Gamma band activity associated with BCI performance: simultaneous MEG/EEG study

Kiwoong Kim,Jun H Hong,Minkyu Ahn,Hohyun Cho,Sung C Jun,Sangtae Ahn,Jin W Chang,Bong S Kim

doi:10.3389/fnhum.2013.00848

Abstract

While brain computer interface (BCI) can be employed with patients and healthy subjects, there are problems that must be resolved before BCI can be useful to the public. In the most popular motor imagery (MI) BCI system, a significant number of target users (called “BCI-Illiterates”) cannot modulate their neuronal signals sufficiently to use the BCI system. This causes performance variability among subjects and even among sessions within a subject. The mechanism of such BCI-Illiteracy and possible solutions still remain to be determined. Gamma oscillation is known to be involved in various fundamental brain functions, and may play a role in MI. In this study, we investigated the association of gamma activity with MI performance among subjects. Ten simultaneous MEG/EEG experiments were conducted; MI performance for each was estimated by EEG data, and the gamma activity associated with BCI performance was investigated with MEG data. Our results showed that gamma activity had a high positive correlation with MI performance in the prefrontal area. This trend was also found across sessions within one subject. In conclusion, gamma rhythms generated in the prefrontal area appear to play a critical role in BCI performance.

Highlights

Over the past several decades, considerable attention has been paid to the subject of brain computer interface (BCI) technology (Wolpaw et al, 2002), as it is an attractive notion that BCI can translate a user’s intention or mental state through brain waves
motor imagery (MI) PERFORMANCE Figure 2 presents classification accuracy estimated by the conventional cross-validation method described in Section“Classification Accuracy From EEG.”
As in previous studies (Ahn et al, 2012), each signal was bandpass-filtered with 8–30 Hz to include alpha and beta rhythms, as these bands are well known to contain very informative features that classify two different MI conditions (Pfurtscheller and Lopes da Silva, 1999; Ahn et al, 2012)

Summary

Introduction

Over the past several decades, considerable attention has been paid to the subject of brain computer interface (BCI) technology (Wolpaw et al, 2002), as it is an attractive notion that BCI can translate a user’s intention or mental state through brain waves. Motor imagery (MI)-based BCI has been one of the most popular designs, such as P300 and steady state visual evoked potential (SSVEP) BCIs (Bashashati et al, 2007; Guger et al, 2011). Recent studies have reported success in decoding the direction of movement (Mehring et al, 2004; Rickert et al, 2005; Waldert et al, 2008, 2009; Ball et al, 2009), target (Hammon et al, 2008; Ubeda et al, 2013), velocity (Bradberry et al, 2009; Lv et al, 2010; Ofner and Müller-Putz, 2012; Robinson et al, 2013), trajectory (Schalk et al, 2007), grasp type (Pistohl et al, 2012) and real-time detection of visuo-spatial working memory (Hamamé et al, 2012)

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Discussion

Conclusion