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Brain-computer interface with rapid serial multimodal presentation using artificial facial images and voice.

¹Department of Electronic Systems Engineering, National Institute of Technology, Kagawa College, 551, Kohda, Takuma-cho, Mitoyo-shi, Kagawa, 769-1192, Japan; Center for Frontier Medical Engineering, Chiba University, 1-33 Yayoi-cho, Inage-ku, Chiba, Japan.

Computers in Biology and Medicine

|August 3, 2021

Summary

This study introduces a novel brain-computer interface (BCI) using rapid serial multimodal presentation (RSMP) with audiovisual stimuli, enhancing BCI performance. The developed RSMP BCI achieved high accuracy, paving the way for improved gaze-independent BCI systems.

Keywords:

Audiovisual BCI Multimodal P300 RSMP RSVP

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Area of Science:

Neuroscience
Computer Science
Biomedical Engineering

Background:

Brain-computer interfaces (BCIs) utilize electroencephalography (EEG) signals.
Multimodal stimuli, combining visual and auditory inputs, can enhance BCI performance.
The effect of multimodal stimuli in rapid serial visual presentation (RSVP) BCIs remains unexplored.

Purpose of the Study:

To propose and evaluate a rapid serial multimodal presentation (RSMP) BCI system.
To investigate the impact of audiovisual stimuli on RSMP BCI performance.
To identify key neural correlates, such as P300, contributing to classification accuracy.

Main Methods:

Development of an RSMP BCI incorporating artificial facial images and artificial voice stimuli.
Utilized scrambled images and masked sounds to isolate the effects of visual and auditory components.
Employed EEG signal analysis to assess BCI performance and identify contributing neural signals.

Main Results:

Audiovisual stimuli significantly improved the performance of the RSMP BCI.
The P300 component, specifically at the Pz electrode, was identified as a key contributor to classification accuracy.
The BCI system achieved a high online accuracy of 85.7 ± 11.5%.

Conclusions:

The integration of audiovisual stimuli enhances RSMP BCI performance.
The P300 signal plays a crucial role in the accuracy of this BCI paradigm.
Findings support the development of advanced, gaze-independent BCI systems.