Brain-Computer Interfaces

Also known as BCI

Brain-computer interfaces (BCIs) measure brain activity and decode it into actions or information, enabling communication and control when ordinary movement or speech is limited. Researchers use non-invasive EEG and MEG as well as implanted signals such as ECoG and intracortical recordings, applying methods from spatial filtering and Riemannian geometry to convolutional, Transformer, and state-space models. Common tasks include motor imagery, P300 and SSVEP selection, speech decoding, and continuous movement control; self-supervised pretraining and adaptation aim to reduce the data and calibration needed for new users or sessions. A central challenge is reliable real-world performance: neural signals vary across people and over time, and stronger offline accuracy does not always mean faster, safer, or more usable communication.

Latest papers

130 papers13 papers in the last four weeks, down 19% on the four weeks before. 0.1% of all new papers.

Feb 20, 2026

Online decoding of rat self-paced locomotion speed from EEG using recurrent neural networks

\textit{Objective.}

Accurate neural decoding of locomotion holds promise for advancing rehabilitation, prosthetic control, and understanding neural correlates of action. Recent studies have demonstrated decoding of locomotion kinematics across species on motorized treadmills. However, efforts to decode locomotion speed in more natural contexts

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where pace is self-selected rather than externally imposed

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are scarce, generally achieve only modest accuracy, and require intracranial implants. Here, we aim to decode self-paced locomotion speed non-invasively and continuously using cortex-wide EEG recordings from rats.

\textit{Approach.}

We introduce an asynchronous brain

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computer interface (BCI) that processes a stream of 32-electrode skull-surface EEG (0.01

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45 Hz) to decode instantaneous speed from a non-motorized treadmill during self-paced locomotion in head-fixed rats. Using recurrent neural networks and a dataset of over 133 h of recordings, we trained decoders to map ongoing EEG activity to treadmill speed.

\textit{Main results.}

Our decoding achieves a correlation of 0.88 (

R^2

= 0.78) for speed, primarily driven by visual cortex electrodes and low-frequency (

< 8

Hz) oscillations. Moreover, pre-training on a single session permitted decoding on other sessions from the same rat, suggesting uniform neural signatures that generalize across sessions but fail to transfer across animals. Finally, we found that cortical states not only carry information about current speed, but also about future and past dynamics, extending up to 1000 ms.

\textit{Significance.}

These findings demonstrate that self-paced locomotion speed can be decoded accurately and continuously from non-invasive, cortex-wide EEG. Our approach provides a framework for developing high-performing, non-invasive BCI systems and contributes to understanding distributed neural representations of action dynamics.

Alejandro de Miguel, Nelson Totah, Uri Maoz

Schmid College of Science and Technology, Chapman University, Orange, California, USA · LUCID-AI, Chapman University, Orange, California, USA · Helsinki Institute of Life Science, University of Helsinki, Finland +5

EEG Decoding Brain-Computer Interfaces

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Feb 19, 2026

Sparse Bayesian Modeling of EEG Channel Interactions Improves P300 Brain-Computer Interface Performance

Electroencephalography (EEG)-based P300 brain-computer interfaces (BCIs) enable communication without physical movement by detecting stimulus-evoked neural responses. Accurate and efficient decoding remains challenging due to high dimensionality, temporal dependence, and complex interactions across EEG channels. Existing approaches often treat channels independently or rely on black-box models, limiting interpretability and personalization. We propose a sparse Bayesian time-varying regression framework that explicitly models pairwise EEG channel interactions while performing temporal feature selection, where a relaxed-thresholded Gaussian process prior induces structured sparsity in both channel-specific and interaction effects, enabling interpretable identification of task-relevant channels and channel pairs. Applied to a public P300 speller dataset of 55 participants, our method achieves a 96.4% median character-level accuracy using all sequence repetitions and attains the best overall decoding performance among competing statistical and deep learning approaches. Incorporating channel interactions yields subgroup-specific gains in character-level accuracy, particularly among participants abstained from alcohol (median 7%, up to 14%). Importantly, our approach improves median BCI-Utility by more than 10% at its optimal operating point, achieving peak throughput after only six sequence repetitions. These results demonstrate that explicitly modeling structured EEG channel interactions within a principled Bayesian framework enhances accuracy and user-centric throughput, and supports personalization in P300 BCI systems.

Guoxuan Ma, Yuan Zhong, Moyan Li +2

Department of Biostatistics, University of Michigan, Ann Arbor MI 48109

Brain-Computer Interfaces EEG Decoding

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Feb 18, 2026

ASPEN: Spectral-Temporal Fusion for Cross-Subject Brain Decoding

Cross-subject generalization in EEG-based brain-computer interfaces (BCIs) remains challenging due to individual variability in neural signals. We investigate whether spectral representations offer more stable features for cross-subject transfer than temporal waveforms. Through correlation analyses across three EEG paradigms (SSVEP, P300, and Motor Imagery), we find that spectral features exhibit consistently higher cross-subject similarity than temporal signals. Motivated by this observation, we introduce ASPEN, a hybrid architecture that combines spectral and temporal feature streams via multiplicative fusion, requiring cross-modal agreement for features to propagate. Experiments across six benchmark datasets reveal that ASPEN is able to dynamically achieve the optimal spectral-temporal balance depending on the paradigm. ASPEN achieves the best unseen-subject accuracy on three of six datasets and competitive performance on others, demonstrating that multiplicative multimodal fusion enables effective cross-subject generalization.

Megan Lee, Seung Ha Hwang, Inhyeok Choi +3

Carnegie Mellon University, Pittsburgh, PA, USA · Kyung Hee University, Yongin, South Korea · Korea Advanced Institute of Science and Technology, Deajeon, South Korea +1

Cross-Subject EEG Decoding Brain-Computer Interfaces Feature Fusion Multimodal Fusion Frequency-Domain Feature Learning

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Feb 17, 2026

Adaptive Semi-Supervised Training of P300 ERP-BCI Speller System with Minimum Calibration Effort

A P300 ERP-based Brain-Computer Interface (BCI) speller is an assistive communication tool. It searches for the P300 event-related potential (ERP) elicited by target stimuli, distinguishing it from the neural responses to non-target stimuli embedded in electroencephalogram (EEG) signals. Conventional methods require a lengthy calibration procedure to construct the binary classifier, which reduced overall efficiency. Thus, we proposed a unified framework with minimum calibration effort such that, given a small amount of labeled calibration data, we employed an adaptive semi-supervised EM-GMM algorithm to update the binary classifier. We evaluated our method based on character-level prediction accuracy, information transfer rate (ITR), and BCI utility. We applied calibration on training data and reported results on testing data. Our results indicate that, out of 15 participants, 9 participants exceed the minimum character-level accuracy of 0.7 using either on our adaptive method or the benchmark, and 7 out of these 9 participants showed that our adaptive method performed better than the benchmark. The proposed semi-supervised learning framework provides a practical and efficient alternative to improve the overall spelling efficiency in the real-time BCI speller system, particularly in contexts with limited labeled data.

Shumeng Chen, Jane E. Huggins, Tianwen Ma

Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, United States · Department of Physical Medicine and Rehabilitation, Department of Biomedical Engineering, University of Michigan, Ann Arbor, United States

Brain-Computer Interfaces EEG Decoding Semi-Supervised Learning

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Dec 10, 2025

NeuroSketch: A Practical Design Recipe for Neural Decoding

Neural decoding is fundamental to brain-computer interfaces, with growing applications in healthcare. Previous research has focused on leveraging signal processing and deep learning methods to enhance neural decoding performance. However, systematic guidance on architectural design for neural decoding remains limited. In this study, we develop NeuroSketch, a practical design recipe for neural decoding, through a basic architecture study followed by macro- and micro-level optimization. Comparing nine basic architectures, we find that CNN-2D outperforms other architectures in neural decoding tasks and explore its effectiveness from temporal and spatial perspectives. Building on this backbone, we combine gradual feature-map expansion and early downsampling at the macro level with grouped convolutions at the micro level. These choices form the recipe, which we instantiate as NeuroSketch-Base (1.4M parameters) and NeuroSketch-Large (4.2M parameters). The recipe is developed and evaluated through nearly 5,000 experiments across eight tasks spanning visual, auditory, and speech modalities and EEG, SEEG, and ECoG signals. Against ten baselines, the two variants collectively achieve the best accuracy on each task. Our code is available at https://github.com/Galaxy-Dawn/NeuroSketch.

Gaorui Zhang, Zhizhang Yuan, Jialan Yang +4

College of Computer Science and Technology, Zhejiang University · Shanghai Institute of Microsystem and Information Technology

Brain-Computer Interfaces Neural Decoding EEG Decoding Convolutional Neural Networks

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Nov 24, 2025

Geometry-Aware Deep Congruence Networks for Manifold Learning in Cross-Subject Motor Imagery

Cross-subject motor imagery decoding remains a fundamental challenge in EEG-based brain-computer interfaces due to substantial inter-subject variability. Recent approaches have leveraged Riemannian geometry by representing EEG signals as covariance matrices on the symmetric positive definite (SPD) manifold. However, existing methods primarily focus on manifold-based representations while largely overlooking subject-specific variations in covariance dispersion and orientation. In this work, we address these challenges through geometry-aware congruence transformations and propose three complementary models: (i) Discriminative Congruence Transform (DCT), (ii) Deep Linear DCT (DLDCT), and (iii) Deep DCT-UNet (DDCT-UNet). The proposed models are evaluated both as manifold alignment modules for downstream classifiers and as end-to-end discriminative architectures optimized via cross-entropy with a custom logistic regression head. Experiments on challenging cross-subject motor imagery benchmarks demonstrate consistent improvements in transductive decoding performance, achieving 2-3% higher accuracy than strong baselines. These results highlight the effectiveness of geometry-aware congruence learning for mitigating inter-subject variability in EEG decoding.

Sanjeev Manivannan, Chandra Shekar Lakshminarayan

Department of Biotechnology · Department of Data Science and Artificial Intelligence

Cross-Subject EEG Decoding MI Classification Brain-Computer Interfaces Neural Decoding EEG Decoding Geometric Representation Learning+1

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Oct 17, 2025

Cortical-SSM: A Deep State Space Model for Motor Imagery Decoding from EEG Signals

Classification of electroencephalogram (EEG) signals obtained during motor imagery (MI) has substantial application potential, including communication assistance and rehabilitation support for patients with motor impairments. These signals remain inherently susceptible to physiological artifacts (e.g., eye blinking and swallowing), which pose persistent challenges. Although Transformer-based approaches for classifying EEG 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 signals across temporal, spatial, and frequency domains. We validated our method across two large-scale public MI EEG datasets containing more than 50 subjects. Our method outperformed baseline methods on both benchmarks. Furthermore, visual explanations derived from our model indicate that it effectively captures neurophysiologically relevant regions of EEG signals. These results indicate that Cortical-SSM provides a robust and interpretable alternative to attention-based architectures for MI EEG decoding. By enabling physiologically grounded feature learning, our method advances the reliability of subject-independent EEG classification and supports the development of practical and clinically deployable brain-computer interface systems.

Shuntaro Suzuki, Shunya Nagashima, Komei Sugiura

Keio University

MI Classification Brain-Computer Interfaces EEG Decoding State Space Models

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Sep 30, 2025

EEG-based AI-BCI Wheelchair Advancement: Transformer-Based Learning with Motor Imagery for Brain Computer Interface

This paper presents an Artificial Intelligence (AI) integrated approach to Brain-Computer Interface (BCI)-based wheelchair development, utilizing a motor imagery right-left-hand movement mechanism for control. The system is designed to simulate wheelchair navigation based on motor imagery right and left-hand movements using electroencephalogram (EEG) data. A pre-filtered dataset, obtained from an open-source EEG repository, was segmented into arrays of 19x200 to capture the onset of hand movements. The data was acquired at a sampling frequency of 200Hz. The system integrates a Tkinter-based interface for simulating wheelchair movements, offering users a functional and intuitive control system. We propose TFormerEEG, a Transformer-driven deep learning architecture, for motor imagery EEG classification. The model achieves a test accuracy of 93.04% compared with various machine learning baseline models, including XGBoost, EEGNet, and an EEG-Deformer model. The TFormerEEG achieved a mean accuracy of 91.18% through stratified cross-validation, showcasing the effectiveness of this model.

Bipul Thapa, Biplov Paneru, Bishwash Paneru +1

Department of Computer Science and Engineering, Kathmandu University, Kavre, Nepal · Department of Electronics and Communication Engineering, Nepal Engineering College, Pokhara University, Bhaktapur, Nepal · Department of Applied Sciences and Chemical Engineering, Institute of Engineering, Pulchowk Campus, Tribhuvan University, Lalitpur, Nepal

MI Classification Brain-Computer Interfaces EEG Decoding Time Series Transformers

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Aug 11, 2025

Cross-Subject and Cross-Montage EEG Transfer Learning via Individual Tangent Space Alignment and Spatial-Riemannian Feature Fusion

Personalised music-based interventions offer a powerful means of supporting motor rehabilitation by dynamically tailoring auditory stimuli to provide external timekeeping cues, modulate affective states, and stabilise gait patterns. Generalisable Brain-Computer Interfaces (BCIs) thus hold promise for adapting these interventions across individuals. However, inter-subject variability in EEG signals, further compounded by movement-induced artefacts and motor planning differences, hinders the generalisability of BCIs and results in lengthy calibration processes. We propose Individual Tangent Space Alignment (ITSA), a novel pre-alignment strategy incorporating subject-specific recentering, distribution matching, and supervised rotational alignment to enhance cross-subject generalisation. Our hybrid architecture fuses Regularised Common Spatial Patterns (RCSP) with Riemannian geometry in parallel and sequential configurations, improving class separability while maintaining the geometric structure of covariance matrices for robust statistical computation. Using leave-one-subject-out cross-validation, `ITSA' demonstrates significant performance improvements across subjects and conditions. The parallel fusion approach shows the greatest enhancement over its sequential counterpart, with robust performance maintained across varying data conditions and electrode configurations. The code will be made publicly available at the time of publication.

Nicole Lai-Tan, Xiao Gu, Marios G. Philiastides +1

Cross-Subject EEG Decoding Brain-Computer Interfaces EEG Decoding

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May 15, 2025

Decoding the Multimodal Mind: Generalizable Brain-to-Text Translation via Multimodal Alignment and Adaptive Routing

Decoding language from the human brain remains a grand challenge for Brain-Computer Interfaces (BCIs). Current approaches typically rely on unimodal brain representations, neglecting the brain's inherently multimodal processing. Inspired by the brain's associative mechanisms, where viewing an image can evoke related sounds and linguistic representations, we propose a unified framework that leverages Multimodal Large Language Models (MLLMs) to align brain signals with a shared semantic space encompassing text, images, and audio. A router module dynamically selects and fuses modality-specific brain features according to the characteristics of each stimulus. Experiments on various fMRI datasets with textual, visual, and auditory stimuli demonstrate state-of-the-art performance, achieving an 8.48% improvement on the most commonly used benchmark. We further extend our framework to EEG and MEG data, demonstrating flexibility and robustness across varying temporal and spatial resolutions. To our knowledge, this is the first unified BCI architecture capable of robustly decoding multimodal brain activity across diverse brain signals and stimulus types, offering a flexible solution for real-world applications.

Chunyu Ye, Yunhao Zhang, Jingyuan Sun +3

1State Key Laboratory of Multimodal Artificial Intelligence System, Institute of Automation, Chinese Academy of Sciences · School of Artificial Intelligence, University of Chinese Academy of Sciences · Department of Computer Science, The University of Manchester +1

Brain-Computer Interfaces Neural Decoding Cross-Modal Alignment Multimodal Fusion

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