n>Our method achieves excellent performance on benchmark datasets in terms of batch correction, modality alignment, and biological signal preservation.
  arXiv  Detail & Related papers  (2025-10-28T21:28:39Z)
• A Novel Data Augmentation Strategy for Robust Deep Learning Classification of Biomedical Time-Series Data: Application to ECG and EEG Analysis [2.355460994057843]
  This study proposes a novel and unified deep learning framework that achieves state-of-the-art performance across different signal types.<n>Unlike prior work, we scientifically increase signal complexity to achieve future-reaching capabilities, which resulted in the best predictions.<n>The architecture requires 130 MB of memory and processes each sample in 10 ms, suggesting suitability for deployment on low-end or wearable devices.
  arXiv  Detail & Related papers  (2025-07-16T21:38:10Z)
• Private Training & Data Generation by Clustering Embeddings [74.00687214400021]
  Differential privacy (DP) provides a robust framework for protecting individual data.<n>We introduce a novel principled method for DP synthetic image embedding generation.<n> Empirically, a simple two-layer neural network trained on synthetically generated embeddings achieves state-of-the-art (SOTA) classification accuracy.
  arXiv  Detail & Related papers  (2025-06-20T00:17:14Z)
• mixEEG: Enhancing EEG Federated Learning for Cross-subject EEG Classification with Tailored mixup [5.367329958716485]
  Cross-subject electroencephalography (EEG) classification exhibits great challenges due to the diversity of cognitive processes and physiological structures between different subjects.<n>Privacy concerns associated with EEG pose significant limitations to data sharing between different hospitals and institutions.<n> Federated learning (FL) enables multiple decentralized clients to collaboratively train a global model without direct communication of raw data.
  arXiv  Detail & Related papers  (2025-04-07T06:24:23Z)
• Geodesic Optimization for Predictive Shift Adaptation on EEG data [53.58711912565724]
  Domain adaptation methods struggle when distribution shifts occur simultaneously in $X$ and $y$. This paper proposes a novel method termed Geodesic Optimization for Predictive Shift Adaptation (GOPSA) to address test-time multi-source DA. GOPSA has the potential to combine the advantages of mixed-effects modeling with machine learning for biomedical applications of EEG.
  arXiv  Detail & Related papers  (2024-07-04T12:15:42Z)
• Physics-informed and Unsupervised Riemannian Domain Adaptation for Machine Learning on Heterogeneous EEG Datasets [53.367212596352324]
  We propose an unsupervised approach leveraging EEG signal physics. We map EEG channels to fixed positions using field, source-free domain adaptation. Our method demonstrates robust performance in brain-computer interface (BCI) tasks and potential biomarker applications.
  arXiv  Detail & Related papers  (2024-03-07T16:17:33Z)
• The effect of data augmentation and 3D-CNN depth on Alzheimer's Disease detection [51.697248252191265]
  This work summarizes and strictly observes best practices regarding data handling, experimental design, and model evaluation. We focus on Alzheimer's Disease (AD) detection, which serves as a paradigmatic example of challenging problem in healthcare. Within this framework, we train predictive 15 models, considering three different data augmentation strategies and five distinct 3D CNN architectures.
  arXiv  Detail & Related papers  (2023-09-13T10:40:41Z)
• Convolutional Monge Mapping Normalization for learning on sleep data [63.22081662149488]
  We propose a new method called Convolutional Monge Mapping Normalization (CMMN) CMMN consists in filtering the signals in order to adapt their power spectrum density (PSD) to a Wasserstein barycenter estimated on training data. Numerical experiments on sleep EEG data show that CMMN leads to significant and consistent performance gains independent from the neural network architecture.
  arXiv  Detail & Related papers  (2023-05-30T08:24:01Z)
• Multiple Time Series Fusion Based on LSTM An Application to CAP A Phase Classification Using EEG [56.155331323304]
  Deep learning based electroencephalogram channels' feature level fusion is carried out in this work. Channel selection, fusion, and classification procedures were optimized by two optimization algorithms.
  arXiv  Detail & Related papers  (2021-12-18T14:17:49Z)
• Common Spatial Generative Adversarial Networks based EEG Data Augmentation for Cross-Subject Brain-Computer Interface [4.8276709243429]
  Cross-subject application of EEG-based brain-computer interface (BCI) has always been limited by large individual difference and complex characteristics that are difficult to perceive. We propose a cross-subject EEG classification framework with a generative adversarial networks (GANs) based method named common spatial GAN (CS-GAN) Our framework provides a promising way to deal with the cross-subject problem and promote the practical application of BCI.
  arXiv  Detail & Related papers  (2021-02-08T10:37:03Z)

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.