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Related Experiment Video

Updated: Oct 15, 2025

P300-Based Brain-Computer Interface Speller Performance Estimation with Classifier-Based Latency Estimation

P300-Based Brain-Computer Interface Speller Performance Estimation with Classifier-Based Latency Estimation

Published on: September 8, 2023

Tiny noise, big mistakes: adversarial perturbations induce errors in brain-computer interface spellers.

Xiao Zhang¹, Dongrui Wu¹, Lieyun Ding²

¹Ministry of Education Key Laboratory of Image Processing and Intelligent Control, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China.

National Science Review

|October 25, 2021

Summary

This summary is machine-generated.

Brain-computer interface (BCI) spellers using electroencephalogram (EEG) are vulnerable to subtle attacks. These security flaws can cause errors or misdiagnoses in communication and clinical applications.

Keywords:

BCI spellers adversarial examples brain-computer interfaces electroencephalogram

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Interaction between Phonological and Semantic Processes in Visual Word Recognition using Electrophysiology

Interaction between Phonological and Semantic Processes in Visual Word Recognition using Electrophysiology

Published on: June 29, 2021

Area of Science:

Neuroscience
Computer Science
Biomedical Engineering

Background:

Brain-computer interface (BCI) spellers enable text input via electroencephalogram (EEG) signals.
These systems are crucial for individuals with severe disabilities, offering a vital communication channel.
Existing research prioritizes speed and reliability, often overlooking BCI security.

Purpose of the Study:

To investigate the security vulnerabilities of EEG-based BCI spellers.
To demonstrate the susceptibility of P300 and steady-state visual evoked potential (SSVEP) BCI spellers to adversarial attacks.
To highlight the potential risks associated with unaddressed BCI security flaws.

Main Methods:

Examined P300 and SSVEP BCI speller paradigms.
Introduced imperceptible adversarial perturbations to EEG signals.
Assessed the impact of these perturbations on speller accuracy and output.

Main Results:

EEG-based BCI spellers are highly vulnerable to adversarial perturbations.
Tiny, unnoticed perturbations can significantly mislead spellers, causing incorrect character selection.
The study demonstrates successful manipulation of BCI speller output through these attacks.

Conclusions:

P300 and SSVEP BCI spellers possess critical security weaknesses.
Adversarial attacks pose a significant threat, potentially leading to user frustration or clinical misdiagnosis.
There is an urgent need to address the security of EEG-based BCIs.