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.

Online decoding of rat self-paced locomotion speed from EEG using recurrent neural networks
Feb 20, 2026Alejandro de Miguel, Nelson Totah, Uri MaozEEG Decoding Brain-Computer Interfaces
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
Sparse Bayesian Modeling of EEG Channel Interactions Improves P300 Brain-Computer Interface Performance
Feb 19, 2026Guoxuan Ma, Yuan Zhong, Moyan Li +2Brain-Computer Interfaces EEG Decoding
Department of Biostatistics, University of Michigan, Ann Arbor MI 48109
ASPEN: Spectral-Temporal Fusion for Cross-Subject Brain Decoding
Feb 18, 2026Megan Lee, Seung Ha Hwang, Inhyeok Choi +3Cross-Subject EEG Decoding Brain-Computer Interfaces
Carnegie Mellon University, Pittsburgh, PA, USA · Kyung Hee University, Yongin, South Korea · Korea Advanced Institute of Science and Technology, Deajeon, South Korea +1
Adaptive Semi-Supervised Training of P300 ERP-BCI Speller System with Minimum Calibration Effort
Feb 17, 2026Shumeng Chen, Jane E. Huggins, Tianwen MaBrain-Computer Interfaces EEG Decoding
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
NeuroSketch: A Practical Design Recipe for Neural Decoding
Dec 10, 2025Gaorui Zhang, Zhizhang Yuan, Jialan Yang +4Brain-Computer Interfaces Neural Decoding
College of Computer Science and Technology, Zhejiang University · Shanghai Institute of Microsystem and Information Technology
Geometry-Aware Deep Congruence Networks for Manifold Learning in Cross-Subject Motor Imagery
Nov 24, 2025Sanjeev Manivannan, Chandra Shekar LakshminarayanCross-Subject EEG Decoding MI Classification
Department of Biotechnology · Department of Data Science and Artificial Intelligence
Cortical-SSM: A Deep State Space Model for Motor Imagery Decoding from EEG Signals
Oct 17, 2025Shuntaro Suzuki, Shunya Nagashima, Komei SugiuraMI Classification Brain-Computer Interfaces
Keio University
EEG-based AI-BCI Wheelchair Advancement: Transformer-Based Learning with Motor Imagery for Brain Computer Interface
Sep 30, 2025Bipul Thapa, Biplov Paneru, Bishwash Paneru +1MI Classification Brain-Computer Interfaces
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
Cross-Subject and Cross-Montage EEG Transfer Learning via Individual Tangent Space Alignment and Spatial-Riemannian Feature Fusion
Aug 11, 2025Nicole Lai-Tan, Xiao Gu, Marios G. Philiastides +1Cross-Subject EEG Decoding Brain-Computer Interfaces
Decoding the Multimodal Mind: Generalizable Brain-to-Text Translation via Multimodal Alignment and Adaptive Routing
May 15, 2025Chunyu Ye, Yunhao Zhang, Jingyuan Sun +3Brain-Computer Interfaces Neural Decoding
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

Feb 20, 2026J/K move · Enter open · S save

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

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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\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

-

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

-

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.