STFEEG-Tool: A Spatial-Temporal-Frequency EEG Analysis Tool for Motor Imagery Brain-Computer Interfaces

Summary

This study presents a standardized and reproducible protocol for implementing the Spatial-Temporal-Frequency EEG analysis tool (STFEEG) for motor imagery EEG decoding, incorporating configurable spatial-temporal-frequency segmentation, Common Spatial Patterns (CSP)-based feature extraction, multiple classification algorithms, and visualization capabilities.

Abstract

Motor imagery-brain-computer interfaces (MI-BCIs) have demonstrated significant potential for neurorehabilitation and cognitive neuroscience. However, a standardized and reproducible MI-EEG workflow for configurable spatial-temporal-frequency feature analysis remains limited, and many pipelines require complex configuration and parameter tuning with limited interpretability, hindering practical deployment and generalization. To address these challenges, an STFEEG-Tool was developed to provide a user-friendly, standardized, and interpretable workflow for EEG decoding in MI paradigms. STFEEG-Tool enables fine-grained configuration of temporal, frequency-band, and spatial segmentation, allowing the extraction of multiscale MI features. The toolbox integrates multiple feature extraction algorithms, including common spatial patterns (CSP) and divergence-based CSP (div-CSP), along with various classifiers, such as support vector machines (SVMs), Ridge Regression Classifier, and Lasso Classifier. A dynamic time-frequency scalp topographical map is provided to summarize spatial patterns across time-frequency segments and support interpretation of decoding results. Overall, STFEEG-Tool serves as a reproducible and extensible platform for fine-grained MI-EEG analysis, facilitating the translation of fine-grained decoding pipelines into practical, user-oriented applications.

Introduction

Motor imagery brain-computer interfaces (MI-BCIs) enable direct communication between the human brain and external devices without requiring peripheral muscle activity¹. In recent years, MI-BCI technology has been widely applied in neurorehabilitation², assistive control³, and human-computer interaction⁴. These applications highlight the need for accessible and reproducible analysis workflows that can be reliably adopted by researchers and clinicians, including users with limited programming expertise.

Conventional MI-EEG decoding approaches are ....

Protocol

The data used in this protocol were obtained from publicly available, de-identified datasets. The original data collection procedures were conducted in accordance with the ethical standards of the respective institutions; therefore, no additional institutional approval was required for this secondary analysis.

Figure 1 illustrates the overall workflow of the Spatial-Temporal-Frequency Electroencephalography Analysis Tool. Follow the steps below to configure each module and reproduce the fine-grained spatial-frequency-time decoding protocol. The software used in this study is listed in the Table of Mater....

Results

The STFEEG-Tool was validated on both the BCI Competition IV 2a²⁶ and the BCI Competition III 3a²⁷, and the system successfully executed the complete decoding pipeline in both cases. The fixed cross-session protocol of BCI Competition IV 2a was followed: training on Session 1 and evaluation on the held-out Session 2, recorded on two separate days. Under the same configuration used in the previously reported fine-grained spatial-temporal-frequency framework^.......

Discussion

STFEEG-Tool is designed as an MI-EEG-specific toolbox that complements established EEG ecosystems such as EEGLAB²⁰, BCILAB²¹, OpenViBE²², PyNoetic²³. While these platforms provide flexible components for MI decoding, fine-grained spatial-temporal-frequency configuration and model-informed interpretation may involve additional integration across processing steps. This is particularly relevant when explicit electrode-group decom.......

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
MATLAB	MathWorks	Version R2024a	Software environment for numerical computing, signal processing, and GUI development.
Signal Processing Toolbox	MathWorks	https://ww2.mathworks.cn/en/products/signal.html	Toolbox for digital filtering and time-frequency analysis
Statistics and Machine Learning Toolbox	MathWorks	https://ww2.mathworks.cn/en/products/statistics.html	Toolbox for model training and statistical analysis