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

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.

Heading for new shores! Overcoming pitfalls in BCI design

“…A neurophysiological predictor within the sensorimotor rhythm (SMR) based on recordings of two-minutes of resting state data was found to be significantly (r=.53) correlated with SMR BCI performance [26]. Also gamma activity during a one-minute resting state period showed significantly correlations with later motor imagery performance [27]. The resting state EEG network (two minutes assessment) can be a predictor for successful use of a SSVEP based BCI [28].…”

Interbrain phase synchronization during turn‐taking verbal interaction—a hyperscanning study using simultaneous EEG/MEG

“…Further, MEG gamma band activity may play a critical role in social interaction [Pavlova et al, ], speech processing [Palva et al, ], and working memory [Jensen et al, ; Jokisch and Jensen, ]. Simultaneous recording of EEG and MEG—multimodal analysis—may yield complementary information and MEG gamma band activity may provide significant findings that are not revealed using EEG [Ahn et al, ].…”

Adaptive Assistance for Brain-Computer Interfaces by Online Prediction of Command Reliability

“…Most of these studies focus on inter-subject variability from a physiological [2][3][4][5][6], anatomical [7,8], or psychological [9,10] perspectives. Although precise distinction between user-related and system-related causes of performance variations may not be simple [11], these studies provide a better understanding of these causes.…”

“…However, most of these studies are limited to the analysis of a single session using offline recordings (where no feedback was provided to the subject). For instance, it is suggested that classification certainty for each subject is positively correlated with the power of EEG oscillations in the gamma band (55 − 85 Hz) [3,6]. Similarly, trial-by-trial classification performance was found to correlate with high-frequency gamma oscillations (70 − 80 Hz) prior to the beginning of each trial [15], while others found correlations with a weighted combination of the theta (3 − 8 Hz), alpha (8 − 13 Hz), and beta (16 − 24 Hz) oscillations in frontal, pariatel and central areas of the brain, respectively [16].…”