Effect of the Green/Blue Flicker Matrix for P300-Based Brain–Computer Interface: An EEG–fMRI Study

Shiro Ikegami,Naokatsu Saeki,Kouji Takano,Makoto Wada,Kenji Kansaku

doi:10.3389/fneur.2012.00113

Abstract

The visual P300-brain–computer interface, a popular system for EEG-based BCI, utilizes the P300 event-related potential to select an icon arranged in a flicker matrix. In the conventional P300-BCI speller paradigm, white/gray luminance intensification of each row/column in the matrix is used. In an earlier study, we applied green/blue luminance and chromatic change in the P300-BCI system and reported that this luminance and chromatic flicker matrix was associated with better performance and greater subject comfort compared with the conventional white/gray luminance flicker matrix. In this study, we used simultaneous EEG-functional magnetic resonance imaging (fMRI) recordings to identify brain areas that were more enhanced in the green/blue flicker matrix than in the white/gray flicker matrix, as these may highlight areas devoted to improved P300-BCI performance. The peak of the positive wave in the EEG data was detected under both conditions, and the peak amplitudes were larger at the parietal and occipital electrodes, particularly in the late components, under the green/blue condition than under the white/gray condition. fMRI data showed activation in the bilateral parietal and occipital cortices, and these areas, particularly those in the right hemisphere, were more activated under the green/blue condition than under the white/gray condition. The parietal and occipital regions more involved in the green/blue condition were part of the areas devoted to conventional P300s. These results suggest that the green/blue flicker matrix was useful for enhancing the so-called P300 responses.

Highlights

The brain–computer interface (BCI) or brain–machine interface (BMI) is an interface technology that utilizes neurophysiological signals from the brain to control external machines or computers, and has become widespread in this decade due to technical and mechanical improvements (Wolpaw et al, 2002; Birbaumer and Cohen, 2007; Kansaku, 2011)
We used simultaneous EEG-functional magnetic resonance imaging recordings to identify brain areas that were more enhanced in the green/blue flicker matrix than in the white/gray flicker matrix, as these may highlight areas devoted to improved P300-BCI performance
We used simultaneous EEG and functional magnetic resonance imaging (fMRI) recordings to investigate brain areas that were enhanced in the green/blue flicker matrix compared with the white/gray flicker matrix, as these may highlight areas devoted to improved P300-BCI performance

Summary

Introduction

The brain–computer interface (BCI) or brain–machine interface (BMI) is an interface technology that utilizes neurophysiological signals from the brain to control external machines or computers, and has become widespread in this decade due to technical and mechanical improvements (Wolpaw et al, 2002; Birbaumer and Cohen, 2007; Kansaku, 2011). One research approach to BCIs relies on electrical signals recorded from the cortical surface [electrocorticograph (ECoG)] or directly from the neuron (unit recording); this approach can be categorized as invasive BCI because it requires neurosurgery (Kennedy et al, 2000; Hochberg et al, 2006; Yanagisawa et al, 2009; Brunner et al, 2011; Krusienski and Shih, 2011). Another approach utilizes neurophysiological signals from the brain that are accessed without surgery, which is called noninvasive BCI. Several P300-BCI systems were developed and tested on individuals with amyotrophic lateral sclerosis, cervical spinal cord injury, and other disorders (Piccione et al, 2006; Sellers and Donchin, 2006; Hoffmann et al, 2008; Nijboer et al, 2008; Ikegami et al, 2011)

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