An EEG-based real-time cortical rhythmic activity monitoring system

Abstract

In the present study, we introduce an electroencephalography (EEG)-based, real-time, cortical rhythmic activity monitoring system which can monitor spatiotemporal changes of cortical rhythmic activity on a subject's cortical surface, not on the subject's scalp surface, with a high temporal resolution. In the monitoring system, a frequency domain inverse operator is preliminarily constructed, considering the subject's anatomical information and sensor configurations, and then the spectral current power at each cortical vertex is calculated for the Fourier transforms of successive sections of continuous data, when a particular frequency band is given. A preliminary offline simulation study using four sets of artifact-free, eye-closed, resting EEG data acquired from two dementia patients and two normal subjects demonstrates that spatiotemporal changes of cortical rhythmic activity can be monitored at the cortical level with a maximal delay time of about 200 ms, when 18 channel EEG data are analyzed under a Pentium4 3.4 GHz environment. The first pilot system is applied to two human experiments-(1) cortical alpha rhythm changes induced by opening and closing eyes and (2) cortical mu rhythm changes originated from the arm movements-and demonstrated the feasibility of the developed system.

Neurofeedback-based motor imagery training for brain–computer interface (BCI)

“…To this end, we developed a kind of neurofeedback systems to train motor imagery by presenting participants with time-varying activation maps of their brain, using a real-time cortical rhythmic activity monitoring system that we recently introduced in a previous study (Im et al, 2007). The real-time cortical activity monitoring system could visualize spatiotemporal changes of cortical rhythmic activity of a specific frequency band on a subject's cortical surface, rather than the subject's scalp surface, with a high temporal resolution.…”

An EMG-based muscle force monitoring system

“…Moreover, the developed system can be used for on-line monitoring of muscle forces. Therefore, the developed system makes it possible to confirm that subjects are performing well according to the experimenter's instructions and to modify experimental protocols without stopping the on-going measurement [18].…”

An EEG-based real-time cortical functional connectivity imaging system

“…The proposed cortical functional connectivity imaging system is based on the EEG-based real-time cortical rhythmic activity monitoring system [22], hereafter referred to as a real-time dynamic neuroimaging system. The real-time dynamic neuroimaging system could visualize spatiotemporal changes in cortical rhythmic activity of a specific frequency band on a subject's cortical surface, rather than the subject's scalp surface, with a high temporal resolution.…”

“…The EEG-based real-time dynamic neuroimaging system [22] consisted of pre-processing and real-time processing parts. In the pre-processing part, a linear inverse operator was constructed in which the subject's anatomical information was reflected.…”

“…Then, the FD-MNE solver was executed, which loads the Fourier transformed signals B(f i ) Re and B(f i ) Im , i = 1, 2, …, n, where Re and Im represent the real and imaginary parts of the Fourier transformed signals, respectively, as well as the pre-saved inverse operator W. The real part q j (f i ) Re and imaginary part q j (f i ) Im of the current source vector at the jth cortical vertex with respect to the frequency of interest f i can then be evaluated by multiplying the corresponding rows (3j -2, 3j -1, and 3jth rows) in W with the Fourier transformed signals B(f i ) Re and B(f i ) Im . We used the FD-MNE method instead of time-domain MNE method to estimate the current source vectors because under the current computing environment maximally 20-30 source images could be calculated per every second due to the computational time required for the inverse process [22]. Then, the instantaneous source power changes for a frequency f i at the jth cortical vertex can be readily estimated by inverse Fourier transforming each directional component (x, y, z-directional components) of q j (f i ) Re and q j (f i ) Im into time-domain series (q x,j (t), q y,j (t), q z,j (t)) and calculating the power of the source vector Q j (t k ) as Q j (t k ) = q x,j (t k ) 2 ?…”