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Updated: May 14, 2026

Extracting Visual Evoked Potentials from EEG Data Recorded During fMRI-guided Transcranial Magnetic Stimulation

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Time-frequency feature extraction method for EEG signals utilizing fractional-order transient-extracting transform.

Sheng-Wei Fei¹, Yi-Bo Hu¹, Jia-le Chen¹

¹College of Mechanical Engineering, Donghua University, Lane 2999, Renmin North Road, Songjiang 201620 Shanghai, People's Republic of China.

Biomedical Physics & Engineering Express

|May 12, 2026

Summary

This summary is machine-generated.

This study introduces a new Fractional-order transient-extracting transform (FOTET) for improved electroencephalographic (EEG) signal analysis in motor imagery (MI) tasks. FOTET enhances feature extraction, boosting brain-computer interface (BCI) performance.

Keywords:

electroencephalogram (EEG) signals fractional-order transient-extracting transform motor imagery time–frequency analysis (TFA)

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Area of Science:

Neuroscience
Signal Processing
Biomedical Engineering

Background:

Extracting transient features from electroencephalographic (EEG) signals during motor imagery (MI) is challenging.
Accurate extraction of these features is crucial for effective brain-computer interface (BCI) performance.

Purpose of the Study:

To propose a novel Fractional-order transient-extracting transform (FOTET) for enhanced EEG signal feature extraction.
To improve the accuracy and efficiency of capturing transient neural activity during MI tasks.

Main Methods:

Introduced a fractional-order transient extracting operator and an iterative optimization process within FOTET.
Developed a method to balance time-frequency resolution by adjusting the fractional order parameter.
Utilized DenseNet-LSTM for classification of four-class MI tasks using extracted features.

Main Results:

FOTET effectively extracts transient features from noisy EEG signals, enabling better discrimination between different MI classes.
Achieved a classification accuracy of 96.71% with FOTET combined with DenseNet-LSTM.
Demonstrated superior performance compared to traditional time-frequency analysis (TFA) methods.

Conclusions:

FOTET significantly enhances the extraction of transient features in EEG signals for MI.
The proposed method offers a superior approach for EEG signal analysis in BCI applications.
FOTET's ability to handle noisy environments and improve classification accuracy validates its effectiveness.