A Brain-Computer Interface Augmented Reality Framework with Auto-Adaptive SSVEP Recognition

TL;DR

This paper introduces an adaptive ensemble classification system for SSVEP-based BCI-AR applications, improving recognition accuracy and robustness to movement interference, enabling more practical and inclusive AR experiences.

Contribution

It presents a novel adaptive ensemble classifier for SSVEP recognition that handles inter-subject variability and movement interference in BCI-AR systems.

Findings

Achieved 80% accuracy on PC and 77% on HoloLens with 5-second stimulation.

Outperformed individual classifiers in robustness and accuracy.

Demonstrated effectiveness during head rotations and movement interference.

Abstract

Brain-Computer Interface (BCI) initially gained attention for developing applications that aid physically impaired individuals. Recently, the idea of integrating BCI with Augmented Reality (AR) emerged, which uses BCI not only to enhance the quality of life for individuals with disabilities but also to develop mainstream applications for healthy users. One commonly used BCI signal pattern is the Steady-state Visually-evoked Potential (SSVEP), which captures the brain's response to flickering visual stimuli. SSVEP-based BCI-AR applications enable users to express their needs/wants by simply looking at corresponding command options. However, individuals are different in brain signals and thus require per-subject SSVEP recognition. Moreover, muscle movements and eye blinks interfere with brain signals, and thus subjects are required to remain still during BCI experiments, which limits AR engagement. In this paper, we (1) propose a simple adaptive ensemble classification system that handles the inter-subject variability, (2) present a simple BCI-AR framework that supports the development of a wide range of SSVEP-based BCI-AR applications, and (3) evaluate the performance of our ensemble algorithm in an SSVEP-based BCI-AR application with head rotations which has demonstrated robustness to the movement interference. Our testing on multiple subjects achieved a mean accuracy of 80\% on a PC and 77\% using the HoloLens AR headset, both of which surpass previous studies that incorporate individual classifiers and head movements. In addition, our visual stimulation time is 5 seconds which is relatively short. The statistically significant results show that our ensemble classification approach outperforms individual classifiers in SSVEP-based BCIs.