Motor imagery and mental fatigue: inter-relationship and EEG based estimation

Spatial constraints and cognitive fatigue affect motor imagery of walking in people with multiple sclerosis

“…MS related fatigue had such a negative impact on the quality of life, daily activities, psychological well-being and relationships with friends. Furthermore, although cognitive fatigue could be related to other different cognitive states as drowsiness, loss of attention, decreased arousal, lower focus level 75 , our results are in line with other studies that confirmed a significant relationship between cognitive fatigue and MI performance 48 , 76 , 77 . A fatigued body after either continuous or intermittent exercises may affect MI ability, since it reproduces both forward and inverse prediction models, that are crucial to generate temporally accurate and vivid motor representations.…”

“…mood, cognitive functioning, disability) 47 . Interestingly, given the significant cognitive effort required to concentrate on MI tasks, loss of attention and declined arousal level due to cognitive fatigue have been found to significantly alter neural signals in the Brain Computer Interface (BCI) system 48 . As indicated by Deluca et al 49 , abnormal cerebral activation in the basal ganglia and frontal lobes (that are also recruited in the MI task) may represent the extra “effort” (i.e., allocation of more neural resources) required to maintain the same level of performance, supporting the notion that increased cerebral activation may reflect the additional effort (i.e., cognitive fatigue) to adequately perform behavioral tasks in PwMS.…”

Adaptive feature extraction in EEG-based motor imagery BCI: tracking mental fatigue

“…Myrden et al [2] have shown the effect of mental states on BCI performance. Recent work [16] has reported the inter-relationship between MI and mental fatigue, i.e., a prolonged session of MI induces mental fatigue which in turn decreases the separability of MI EEG features. In such cases, there is an ample necessity for continuously monitoring the change in the user's cognitive state to decide when to initiate adaptation in a proper manner.…”

“…The growth of mental fatigue was tracked using the Kernel Partial Least Square method [18]. Talukdar et al [16] have presented a detailed formulation of fatigue analysis. The covariance matrices of CSPs are updated using adaptive weights.…”

Emerging techniques in statistical analysis of neural data

“…For example, state-space models are used to understand how latent variables (states) influence neural and behavioral measurements or to simply explain how and why control systems in the central nervous system operate the way they do. This special issue includes pioneering studies that describe methods to sift through large amounts of data to identify brain regions and frequency bands of interest (Breault et al 2018); to construct models from multi-scale neural data ranging from spike trains from individual neurons (Chen et al 2018 to EEG recordings from populations of neurons (Talukdar et al 2018); and to decode behavior from neural data (Han et al 2018), with applications to neuroprosthetics and brain-machine interfaces. Network connectivity studies in the contexts of brain state changes (Luckett et al 2018, Xiao et al 2018 and language are also presented (Grappe et al 2018).…”