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Related Experiment Video

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Assessment and Communication for People with Disorders of Consciousness

Assessment and Communication for People with Disorders of Consciousness

Published on: August 1, 2017

Integrating active brain-computer interfaces (aBCIs) with passive BCIs (pBCIs) under different frustration levels.

Xin Gao¹, Haipeng Lin², Xiaolong Wu¹

¹Bath Institute for the Augmented Human, University of Bath, Bath, UK.

Scientific Reports

|December 5, 2025

Summary

This study integrates passive brain-computer interfaces (pBCIs) to detect user frustration, improving active brain-computer interface (aBCI) performance by adapting motor imagery (MI) models. The novel approach enhances aBCI accuracy by recognizing and classifying frustration levels during tasks.

Keywords:

Active BCI Adaptive BCI Electroencephalography Frustration Passive BCI

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Last Updated: Jan 9, 2026

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An Experimental Platform to Study the Closed-loop Performance of Brain-machine Interfaces

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Published on: March 10, 2011

Area of Science:

Neuroscience and Biomedical Engineering
Human-Computer Interaction
Signal Processing

Background:

User mental states, particularly frustration, significantly impact active brain-computer interface (aBCI) performance.
Developing passive brain-computer interfaces (pBCIs) to monitor user emotions is crucial for adaptive BCIs.
Frustration is a key mental state affecting user engagement and performance in BCIs.

Purpose of the Study:

To develop a novel paradigm combining aBCIs and pBCIs to measure and adapt to user frustration levels.
To investigate the influence of frustration on motor imagery (MI) based aBCI performance.
To enhance aBCI accuracy by incorporating real-time frustration detection via pBCIs.

Main Methods:

A new experimental paradigm using visual feedback to induce varying frustration levels was designed.
Electroencephalography (EEG) data were collected for both pBCI (frustration detection) and aBCI (MI classification).
Filter bank common spatial pattern (FBCSP) and support vector machine (SVM) were used for feature extraction and classification of frustration levels and MI tasks.

Main Results:

The pBCI successfully classified three frustration levels (low, moderate, high) using FBCSP+SVM.
Two novel methods incorporating pBCI results improved aBCI mean classification accuracy by 7.40% and 8.62% compared to conventional methods.
The study demonstrated an integrated approach for objective frustration recognition and adaptive aBCI model calibration.

Conclusions:

Integrating pBCIs for frustration detection offers a viable method to enhance aBCI performance.
The developed methods provide a significant improvement in MI-based aBCI accuracy under varying user frustration.
This work represents an initial integrated demonstration of using pBCI to adapt non-invasive MI-based aBCIs, advancing EEG data analysis.