Induced Gamma-Band Activity during Actual and Imaginary Movements: EEG Analysis

Abstract

The purpose of this paper is to record and analyze induced gamma-band activity (GBA) (30–60 Hz) in cerebral motor areas during imaginary movement and to compare it quantitatively with activity recorded in the same areas during actual movement using a simplified electroencephalogram (EEG). Brain activity (basal activity, imaginary motor task and actual motor task) is obtained from 12 healthy volunteer subjects using an EEG (Cz channel). GBA is analyzed using the mean power spectral density (PSD) value. Event-related synchronization (ERS) is calculated from the PSD values of the basal GBA (GBAb), the GBA of the imaginary movement (GBAim) and the GBA of the actual movement (GBAac). The mean GBAim and GBAac values for the right and left hands are significantly higher than the GBAb value (p = 0.007). No significant difference is detected between mean GBA values during the imaginary and actual movement (p = 0.242). The mean ERS values for the imaginary movement (ERSimM (%) = 23.52) and for the actual movement (ERSacM = 27.47) do not present any significant difference (p = 0.117). We demonstrated that ERS could provide a useful way of indirectly checking the function of neuronal motor circuits activated by voluntary movement, both imaginary and actual. These results, as a proof of concept, could be applied to physiology studies, brain–computer interfaces, and diagnosis of cognitive or motor pathologies.

Reorganization of Resting-State EEG Functional Connectivity Patterns in Children with Cerebral Palsy Following a Motor Imagery Virtual-Reality Intervention

“…Since distinct EEG rhythms might relate to different cognitive and affective states [49], it is actually reasonable that FC patterns were not equal across all rhythms. In addition, it is worth observing that, even though MI is predominantly linked to rhythms within the α range (i.e., the µ band), this task could also induce the observed alterations for the β and γ bands; the latter, especially, is commonly overlooked in MI-EEG studies (notwithstanding, recent works have increasingly argued for the involvement of this band in this type of task-e.g., [50]). Moreover, as mentioned, functional links were more consistently found across subjects prior to the AO+MI-VR intervention.…”

Neural mechanism by which physical fatigue sensation suppresses physical performance: a magnetoencephalography study

“…The increased gamma band power is thought to indicate sensorimotor integration in the motor cortex, re ecting motor planning 35,36 . In addition, these increases in gamma band power in the M1 and SMA are observed not only during actual motor execution, but also during motor imagery 37 . Taking such ndings into consideration, the correlation between gamma band activity in the precentral gyrus and the level of fatigue sensation observed in our study may suggest enhanced adjustment of motor planning in the precentral brain regions caused by the induction of a fatigue sensation to properly suppress motor output and thus maintain homeostasis.…”

Evaluation of temporal, spatial and spectral filtering in CSP-based methods for decoding pedaling-based motor tasks using EEG signals

“…Although the above metrics demonstrate good performance in classification approaches, it should be noted that AM classification can be more challenging, due to the presence of motion that can alter EEG signals [16,20]. Additionally, it has been shown that motionrelated therapies can generate greater motivation in HMI users than those based on imagination [10,13].…”

“…This could be a barrier between the user and the system, which could cause demotivation and low Human Machine Interaction (HMI) [10,11]. Therefore, several studies have found that tasks that are harder to manipulate are also more difficult to manipulate mentally, which highlights the influence of realworld motor experiences on mental imagery [12,13].…”

“…Hosseini et al used a strategy to detect current 2D hand motion to give visual feedback to the patient [15]. Amo et al focused on studying and comparing different frequency bands during execution and imagination of wrist motion [13]. Thompson et al mentioned the importance of EEG in understanding cortical rhythms in performance-enhancing sports domains [16].…”