Abstract

Improving the independent living ability of people who have suffered spinal cord injuries (SCIs) is essential for their quality of life. Brain-computer interfaces (BCIs) provide promising solutions for people with high-level SCIs. This paper proposes a novel and practical P300-based hybrid stimulus-on-device (SoD) BCI architecture for wireless networking applications. Instead of a stimulus-on-panel architecture (SoP), the proposed SoD architecture provides an intuitive control scheme. However, because P300 recognitions rely on the synchronization between stimuli and response potentials, the variation of latency between target stimuli and elicited P300 is a concern when applying a P300-based BCI to wireless applications. In addition, the subject-dependent variation of elicited P300 affects the performance of the BCI. Thus, an adaptive model that determines an appropriate interval for P300 feature extraction was proposed in this paper. Hence, this paper employed the artificial bee colony- (ABC-) based interval type-2 fuzzy logic system (IT2FLS) to deal with the variation of latency between target stimuli and elicited P300 so that the proposed P300-based SoD approach would be feasible. Furthermore, the target and nontarget stimuli were identified in terms of a support vector machine (SVM) classifier. Experimental results showed that, from five subjects, the performance of classification and information transfer rate were improved after calibrations (86.00% and 24.2 bits/ min before calibrations; 90.25% and 27.9 bits/ min after calibrations).

Highlights

  • According to World Health Organization (WHO) statistics, in 2013, there were between 250,000 and 500,000 people suffering from spinal cord injuries (SCIs) [1]

  • This work attempts to assess how the latency of the elicited P300 correlates with the phase lag of the evoked state visually evoked potential (SSVEP)

  • Because a 16 Hz flickering stimulus was adopted, the reference signal peaks at 15.625 ms and the phase lags were obtained according to (10), where tres is the latency of peak of the evoked SSVEP, and T is the period of flickering stimuli

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Summary

Introduction

According to World Health Organization (WHO) statistics, in 2013, there were between 250,000 and 500,000 people suffering from spinal cord injuries (SCIs) [1]. Due to difficulty with mobility, an estimated 20% to 30% of people with SCIs show clinically significant signs of depression. Improvement in the independent living ability of people suffering from SCIs is one method to overcome the disability’s barriers. BCIs provide subjects with a nonmuscular method to connect with the world. For disabled people who suffer from SCIs and strokes, BCIs improve their independence in daily life. Several studies have proven that there is a great potential to develop BCIs with wide applications, such as assistive spelling systems, robotics, and rehabilitation tools. BCI studies have reached a critical point and continue to seek innovative applications

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